2026-06-04T06:59:45Z https://www.fdr.uni-hamburg.de/oai2d

oai:fdr.uni-hamburg.de:9563 2023-01-25T09:34:28Z user-cliccs user-icdc user-cen user-uhh

Weidinger, Johannes Gerlitz, Lars Bobrowski, Maria Böhner, Jürgen Chaudhary, Ram Prasad Schickhoff , Udo Schwab, Niels Scholten, Thomas 2021-08-02 This data set includes meteorological and pedo-climatic data obtained from a study area in central Himalayas, Nepal. The study area is located in the Rolwaling valley along the Rolwaling Himal in the Gaurishankar Conservation Area (GCA) in the Dolakha district. In this region the upper treeline ecotone is located in a subtropical high-altitude alpine forest zone with strong differences in land cover on opposing valley slopes. South-facing slopes are traditionally highly shaped by anthropogenic practice and dominant tree species forming the treeline are Juniperus spp. On north-facing slopes dense forests are consisting primarily of Betula utilis and Abies spectabilis with Rhododendron campanulatum, Sorbus microphylla, Acer caudatum and Prunus rufa communities. On the upper end of the main study site the forests are locked with a wide Rhododendron campanulatum krummholz belt which is followed by Rhododendron spp. dwarf shrubs and alpine tundra in higher areas. Within the east-west extending valley eight automatic weather stations (AWS) were installed since April 2013. These were mounted on different slopes and positions stretching from around 3700m up to over 5000m a.s.l. In 2m above surface (sensor type in brackets) incoming solar radiation [Wm-²] (ONS-S-LIB-M003), air temperature [°C] (ONS-S-THB-M002) and relative humidity [%] (ONS-S-THB-M002), wind speed [ms-1] (ONS-S-WSB-M003) and wind direction [°] (ONS-S-WDA-M003) as well as precipitation [mm] (ONS-S-RGB-M002) are measured every 3min and logged every 15min. Four AWS are positioned in the main study site in the valley representing two altitudinal transects along different slope expositions in north-western and north-eastern directions. Additionally 34 pedo-climatic Koubachi combined soil temperature [°C] and soil moisture [pF] loggers were installed from April 2013 until September 2015 in four different altitudinal belts along the transects in 10cm depth. These also follow the aforementioned pattern with different expositions and both parameters were logged in hourly intervals. Data quality: Winter precipitation is underestimated due to non-heated rain gauges. During the project start several vandalism incidents resulted in data loss and gaps in the time series. For hydrological modelling temperature and precipitation data sets are available in a gap filled version. Atmospheric sensors of AWS: 2m above surface: incoming solar radiation [Wm-²]; air temperature [°C]; relative humidity [%]; wind speed [ms-1]; wind direction [°]; precipitation [mm]; measuring interval: 3min; logging interval: 15min. Locations of automated weather stations (ONSET) Station; Longitude [°E]; Latitude [°N]; Altitude [m.a.s.l.]; Instal. Date; NW bottom; 86.3762; 27.9009;3718.9; 2013-04-18; NW top; 86.3742; 27.8967; 4035.9; 2013-04-15; NE bottom; 86.3791; 27.8986; 3734.2; 2013-04-18; NE bottom; 86.3791; 27.8986; 3734.2; 2013-04-18; NE top; 86.3759; 27.8934; 4158.3; 2013-10-17; Beding Gompa; 86.3755; 27.9050; 3886.0; 2013-04-19; Na; 86.4337; 27.8782; 4192.1; 2013-04-21; Dudgunda; 86.4604; 27.8756; 4532.2; 2016-09-29; Yalun; 86.4338; 27.8590; 5005.2; 2013-10-20; Pedo-climatic sensors of Koubachis: 10cm sensor soil depth; soil temperature [°C]; soil moisture [pF]; measuring and logging interval: 60min. Locations of pedo-climatic loggers (Koubachi AG) Belt / Logger groups; Altitudinal range [m a.s.l]; Altitudinal zone; Number of functional loggers (in 06/2015) NE-2 A; 3750 - 3900 m; closed forest; 4 (100%); NE-2 B; 3900 - 4000 m; uppermost closed forest; 2 (50%); NE-2 C; 4000 - 4100 m; krummholz belt; 4 (100%); NE-2 D; 4100 - 4250 m; dwarf scrub heath / alpine tundra; 3 (75%); NW-1 A; 3750 - 3800 m; closed forest; 3 (75%); NW-1 B; 3800 - 3900 m; uppermost closed forest; 3 (75%); NW-1 C; 3900 - 4050 m; krummholz belt; 3 (75%); NW-1 D; 4100 - 4250 m; dwarf scrub heath / alpine tundra; 3 (75%); NE-2 sp; 4000 - 4050 m; Abies spectabilis / Betula utilis individuals; 2 (100%); All data is stored as .csv files in UTF-8 encoding. https://www.fdr.uni-hamburg.de/record/9563 10.25592/uhhfdm.9563 oai:fdr.uni-hamburg.de:9563 eng doi:10.3354/cr01518 doi:10.25592/uhhfdm.9562 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode TREELINE Project soil and atmosphere wind speed wind direction air temperature relative humidity precipitation radiation data automatic weather stations soil moisture soil temperature treeline meteorological observations monsoon Rolwaling Himal Himalayas Nepal Gaurishankar Conservation Area remote sensing TREELINE - Longterm atmospheric and pedo-climatic observations along an upper treeline ecotone in the Himalayas, Nepal info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10467 2023-08-07T13:41:45Z user-cliccs user-icdc user-cen

Sadikni, Remon Jahnke-Bornemann, Annika Kern, Stefan 2022-11-09 Abstract: The soil moisture time series data of the EUMETSAT H-SAF product H119 and its extension H120 based on MetOp-A, -B, and -C ASCAT data, processing version v7 (https://doi.org/10.15770/EUM_SAF_H_0009), are converted into geographic maps (cartesian grid) of daily running 5-day average/composite soil moisture (SM) distribution separately for ascending and descending overpasses. Two different 5-day SM distributions are given: one is based solely on nominally computed SM, the other one includes also those SM values which were negative (down to -25%, correction flag = 1) or positive (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. All data are interpolated into a cartesian grid of x- and y-dimensions of original grid. For more information see the respective global attribute in the netCDF file. TableOfContents: soil moisture; soil moisture noise; soil moisture extended; soil moisture extended noise; soil moisture status flag; number of overpasses per grid cell; historic probability of snow cover; historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD Technical Info: dimensons: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2007-01-01; temporalExtent_endDate: 2021-12-31; temporalResolution: daily; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.1125; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-A, MetOp-B, MetOp-C); instrumentProvider: EUMETSAT, ESA; License: The following applies to the original product: All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products. Methods: For a description of the methods used to obtain the 5-day average / composite data we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi:10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.2, 2022; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.1, 2021; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v1.1, 2022. Units: units for all variables (see TableOfContents): percent, percent, percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: -90.0 degrees North; northBoundLongitude: 90.0 degrees North; geoLocationPlace: global over land Size: 730 (leap year: 732) files per year [note: there are 2 files per day, one for the ascending, one for the descending overpasses]; ~61.569 MegaByte per file; ~43.892 GigaByte per year; ~658.860 GigaByte in total (provided as two zip-files per year) Format: netCDF DataSources: Original Data as time series on a 12.5 km DGG Grid: https://hsaf.meteoam.it/Products/Detail?prod=H119 and https://hsaf.meteoam.it/Products/Detail?prod=H120 (last access: 2022-011-07); this original product comes with the following notion: "All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products." See also: http://hsaf.meteoam.it; https://hsaf.meteoam.it/Products/Detail?prod=H119, and https://hsaf.meteoam.it/Products/Detail?prod=H120 Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/10467 10.25592/uhhfdm.10467 oai:fdr.uni-hamburg.de:10467 eng info:eu-repo/semantics/altIdentifier/doi/10.25592/uhhfdm.8680 doi:10.15770/EUM_SAF_H_0009 url:http://hsaf.meteoam.it url:https://www.cen.uni-hamburg.de/en/icdc/land/ascat-soilmoisture.html doi:10.25592/uhhfdm.10195 doi:10.25592/uhhfdm.13101 doi:10.25592/uhhfdm.8680 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Daily Satellite Remote Sensing ASCAT MetOp-A/B/C EUMETSAT HSAF University of Vienna ICDC ASCAT Global Maps of daily running 5-day mean surface soil moisture info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10212 2024-02-27T09:04:47Z user-icdc user-uhh

Turner, Dave Rochette, Luc 2022-05-18 This dataset contains thermodynamic profiles retrieved from the ground-based infrared spectrometer called the ASSIST. The ASSIST was located at the "MOLRAO" location at the DWD's Lindenberg site. The primary retrieved variables are T(z), q(z), liquid water path, and effective radius. The uncertainties of these variables are provided, as well as a full error covariance matrix. Most of the information from the ASSIST observation is in the lowest 2 km; the "cumulative degrees of freedom" fields (cdfs_temperature and cdfs_watervapor) provide a quantitative description of this information with height. The data structure follows the standard DOE Atmospheric Radiation Measurement (ARM) program's data format. To get the time for the samples, add "base_time" to "time_offset" to get the number of seconds since 1 Jan 1970 at 00:00:00 UTC. Quality: There are uncertainty profiles for all of the retrieved variables in the netCDF files. However, the information content in the ASSIST observations decreases rapidly with height; profile quantifies above 3 km should be used with care. Also, if the retrieved LWP > 10 g/m2, then there will be very little-to-no information on the temperature and humidity profile above the cloud base height. I also recommend only using retrievals wherein the RMSr field is less than 8 and gamma field is less than 5. All of these variables are in the netCDF file. Parameters: temperature, waterVapor, LWP, ReffL Funding: LRtech provided the instrument to FESSTVaL free of charge. The retrievals were performed under the auspices of the NOAA Atmospheric Science for Renewable Energy program https://www.fdr.uni-hamburg.de/record/10212 10.25592/uhhfdm.10212 oai:fdr.uni-hamburg.de:10212 eng doi:10.25592/uhhfdm.10211 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Infrared spectrometer ASSIST thermodynamic profiles optimal estimation FESSTVaL SAMD measurement Thermodynamic retrieved profiles from the ASSIST infrared spectrometer, FESSTVaL campaign info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:11980 2024-04-18T15:38:44Z user-icdc user-cen user-uhh

Kern, Stefan Sadikni, Remon Päschke, Eileen Becker, Claudia Beyrich, Frank 2023-04-25 Abstract: This data set contains time series of the regional-scale sensible and latent heat fluxes derived from measurements with an optical-microwave scintillometer over a path length of 4.85 km between the Falkenberg boundary layer field site (GM Falkenberg) and the Lindenberg observatory site during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) from May 18 to August 31, 2021. The Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory and the GM Falkenberg supersites are operated by the German national meteorological service (Deutscher Wetterdienst, DWD). Data are level-2 data as 10-minute averages. TableOfContents: Surface Upward Sensible Heat Flux; Surface Upward Sensible Heat Flux Quality Flag; Surface Upward Latent Heat Flux; Surface Upward Latent Heat Flux Quality Flag Technical Info: dimension: 144 x 1; temporalExtent_startDate: 2021-05-01 00:00:00; temporalExtent_endDate: 2021-08-31 23:59:59; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: BLS-900 optical large aperture scintillometer, MWSC-160 microwave scintillometer; instrumentType: Scintillometer; instrumentLocation: Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Scintec AG, Radiometer Physics GmbH Methods: The fluxes have been derived from simultaneous operation of a BLS-900 large-aperture optical scintillometer and a MWSC-160 microwave scintillometer. Data acquisition, data analysis and flux calculations were performed with the mwsc.exe software package. Structure parameters and the temperature-humidity correlation coefficient (rTq) for each 10min time interval have been calculated twice based on different settings, i.e. using the methods described in Hill (1997, https://doi.org/10.1175/1520-0426(1997)014<0456:AFOASL>2.0.CO;2) which assumes a constant rTq = -0.6 at night and rTq = 0.8 during daytime and in Lüdi et al. (2003, https://doi.org/10.1007/s10546-005-1751-1) which calculates rTq from the cross-correlation of the optical and microwave signals. The similarity model proposed by Koijmans and Hartogensis (2016, https://doi.org/10.1007/s10546-016-0152-y) was then used to derive the heat fluxes from the structure parameters. Using temperature and humidity profile measurements at the Falkenberg tower and measurements of the radiation budget, the deduced fluxes have been checked for sign consistency with the mean gradients of temperature and humidity and for a violation of the energy budget. In the end “most plausible” fluxes from the two methods (Hill, Lüdi et al. – see above) have been merged to a composite to ensure a better availability / quality of the fluxes especially around sunrise and sunset when the assumptions of the Hill approach typically fail. Quality flags have been assigned to each flux value, where G = good, D = dubious, B = bad, M = missing. Units: Units for all variables (see TableOfContents): W/m²;1;W/m²;1 geoLocations: BoundingBox: westBoundLongitude: 14.1199 degrees East; eastBoundLongitude: 14.1222 degrees East; southBoundLatidude: 52.1665 degrees North; northBoundLatitude: 52.2096 degrees North; geoLocationPlace: Germany, UTM zone 33U Locations: Transmitters: 52.1665 °N, 14.1222 °E, 124 m above mean sea level, 51 m above ground Receivers: 52.2096 °N, 14.1199 °E, 129 m above mean sea level, 26 m above ground Size: Data (level 2 only) are packed into one packed tar-archive. Its size is roughly 400 Kbyte. Format: netCDF DataSources: Single site ground-based remote sensing, see "Technical Info" for instruments Contact: eileen.paeschke (at) dwd.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-oms-l2-turb.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html https://www.fdr.uni-hamburg.de/record/11980 10.25592/uhhfdm.11980 oai:fdr.uni-hamburg.de:11980 eng doi:10.1175/1520-0426(1997)014<0456:AFOASL>2.0.CO;2 doi:10.1007/s10546-005-1751-1 doi:10.1007/s10546-016-0152-y doi:10.25592/uhhfdm.9824 doi:10.25592/uhhfdm.9902 url:https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html url:https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-oms-l2-turb.html doi:10.25592/uhhfdm.11979 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Atmosphere Scintillometer Measurements Vertical Fluxes Sensible heat flux Latent heat flux FESSTVAL SAMD Regional-scale vertical fluxes from an optical-microwave scintillometer during FESSTVAL 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10563 2024-04-18T15:30:39Z user-icdc user-cen user-uhh

Lange, Ingo Ament, Felix 2022-09-09 The data set "_basic" contains level1 meteorological standard variables of the eddy-covariance station in Birkholz (Rietz-Neuendorf) during FESSTVaL 2021 from May to August. All values are retrieved by averaging the single samples of each minute. The individual sampling frequencies of the instruments reach from 5 seconds (pressure), 1 second (temperature and humidity) to 0.05 seconds (wind). Further humidity values are calculated from temperature, relative humidity, and pressure. The "_rad" data set contains level1 solar and infra-red radiation and soil heat flux variables of the eddy-covariance station in Birkholz (Rietz-Neuendorf) during FESSTVaL 2021 from May to August. All values are retreived by averaging the single samples of each minute. The sampling frequency of the instrument is 1 second. Further values like albedo and radiation balance are calculated from the measured values. The "_turb" data set contains level1 turbulence variables of the eddy-covariance station in Birkholz (Rietz-Neuendorf) during FESSTVaL 2021 from May to August. A full set of variables is provided including mean values, standard deviations and covariances of all wind components (u, v, w) and the sonic tmeperature. The original sampling frequency is 20 Hz. A humidity (and CO2) sensor was attached for retreiving water vapor and latent heat fluxes. All variables are calculated for 1 minute each. Further values are friction velocity and TKE. Quality: The "_basic" data was processed by approved software including basic plausibility checks. Gaps in the data set can be caused by maintenance work, power loss, mal-function, or similar reasons. Single missing values are filled by interpolation. A description of the used formulas and plausibility checks exists and can be retrieved from the contact person. The "_rad" data was processed by approved software including basic plausibility checks. Gaps in the data set can be caused by maintenance work, power loss, mal-function, or similar reasons. Single missing values are filled by interpolation. A description of the used formulas and plausibility checks exists and can be retreived from the contact person. The "_turb" data was processed by approved software including basic plausibility checks. Gaps in the data set can be caused by maintenance work, power loss, mal-function, or similar reasons. Single missing values are filled by interpolation. The turbulence values are calculated in accordance to VDI guidelines. No further corrections were applied. A description of the used formulas and plausibility checks exists and can be retreived from the contact person. The LI-7500 does not work properly in the rain. Use the window contamination value (AGC) for filtering such periods. Location: FESSTVaL supersite Birkholz, 15848 Rietz-Neuendorf, Bandenburg, Germany, 52.20132° N, 14.19183° E, 68 m AMSL Instruments: Instruments _basic: Pressure Sensor VAISALA PTB200A Humidity and Temperature Meter VAISALA HMT337 3D Sonic Anemometer METEK USA-1 Instruments _rad: Net Radiometer Kipp & Zonen CNR4 Heat Flux Plate Hukseflux HFP01 Instruments _turb: 3D Sonic Anemometer METEK USA-1 H2O/CO2 Gas Analyzer LI-COR LI-7500 https://www.fdr.uni-hamburg.de/record/10563 10.25592/uhhfdm.10563 oai:fdr.uni-hamburg.de:10563 eng doi:10.25592/uhhfdm.10562 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode measurement atmosphere FESSTVaL Birkholz Gut Hirschaue pressure temperature humidity wind shortwave radiation longwave radiation radiation balance global radiation soil heat flux turbulence moisture heat flux momentum flux standard deviations covariance Standard meteorology Pressure, Temperature, Humidity, Radiation fluxes and Soil Heat Flux, and Turbulent fluxes (2021) from FESSTVaL Supersite in Birkholz, Germany info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:16655 2025-01-14T15:21:22Z user-cen user-icdc user-cliccs

Sadikni, Remon Kern, Stefan 2025-01-15 Abstract: Original forest cover fraction, vegetation cover fraction and fraction of non-vegetated area on 250m grid resolution sinusoidal grid were obtained in HDF file format from https://lpdaac.usgs.gov/mod44bv061/, read together with the bit-encoded quality information and converted into netCDF file format with latitude/longitude coordinates of every 250m x 250m pixel, and decoded quality flag information included (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html). TableOfContents: forest cover fraction; other vegetation cover fraction; non-vegetated land cover fraction; forest cover fraction standard deviation; quality flag Technical Info: dimension: 4800 columns x 4800 rows x unlimited; temporalExtent_startDate: 2023-03-06; temporalExtent_endDate: 2024-03-04; temporalResolution: yearly; spatialResolution: 250; spatialResolutionUnit: meters; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: meters; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: meters; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] https://lpdaac.usgs.gov/products/mod44bv061/; [2] Townshend, J., et al., User Guide for the MODIS Vegetation Continuous Fields product Collection 6.1, verison 1, https://lpdaac.usgs.gov/documents/1494/MOD44B_User_Guide_V61pdf; [3] Algorithm Theoretical Basis Document (ATBD), https://lpdaac.usgs.gov/documents/113/MOD44B_ATBD.pdf; [4] Carroll, M., et al., 2011. Vegetative Cover Conversion and Vegetation Continuous Fields. In: Ramachandran, B., C. O. Justice, and M. Abrams (eds.), Land Remote Sensing and Global Environment Change: NASA's Earth Observing System and the Science of ASTER and MODIS. Springer Verlag.; [5] Hansen, M., et al., 2005. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Rem. Sens., 26(19), 4359-4380. Units: Units for all variables (see TableOfContents): percent; percent; percent; percent; 1 geoLocations: westBoundLongitude:depends on tile; eastBoundLongitude: depends on tile; southBoundLatitude: depends on tile; northBoundLatitude: depends on tile; geoLocationPlace: global on land, see: https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Size: files are packed into one zip-archive per year with an average size of about 25.6 GByte. Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD44B.061 (last accessed 2024-12-27), see also https://lpdaac.usgs.gov/products/mod44bv061/ (last accessed: 2024-12-27) Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html https://www.fdr.uni-hamburg.de/record/16655 10.25592/uhhfdm.16655 oai:fdr.uni-hamburg.de:16655 eng doi:10.5067/MODIS/MOD44B.061 doi:10.25592/uhhfdm.11198 url:https://lpdaac.usgs.gov/products/mod44bv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html doi:10.25592/uhhfdm.16656 doi:10.25592/uhhfdm.12921 doi:10.25592/uhhfdm.11197 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Forest Cover Fraction Vegetation Cover Fraction Sinusoidal grid tiles Yearly Satellite Remote Sensing MODIS EOS-Terra University of Maryland ICDC MODIS Collection 6.1 sinusoidal tiles yearly Forest and Vegetation Cover Fraction Extension 02 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:11449 2025-04-14T11:24:03Z user-cliccs user-cen user-icdc user-uhh

Sadikni, Remon Kern, Stefan Böhner, Jürgen Dietrich, Helge Wehberg, Jan 2023-11-27 Abstract: Gridded climate time series for Germany derived through downscaling of EURO-CORDEX historical simulations and climate projections from following ensemble members (www.euro-cordex.net):: MPI-M-MPI-ESM-LR(r1)_CLMcom-CCLM4-8-17: RCPs 8.5, 4.5, 2.6 and historical (MPI_CLM) ICHEC-EC-EARTH(r12)_KNMI-RACMO22E(v1): RCP 8.5 and historical (ECE_RAC) CCCmaCanESM2_r1i1p1_CLMcomCCLM4817_v1: RCP 8.5 and historical (CA2_CLM) All time series were consistently calculated at daily resolution and a grid cell spacing of 250 × 250 meter. Historical 1950–2005 data sets and 2006–2100 RCP projections comprise of mean temperature, minimum temperature, maximum temperature, precipitation, global radiation, air pressure, wind speed, specific humidity and delineated variables (relative humidity, potential evapotranspiration, water vapor pressure). All data sets except specific humidity and surface air pressure are available twice, as downscaled but non-bias corrected EURO-CORDEX data, and as bias corrected data sets. Correction terms for empirical adjustment of downscaling results were computed according to Sachindra et al. (2014) using gridded WP-KS-KW data as observational reference (Dietrich et al. 2019). Dietrich, H., Wolf, T., Kawohl, T., Wehberg, J., Kändler, G., Mette, T., Röder, A. & Böhner, J. (2019): Temporal and spatial high-resolution climate data from 1961-2100 for the German National Forest Inventory (NFI). – Annals of Forest Science 76: 6, https://doi.org/10.1007/s13595-018-0788-5. Sachindra, D.A., Huang, F., Bartona, A. & Pereraa, B.J.C. (2014): Statistical downscaling of general circulation model outputs to precipitation – part 2: bias-correction and future projections. – Int. J. Climatol. 34: 3282–3303, https://doi.org/10.1002/joc.3915. TableOfContents: daily mean 2m-air temperature (tav); daily minimum 2m-air temperature (tmn), daily maximum 2m-air temperature (tmx); daily sum of precipitation (prz); daily sum of global radiation (sgz); daily surface air pressure (psz); daily mean 10m wind speed (wsp); daily mean specific humidity (hus); daily mean relative humidity (rhm); potential evapotranspiration (pet); daily mean water vapor pressure (vap) TechnicalInfo: dimension: 2578 columns x 3476 rows; temporalExtent_startDate_Historlcal: 1950-01-01 00:00:00; temporalExtent_endDate_Historical: 2005-12-31 23:59:59; temporalDuration_Historical: 56; temporalDurationUnit_Historical: a; temporalExtent_startDate_RCPs: 2006-01-01 00:00:00; temporalExtent_endDate_RCPs: 2100-12-31 23:59:59; temporalDuration_RCPs: 95; temporalDurationUnit_RCPs: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 250; spatialResolutionUnit: m; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: m; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: m; verticalResolution: none; verticalResolutionUnit: none Methods: Statistical downscaling of EURO-CORDEX data is performed, merging MOS (Model Output Statistics) downscaling with surface parameterization techniques (Böhner & Antonic 2009; Böhner & Bechtel 2018) to account for terrain-forced fine-scale topoclimatic variations. For a comprehensive description of the methods, see Wehberg & Böhner (2023). Böhner, J. & Antonic, O. (2009): Land-Surface Parameters Specific to Topo-Climatology. – In: Hengl, T & Reuter, H.I. [Eds.]: Geomorphometry: Concepts, Software, Applications. – Developments in Soil Science, Elsevier, Volume 33, 195-226, https://doi.org/10.1016/S0166-2481(08)00008-1. Böhner, J. & Bechtel, B. (2018): GIS in Climatology and Meteorology. – In: Huang, B. [Ed.]: Comprehensive Geographic Information Systems. – Vol. 2, pp. 196–235. Oxford: Elsevier. http://dx.doi.org/10.1016/B978-0-12-409548-9.09633-0. Böhner, J. & Wehberg, J.-A. (2022): Schlussbericht zum Verbundvorhaben Standortsfaktor Wasserhaushalt im Klimawandel (WHH-KW); Teilvorhaben 4: Klimadaten. Universität Hamburg/Centrum für Erdsystemforschung und Nachhaltigkeit (CEN)/Institut für Geographie/Abt. Physische Geographie. Waldklimafonds, Bundesministerium für Ernährung und Landwirtschaft, Bundesministerium für Umwelt, Naturschutz und nukleare Sicherheit. 14 Seiten. Wehberg, J.-A. & Böhner, J. (2023): Hochaufgelöste Klimaprojektionen für Deutschland. Forstliche Forschungsberichte München 224. Schriftenreihe des Zentrums Wald-Forst-Holz Weihenstephan, ISBN 3-933506-55-7, pp. 69-78. Quality: -- Units: degC; degC; degC; mm; MJ/m2; hPa; m/s; kg/kg; percent; mm; hPa ScaleFactors: 0.1; 0.1; 0.1; 0.1; 0.1; 0.1; 0.1; 1; 1; 0.1; 1 GeoLocation: westBoundCoordinate: 278750; westBoundCoordinateUnit: m; eastBoundCoordinate: 923000; eastBoundCoordinateUnit: m; southBoundCoordinate: 5234000; southBoundCoordinateUnit: m; northBoundCoordinate: 6102750; northBoundCoordinateUnit: m; ProjectCoordinateSystem: Transverse_Mercator; ProjectionCoordinateSystemParameters: [+proj=utm +datum=WGS84 +zone=32 +no_defs]. geoLocationPlace:Germany; UTMZone: 32 Size: Files are stored into one NetCDF-file per year and variable and uploaded as tar-archives - one per variable, model and run. The file size of the netCDF files differs between 36 and 206 GB per future scenario simulation and variable (95 years) and between 21 and 113 GB per historical run and variable (56 years). Format: netCDF DataSources: EURO-CORDEX data published via ESGF (https://cordex.org/data-access/esgf/). Jacob, D., Petersen, J., Eggert, B. et al. EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14, 563–578 (2014). https://doi.org/10.1007/s10113-013-0499-2 Contact: Prof. Dr. Jürgen Böhner, Universität Hamburg, Center for Earth System Research and Sustainability, Institute of Geography, Bundesstraße 55, 20146 Hamburg, juergen.boehner (at) uni-hamburg.de; https://www.geo.uni-hamburg.de/en/geographie/mitarbeiterverzeichnis/boehner.html Webpage: https://www.waldklimafonds.de/ and https://www.lwf.bayern.de/boden-klima/wasserhaushalt/223446/index.php https://www.fdr.uni-hamburg.de/record/11449 10.25592/uhhfdm.11449 oai:fdr.uni-hamburg.de:11449 eng doi:10.1007/s13595-018-0788-5 doi:10.1002/joc.3915 doi:10.1007/s10113-013-0499-2 doi:10.25592/uhhfdm.11448 info:eu-repo/semantics/restrictedAccess Climate Model Regionalisation Historical simulations Climate projections Downscaling Meteorology Global radiation 2m-air temperature Surface air pressure Precipitation Specific humidity Relative humidity Water vapor pressure Potential evaporation 10m wind speed Germany Temporal and spatial high-resolution climate data from regional and global climate models for the German National Forest Inventory for 1950-2100 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:17916 2025-09-12T10:28:45Z user-icdc user-cen user-uhh

Sadikni, Remon Kern, Stefan 2025-09-10 Abstract: In the framework of the European Space Agency (ESA) Climate Change Initiative (CCI+) sea ice essential climate variable (ECV) project phase 2, Landsat-1 Multispectral Scanner (MSS) images were obtained from https://earthexplorer.usgs.gov/ for both hemispheres with the purpose to evaluate the ESA CCI+ Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR) sea-ice concentration data product v1.1. From these Landsat-1 MSS images surface broadband albedo values were estimated based on channels 4 to 7 (mostly 4, 5, and 7). Secondly, a supervised classification was employed, classifying the broadband albedo maps into open water, thin/bare ice, and thick/snow-covered ice using simple threshold values selected based on visual manual interpretation of the images. Thresholds used for this classification are provided in the metadata txt-file along with the data. MSS images have been pre-processed and quality assessed as much as possible to avoid artefacts from missing and/or corrupt scanlines and clouds or cloud shadows. TableOfContents: surface type flag (100: open water, 150: thin or bare sea ice, 200: thick or snow-covered ice, 255: land, missing data, or clouds) Technical Info: dimensions actual: variable, depends on how the Landsat scene fits into a rectangular bounding box determined by the minimum and maximum values of latitude and longitude of each scene, something around 4000 columns x 4000 rows; temporalExtent_startDate: 1974-03-11; temporalExtent_endDate: 1974-09-08; temporalResolution: ~28 s / image; spatialResolution: 60; spatialResolutionUnit: meters; horizontalResolutionXdirection: 60; horizontalResolutionXdirectionUnit: meters; horizontalResolutionYdirection: 60; horizontalResolutionYdirectionUnit: meters; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Landsat-1: Multispectral Scanner (MSS); instrumentType: optical sensor; instrumentLocation: Landsat-1; instrumentProvider: NASA Methods: [1] ESA CCI sea ice ecv project phase 2 product validation and intercomparison report for sea-ice concentration: D4.1_SICCI_P2_PVIR-SIC_Issue_1.1.pdf (chapter 3.3); [2] Koepke, P., Removal of Atmospheric Effects from AVHRR albedos, J. Appl. Meteorol., 28, 1341-1348, 1989; [3] Chander, G., Markham, B. L., and Helder, D. L.: Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Rem. Sens. Environ. 113, 893-903, https://doi.10.1016/j.rse.2009.01.007, 2009; [4] Metadata file containing thresholds for open water - thin/bare ice and thin/bare ice - thick/snow covered ice discrimination, the sun elevation angle and the used channels for 1974: 1974_NH_settings.txt Units: 1 geoLocations: Northern Hemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: 50.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: Northern Hemisphere over water Size: (files are packed into one zip-file per year) Northern Hemisphere: 50 files in total for 1974; 19.1 Gbyte (zipped: 12.0Gbyte) Format: netCDF DataSources: https://earthexplorer.usgs.gov/ [last access: 2025-09-01] Contact: stefan.kern (at) uni-hamburg.de https://www.fdr.uni-hamburg.de/record/17916 10.25592/uhhfdm.17916 oai:fdr.uni-hamburg.de:17916 eng url:https://earthexplorer.usgs.gov/ doi:10.25592/uhhfdm.17915 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Polar Oceans Sea Ice Supervised Classification Satellite Remote Sensing Landsat-1 MSS USGS ICDC Landsat-1 Multispectral Scanner surface type over water from supervised classification of surface broadband albedo estimates info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:18068 2025-11-07T16:23:48Z user-cen user-cliccs user-icdc user-uhh

Sadikni, Remon Kern, Stefan Yakubu, Fuseini Böhner, Jürgen Schickhoff, Udo Scholten, Thomas Hasson, Shabeh Ul 2025-11-07 Abstract: This dataset provides globally consistent, bias-corrected climate data at 0.5° spatial resolution, consisting of a set of seven climate variables derived from three General Circulation Models (GCMs) participating in CMIP5, downscaled by 10 CORDEX Regional Climate Model (RCM) simulations and bias-corrected globally for the period 1950/1960–2099. It includes data from three climate change scenarios, namely RCP2.6, RCP4.5 and RCP8.5. The three GCMs are: ICHEC-EC-EARTH, MPI-M-MPI-ESM-LR, NOAA-GFDL-GFDL-ESM2M. Data are originally available as one netCDF file per GCM (3) per variable (7, NOAA-GFDL-GFDL-ESM2M: 5) per run (4, NOAA-GFDL-GFDL-ESM2M: 3). Available here are netCDF files per quantity (see TableOfContents), run (historical, rcp26, rcp45, rcp85), and GCM (see Size for the overall sum per GCM). TableOfContents: daily mean 2m-air temperature (tas); daily minimum 2m-air temperature (tasmin), daily maximum 2m-air temperature (tasmax); daily sum of precipitation (pr); daily mean surface downwelling longwave radiation (rlds)*; daily mean 10m wind speed (sfcWind)*; daily mean relative humidity (hurs) *: These variables are NOT included in the NOAA-GFDL-GFDL-ESM2M driven data. TechnicalInfo: dimension: 720 columns x 360 rows; temporalExtent_startDate_Historlcal: 1950-01-01 00:00:00; temporalExtent_endDate_Historical: 2019-12-31 23:59:59; temporalDuration_Historical: 70; temporalDurationUnit_Historical: a; temporalExtent_startDate_RCPs: 2020-01-01 00:00:00; temporalExtent_endDate_RCPs: 2099-12-31 23:59:59; temporalDuration_RCPs: 80; temporalDurationUnit_RCPs: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 0.5; spatialResolutionUnit: degree; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degree; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degree; verticalResolution: none; verticalResolutionUnit: none *) For MPI-M-MPI-ESM-LR: temporalExtent_startDate_Historlcal: 1960-01-01 00:00:00; temporalExtent_endDate_Historical: 2019-12-31 23:59:59; temporalDuration_Historical: 60; Methods: The ISIMIP3BASD v2.5 bias correction method (see Lange [2019; 2021]) was applied to adjust systematic biases using the GSWP3-W5E5 observational dataset. The regional climate models (RCMs) used are: (listed are Institution/working group, RCM Model, Driving GCM): Climate Service Center Germany (GERICS), REMO2009, MPI-ESM-LR Swedish Meteorological and Hydrological Institute (SMHI), RCA4, MPI-ESM-LR Climate Limited-area Modelling Community (CLMcom), CCLM4-8-17-CLM3-5, MPI-ESM-LR Climate Limited-area Modelling Community (CLMcom), CCLM5-0-2, MPI-ESM-LR Universite du Quebec a Montreal, CRCM5, MPI-ESM-LR Swedish Meteorological and Hydrological Institute (SMHI), RCA4, ICHEC-EC-EARTH Climate Limited-area Modelling Community (CLMcom), CCLM4-8-17-CLM3-5, ICHEC-EC-EARTH Climate Limited-area Modelling Community (CLMcom), CCLM5-0-2, ICHEC-EC-EARTH Swedish Meteorological and Hydrological Institute (SMHI), RCA4, NOAA-GFDL-GFDL-ESM2M National Center for Atmospheric Research, WRF, NOAA-GFDL-GFDL-ESM2M The historical runs begin 1950-01-01 (ICHEC-EC-EARTH and NOAA-GFDL-GFDL-ESM2M) or 1960-01-01 (MPI-M-MPI-ESM-LR) and end 2005-12-31. Historical runs are appended by rcp85 runs for years 2006-01-01 to 2019-12-31. All projection runs begin 2020-01-01 and end 2099-12-31. The routines (python) used to create and work with the data sets are available from this web page as well: Discontinuity_Analysis_GlobCORD-HC.py; IBICUS-BIAS-CORRECTION.py For more information please take a look at this publication: Yakubu et al., 2025. Quality: Not all of the domains have been downscaled by CORDEX RCMs. Therefore, data files for scenario rcp26 only contain 7 CORDEX domains; all other files contain 8 domains (see also https://cordex.org/domains/cordex-domain-description/) Units (see TableOfContents): K; K; K; kg m-2 s-1; W m-2; m s-1; percent GeoLocation: westBoundCoordinate: -167.0; westBoundCoordinateUnit: degrees East; eastBoundCoordinate: 180.0; eastBoundCoordinateUnit: degrees East; southBoundCoordinate: -56.0; southBoundCoordinateUnit: degrees North; northBoundCoordinate: 76.0; northBoundCoordinateUnit: degrees North Size: ICHEC-EC-EARTH: 137.7 GByte, MPI-M-MPI-ESM-LR: 130.6 GByte, NOAA-GFDL-GFDL-ESM2M: 55.5 GByte Format: netCDF DataSources: See the file "GloBCORD-HD_ESMs-RCMs.pdf" Contact: fuseini.yakubu (at) uni-hamburg.de; shabeh.hasson (at) uni-hamburg.de Webpage: https://www.geo.uni-hamburg.de/geographie/abteilungen/physische-geographie/arbeitsgruppen/ag-hareme.html https://www.fdr.uni-hamburg.de/record/18068 10.25592/uhhfdm.18068 oai:fdr.uni-hamburg.de:18068 eng info:eu-repo/semantics/altIdentifier/doi/10.25592/uhhfdm.17395 doi:10.5281/zenodo.4686991 doi:10.5194/gmd-12-3055-2019 doi:10.25592/uhhfdm.17560 doi:10.25592/uhhfdm.17799 doi:10.1038/s41597-025-06200-4 doi:10.25592/uhhfdm.17395 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Model Downscaling CORDEX Air Temperature Relative Humidity Surface Wind Speed Precipitation Amount Surface Downwelling Longwave Radiation CMIP5 ICDC Global Bias-Corrected CORDEX Datasets at Half Degree Resolution info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:17799 2025-12-27T21:24:04Z user-cen user-icdc user-cliccs user-uhh

Sadikni, Remon Kern, Stefan Yakubu, Fuseini Böhner, Jürgen Schickhoff, Udo Scholten, Thomas ul Hasson, Shabeh 2025-12-09 Abstract: This dataset provides globally consistent, bias-corrected climate data at 0.25° grid resolution, consisting of a set of five climate variables derived from four General Circulation Models (GCMs) participating in CMIP5 downscaled by 4 CORDEX Regional Climate Model (RCM) simulations and bias-corrected globally for the period 1979–2099 for 7 to 10 CORDEX domains. It includes data from two climate change scenarios, namely RCP2.6 and RCP8.5. The CMIP5 GCMs are: MOHC-HadGEM2-ES, MPI-M-MPI-ESM-LR and MR, and NCC-NorESM1-M. Available here are netCDF files per run, GCM and variable (see Size for the overall sum per GCM). TableOfContents: daily mean 2m-air temperature (tas); daily minimum 2m-air temperature (tasmin), daily maximum 2m-air temperature (tasmax); daily sum of precipitation (pr); daily mean surface downwelling shortwave radiation (rsds) TechnicalInfo: dimension: 1440 columns x 720 rows; temporalExtent_startDate_Historlcal: 1979-01-01 00:00:00; temporalExtent_endDate_Historical: 2005-12-31 23:59:59; temporalDuration_Historical: 27; temporalDurationUnit_Historical: a; temporalExtent_startDate_RCPs: 2006-01-01 00:00:00; temporalExtent_endDate_RCPs: 2099-12-31 23:59:59; temporalDuration_RCPs: 94; temporalDurationUnit_RCPs: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 0.25; spatialResolutionUnit: degree; horizontalResolutionXdirection: 0.25; horizontalResolutionXdirectionUnit: degree; horizontalResolutionYdirection: 0.25; horizontalResolutionYdirectionUnit: degree; verticalResolution: none; verticalResolutionUnit: none Methods: The ISIMIP3BASD v2.5 bias correction method (see Lange [2019; 2021]) was applied to adjust systematic biases while preserving the climate change signals. This parametric quantile mapping approach: • Corrects biases across all percentiles of variable distributions • Preserves trends in these percentiles • Applies variable-specific treatments (e.g., handling drizzle issues for precipitation) • Maintains physical relationships between variables (particularly for temperature variables) using the CHELSA-W5E5 observational reference dataset. The regional climate models (RCMs) used are: (listed are Institution/working group; RCM Models; Driving GCMs): Climate Service Center Germany (GERICS), Hamburg, Germany; REMO2015 v1; MPI-ESM-LR and NCC-NorESM1-M and MOHC-HadGEM2-ES Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy; RegCM4-4 v0 and RegCM4-7 v0; MPI-ESM-MR Centre pour l’Étude et la Simulation du Climat à l’Échelle Régionale (ESCER), Université du Québec à Montréal, Canada; CRCM5 v1; MPI-ESM-MR The historical runs begin 1979-01-01 and end 2005-12-31. All projection runs begin 2006-01-01 and end 2099-12-31. The routines (python) used to create and work with the data sets are available from this web page as well: Discontinuity_Analyses_GloBCORD-QD.py Quality: Not all of the domains have been downscaled by CORDEX RCMs. Therefore, data files for MPI-M-MPI-ESM-MR only contain 8 (rcp26: 7) CORDEX domains; all other files contain 10 domains (see also https://cordex.org/domains/cordex-domain-description/) Units: K; K; K; kg m-2 s-1; W m-2 GeoLocation: westBoundCoordinate: -165.0; westBoundCoordinateUnit: degrees East; eastBoundCoordinate: 179.0; eastBoundCoordinateUnit: degrees East; southBoundCoordinate: -55.0; southBoundCoordinateUnit: degrees North; northBoundCoordinate: 76.0; northBoundCoordinateUnit: degrees North Size: MOHC-HadGEM2-ES: 280.3 GByte, MPI-M-MPI-ESM-LR: 271.0 GByte, MPI-M-MPI-ESM-MR: 214.3GByte, NCC-NorESM1-M: 280.4 GByte Format: netCDF DataSources: See the file "GloBCORD-QD_Description.pdf" Contact: fuseini.yakubu (at) uni-hamburg.de; shabeh.hasson (at) uni-hamburg.de Webpage: https://www.geo.uni-hamburg.de/geographie/abteilungen/physische-geographie/arbeitsgruppen/ag-hareme.html https://www.fdr.uni-hamburg.de/record/17799 10.25592/uhhfdm.17799 oai:fdr.uni-hamburg.de:17799 eng doi:10.5281/zenodo.4686991 doi:10.5194/gmd-12-3055-2019 doi:10.25592/uhhfdm.18068 doi:10.25592/uhhfdm.17798 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Model Downscaling CORDEX Air Temperature Precipitation Amount Surface Downwelling Shortwave Radiation CMIP5 ICDC Global Bias-Corrected CORDEX Datasets at Quarter Degree Resolution info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:18378 2026-02-24T13:45:24Z user-icdc user-cen user-uhh user-cliccs

Sadikni, Remon Kern, Stefan 2026-02-24 Abstract: Original LAI and FAPAR data (see https://lpdaac.usgs.gov/products/mod15a2hv061/) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the LAI and FAPAR data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html and https://doi.org/10.25592/uhhfdm.16751). These are subsequently read and re-gridded onto an equi-rectangular climate modeling grid (CMG). Only those LAI and FAPAR values are used where i) the cloud flag indicates a maximum of two cloud influences, and where ii) cloud cover is clearly defined, i.e. "assumed clear sky" is not used. Flag layers are summarized such that there is gridded information about 1) cloud fraction, 2) fraction of average and high aerosol load, 3) primary and secondary land-cover type, and 4) primary and secondary quality flag. Primary and secondary refer to the highest and 2nd-highest pixel count of the respective type or flag within the grid cell. Note that the count of valid values differs for the grid cell mean LAI and FAPAR and their variance, and for the grid-cell mean retrieval standard deviation. TableOfContents: Grid cell mean FAPAR; Grid cell mean LAI; FAPAR variance across grid cell; LAI variance across grid cell; Grid cell mean FAPAR retrieval standard deviation; Grid cel mean LAI retrieval standard deviation; Count of useful FAPAR or LAI values per grid cell; Count of useful FAPAR or LAI retrieval standard deviation values in grid cell; Primary quality flag; Secondary quality flag; Primary land cover; Secondary land cover; Grid cell cloud fraction; Grid cell aerosol fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2025-12-31; temporalResolution: 8-daily; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] MODIS collection 6.1 (C61) LAI/FPAR Product User Guide, https://lpdaac.usgs.gov/documents/926/MOD15_User_Guide_V61.pdf ; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document (ATBD), v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf; [3] Yang, et al., From validation to algorithm improvement. Trans. Geosci. Rem. Sens., 44, 1885-1898, 2006; [4] Morisette, et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, Trans. Geosci. Rem. Sens., 44, 1804-1817, 2006; [5] Garrigues, et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008; [6] https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html Units: Units for all variables (see TableOfContents): percent, m2/m2, percent, m4/m4, percent, m2/m2, 1, 1, 1, 1, 1, 1, percent, percent geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: (files are packed into one zip-file per year) 2000: 40 files, 9864980 byte / file 2001: 44 files (20010618 and 20010626 are missing) 2002: 35 files (20020101 until 20020322 are missing) 2003-2025: 46 files / year Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD15A2H.061 [last access: 2026-01-12], see also: https://lpdaac.usgs.gov/products/mod15a2hv061/ [last access: 2026-01-12] Data on sinusoidal grid tiles in netCDF-format: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2026-02-24] and https://doi.org/10.25592/uhhfdm.10866 [last access: 2026-02-24] Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2026-02-24] https://www.fdr.uni-hamburg.de/record/18378 10.25592/uhhfdm.18378 oai:fdr.uni-hamburg.de:18378 eng doi:10.25592/uhhfdm.10866 url:https://lpdaac.usgs.gov/products/mod15a2hv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html doi:10.25592/uhhfdm.16752 doi:10.25592/uhhfdm.8584 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Leaf Area Index LAI FAPAR Global Maps 8-daily Satellite Remote Sensing MODIS EOS-Terra Boston University ICDC MODIS Collection 6.1 global 8-daily LAI and FAPAR info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:637 2023-01-25T09:18:58Z user-uhh user-cen user-icdc

Jahnke-Bornemann, Annika 2019-09-17 The weather and climate change in the North Atlantic has a strong influence on Europe's weather and especially the position of steering lows is of great interest. In the long-term mean of atmospheric pressure in the North Atlantic appeared two regions with increased activity. These lows have a significant impact on the meridional heat flows to Europe. The two activity centres are located on the one hand over the Irminger Sea near Iceland and on the other over the Norwegian Sea near the Lofoten Islands. There is also a primary and secondary minimum in the long-term mean air pressure field at sea level. To study these minima, a climate index of the Iceland-Lofoten pressure difference (ILD index) was defined [Jahnke-Bornemann 2008, 2010]. The calculation of the index was carried out, analogous to the station-based NAO index [Hurrell, 1995], as the difference of the standardized atmospheric pressure anomalies of a defined Iceland and Lofoten region. This data set contains the monthly ILD-Index, calculated from mean sea level pressure (MSLP) fields from ECMWF Reanalysis data ERA 40 (9/1957-12/1978) and ERA Interim (1/1979-1/2017) for the period September 1957 to January 2017, with the reference period 01/1979-12/2000. The areas used for the calculation of pressure means are 70N to 72.5N, 13.5E to 15.75 E for Lofoten, and 60N to 63N, 35W to 38W for Iceland. https://www.fdr.uni-hamburg.de/record/637 10.25592/uhhfdm.637 oai:fdr.uni-hamburg.de:637 eng doi:10.1111/j.1600-0870.2009.00401.x urn:urn:nbn:de:gbv:18-44949 doi:10.25592/uhhfdm.636 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode climate index Iceland Lofoten North Atlantic ILD pressure low Iceland-Lofotes-Difference-Index (ILD) 1957-2017 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8681 2022-11-07T13:42:24Z user-cliccs user-cen user-icdc

Sadikni, Remon Jahnke-Bornemann, Annika Kern, Stefan 2021-02-10 Abstract: The soil moisture time series data of the EUMETSAT H-SAF product H115 and its extension H116 based on MetOp-A and -B ASCAT data, processing version v5, are converted into geographic maps (cartesian grid) of daily running 5-day average/composite soil moisture (SM) distribution separately for ascending and descending overpasses. Two different 5-day SM distributions are given: one is based solely on nominally computed SM, the other one includes also those SM values which were negative (down to -25%, correction flag = 1) or positive (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. All data are interpolated into a cartesian grid of x- and y-dimensions of original grid. For more information see the respective global attribute in the netCDF file. TableOfContents: soil moisture; soil moisture noise; soil moisture extended; soil moisture extended noise; soil moisture status flag; number of overpasses per grid cell; historic probability of snow cover; historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD Technical Info: dimensons: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2007-01-01; temporalExtent_endDate: 2020-06-30; temporalResolution: daily; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.1125; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-A, MetOp-B); instrumentProvider: EUMETSAT, ESA; License: The following applies to the original product: All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products. Methods: For a description of the methods used to obtain the 5-day average / composite data we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi:10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.1, 2019; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling ( H115) and Extension (H116), v0.1, 2019; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.3, 2019. Units: units for all variables (see TableOfContents): percent, percent, percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: -90.0 degrees North; northBoundLongitude: 90.0 degrees North; geoLocationPlace: global over land Size: 730 (leap year: 732) files per year [note: there are 2 files per day, one for the ascending, one for the descending overpasses]; 61.568900 MegaByte per file; 43.891984 GigaByte per year; 592.843473 GigaByte in total (provided as two zip-files per year) Format: netCDF DataSources: Original Data as time series on a 12.5 km DGG Grid: https://doi.org/10.15770/EUM_SAF_H_0006 (last access: 2020-07-23); this original product comes with the following notion: "All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products." See also: http://hsaf.meteoam.it; https://navigator.eumetsat.int/product/EO:EUM:DAT:METOP:H115; https://navigator.eumetsat.int/product/EO:EUM:DAT:METOP:H116 Contact: stefan.kern (at) uni-hamburg.de Web page: https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/8681 10.25592/uhhfdm.8681 oai:fdr.uni-hamburg.de:8681 eng doi:10.15770/EUM_SAF_H_0006 url:http://hsaf.meteoam.it url:https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html doi:10.25592/uhhfdm.8680 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Daily Satellite Remote Sensing ASCAT MetOp-A/B EUMETSAT HSAF University of Vienna ICDC ASCAT Global Maps of daily running 5-daily mean surface soil moisture info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:9891 2023-01-25T09:34:32Z user-cen user-icdc user-uhh

Sarah, Wiesner 2022-02-02 Data Policy for FESSTVaL campaign data This policy holds for all FESSTVaL campaign data. As it is provided via the SAMD archive, SAMD archive data policy is applicable. https://www.fdr.uni-hamburg.de/record/9891 10.25592/uhhfdm.9891 oai:fdr.uni-hamburg.de:9891 doi:10.25592/uhhfdm.9890 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode SAMD FESSTVAL Data Policy for FESSTVaL campaign data info:eu-repo/semantics/other publication-other

oai:fdr.uni-hamburg.de:10090 2023-01-25T09:34:29Z user-icdc user-cliccs user-cen user-uhh

Burgemeister, Finn Clemens, Marco Ament, Felix 2022-03-08 This data set contains rainfall rates estimated from X-band radar (WRX) observations during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) from June to August 2021. This single-polarized, local area weather radar was located at the boundary layer field site of the German Weather Service (Deutscher Wetterdienst - DWD) at Falkenberg. The radar operated at an elevation angle of 2.3° with a high temporal 30 s, range 60 m, and sampling 1° resolution refining observations of the German nationwide C-band radars within a 20 km scan radius. The high-resolution rainfall rates are available as hourly NetCDF-files. Quality: Several sources of radar-based errors were adjusted gradually affecting the precipitation estimate, e.g. the radar calibration, alignment, attenuation, noise, non-meteorological echoes. However, a few azimuth angles of the measurements are affected by beam blockage. The WRX’s reflectivity values are calibrated and the derived rainfall rates are evaluated to measurements of a micro rain radar (MRR) located at the Lindenberg Meteorological Observatory – Richard Assmann Observatory (MOL-RAO) of the German Weather Service (DWD). The rainfall rates of the WRX and MRR are in good agreement. Instruments: local area weather radar / X-band weather radar FLK Funding: partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany‘s Excellence Strategy – EXC 2037 'CLICCS - Climate, Climatic Change, and Society' – Project Number: 390683824, contribution to the Center for Earth System Research and Sustainability (CEN) of Universität Hamburg; partly funded by the Hans Ertel Center for Weather Research within the project ‘Advancing the Representation of Convection across Scales (ARCS) - FKZ 4818DWDP1B. https://www.fdr.uni-hamburg.de/record/10090 10.25592/uhhfdm.10090 oai:fdr.uni-hamburg.de:10090 eng doi:10.25592/uhhfdm.10089 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc/1.0/legalcode precipitation rainfall rain high-resolution radar X-band observations measurement FESSTVaL SAMD Rainfall rates estimated from X-Band radar observations during FESSTVaL 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10416 2023-01-25T09:15:50Z user-icdc user-cen

Jahnke-Bornemann, Annika 2022-08-18 The SAMD light data-product description-document includes the conventions for file names, variables and NetCDF-files. The standardized XML-file convention is included as well as all necessary abbreviations for institutes, instruments, variables, etc. https://www.fdr.uni-hamburg.de/record/10416 10.25592/uhhfdm.10416 oai:fdr.uni-hamburg.de:10416 eng doi:10.25592/uhhfdm.9902 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode SAMD FESSTVaL data standard measurement FAIR The SAMD Product Standard (Standardized Atmospheric Measurement Data) info:eu-repo/semantics/technicalDocumentation publication-technicalnote

oai:fdr.uni-hamburg.de:11229 2023-02-02T09:11:58Z user-icdc user-cen user-uhh

Sadikni, Remon Kern, Stefan Jung, Saskia Boventer, Jakob Platis, Andreas Bange, Jens 2023-01-23 Abstract: This data set contains in-situ airborne measurements of atmospheric parameters by unmanned aircraft system (UAS) of type fixed-wing conducted with the multi-purpose airborne sensor carrier "MASC-3" (Rautenberg et al., 2019b). The UAS recorded in-situ meteorological parameters of the three-dimensional wind vector, temperature and humidity at high temporal resolution with a sampling rate of 100 Hz. The data was obtained at the boundary layer-measurement site (Grenzschichtmessfeld (GM)) Falkenberg during two intensive observational periods (IOPs): 07 July - 31 July 2020 and 06 June - 02 July 2021, in the framework of the VALUAS project. The project is funded by the German Meteorological Service (DWD) to validate Doppler wind lidar measurements. The measurements in 2021 took place in parallel to the Field Experiment on submesoscale spatio-temporal variability in Lindenberg (FESSTVAL) campaign. The data set contains 53 flights and has been processed by the Environmental Physics group at the University of Tübingen, Germany. TableOfContents: eastward wind component u; standard deviation of eastward wind component uu; northward wind component v; standard deviation of northward wind component vv; upward air velocity w; standard deviation of upward air velocity ww; wind speed; standard deviation of wind speed; wind direction; standard deviation of wind direction; air pressure; air temperature; relative humidity; turbulent kinetic energy tke; momentum flux mfs Note: For each flight the netCDF file contains the median time, latitude, and longitude and the mean height above ground of the respective legs together with a leg ID and the bounds for latitude and longitude of every leg. Technical Info: dimension: total number of legs (varies between flights); temporalExtent_startDate: 2020-07-07 09:50:18; temporalExtent_endDate: 2021-07-02 18:18:03; temporalResolution: none (depends on flight duration per leg i.e. variable time_bnds); temporalResolutionUnit: none; spatialResolution: none (depends on leg length i.e. variables lat_bnds and lon_bnds); spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none (depends on leg height above ground i.e. variable zsl); verticalResolutionUnit: none; verticalStart: 90; verticalStartUnit: meters; verticalEnd: 580; verticalEndUnit: meters; instrumentName: multi-purpose airborne sensor carrier MASC-3; instrumentType: five-hole probe, fine wire platinum resistance thermometer; instrumentLocation: Fixed-wing unmanned aircraft system; instrumentProvider: University of Tübingen, Germany Methods: For measuring the three-dimensional wind vector MASC-3 uses a five-hole probe (Rautenberg et al., 2019a; Wildmann et al., 2014), for temperature a fine wire platinum resistance thermometer (Wildmann et al., 2013), and for positional measurements an INS with a GPS sensor. The MASC-3 can resolve turbulence down to an eddy size of about 1 m (Rautenberg et al. 2019b). The overall atmospheric conditions during the campaign were typical summer-time atmospheric boundary layers with strong convection. The overall duration of a flight is 1 – 1.5 h in an altitude band of 90 m - 580 m above ground. Each measurement flight of the presented data set comprises flight sections, which are straight and leveled (called legs), with a length of approx. 2.5 km. Flights took place only in good weather conditions (visual contact with the aircraft, no flying in rain, thunderstorms, above clouds or at night). The maximum measurable wind speed is approx. 12 - 15 m/s. All flights have been quality controlled and filtered for systematic errors. The uncertainties of the measurement principle are described in detail in van den Kroonenberg et al. (2008). The uncertainty of absolute wind measurements was validated to below 0.4 m/s. Only straight and level flight sections (legs) are used during post-processing. The median values (u-, v-, and w-component of the wind), mean values (all other variables except time, latitude and longitude) and standard deviations are calculated for each variable, resulting in the data set archived here, i.e. all values are median or mean values of the specific flight legs. All meteorological equations are based on the Python package “PARMESAN” (https://tue-umphy.gitlab.io/software/parmesan/) Units: m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; degrees; degrees; Pa; K; 1; m²/s²; N/m² geoLocations: westBoundLongitude: 14.1024 degrees East; eastBoundLongitude: 14.1401 degrees East; southBoundLatitude: 52.1539 degrees North; northBoundLatitude: 52.1780 degrees North; Germany, UTM zone 33U Size: Data are organized per flight leg. Data from all flights of individual years (here: 2020: 25 flights and 2021: 28 flights) are packed into separate tar archives = 2 archives. Total size: < 200 KByte Format: netCDF DataSources: Fixed-wing unmanned aircraft system (UAS) based in-situ airborne measurements using the multi-purpose airborne sensor carrier MASC-3 Contact: saskia.jung (at) uni-tuebingen.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/fval-ekut-masc3-l2-wind.html , see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html https://www.fdr.uni-hamburg.de/record/11229 10.25592/uhhfdm.11229 oai:fdr.uni-hamburg.de:11229 eng doi:10.25592/uhhfdm.9824 doi:10.25592/uhhfdm.9902 doi:10.3390/s19102292 doi:10.3390/atmos10030124 doi:10.5194/amt-7-1027-2014 doi:10.5194/amt-6-2101-2013 doi:10.25592/uhhfdm.11228 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Fixed-wing UAS Measurements Wind speed Wind direction Turbulence Kinetic energy VALUAS FESSTVAL SAMD Fixed-wing UAS wind and turbulence measurements at GM Falkenberg during FESSTVAL 2020 and 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10559 2024-02-26T10:16:26Z user-icdc user-uhh

Päschke, Eileen 2022-09-05 This data set contains profiles of estimates for wind and turbulence variables derived from Doppler lidar measurements at the GM Falkenberg boundary layer field site during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) during the period May 18, 2021, and August 31, 2021 The GM Falkenberg as part of the Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory supersite is operated by the German national meteorological service (Deutscher Wetterdienst, DWD). The product variables are based on a measurement and retrieval approach outlined in Smalikho et. al (2017, DOI:10.5194/amt-2017-140). The measurement approach is based on a conically Doppler lidar (DL) scanning strategy with high spatio-temporal resolution (azimuth resolution of approx. ~1.3 deg; duration of one full scan ~ 72s) and a constant zenith angle of 54.7 deg. The realization of such a scanning strategy was possible via the continuous scan mode option of the DL system with 2000 accumulated pulses per beam. The retrieval approach outlined in Smalikho et. al (2017) allows for a simultaneous derivation of mean wind profiles and a consistent set of turbulence variables, namely the profiles of turbulence kinetic energy (TKE), turbulent energy dissipation rate (EDR), integral scale of turbulence (LV) and momentum fluxes (e.g. < u‘w ‘> ). The TKE retrieval includes additional correction terms with the following purposes: (a) to compensate the typical underestimation of the DL derived TKE by unresolved small-scale wind fluctuations in the measured radial velocity due to the averaging over the DL pulse volume and (b) to reduce the retrieval error due to random errors in the derived radial velocity. Note that in Smalikho et. al (2017) the primary focus is on turbulence. The scanning strategy, however, is also useful to simultaneously retrieve the mean wind. Here, the FSWF (filtered.sine-wave-fit) approach as outlined in Smalikho et. al (2003, https://doi.org/10.1175/1520-0426(2003)020<0276:TOWVEF>2.0.CO;2) has been used. Two subsets of data are provided: The Level-1 data set includes both the instantaneous DL measurements and related values (e.g. radial velocity and signal-to-noise ratio as function of time, range gate, azimuth) and relevant information on the system’s specific parameters which are either fixed by the manufacturer (e.g. wavelength, pulse repetition frequency, pulse length) or can be configured by the user (e.g. range gate length, number of pulse accumulation, focus). Level-2 data represent 30-min averages of the derived mean wind vector and turbulence variables, respectively. Furthermore, additional quality flags for the derived products are provided. All data are organized in daily files. The original measurements cover the lowermost 500m above ground level. However, depending on the signal quality and the results of the product’s quality assurance, the availability of reliable data can be limited to lower heights. Data Set Quality The success of the retrieval approach by Smalikho et. al (2017) strongly depends on the quality of the estimates for the Doppler velocity. During a routine application with a naturally varying density of backscattering targets in the atmosphere the number of pulse accumulations (Npa = 2000) was not always high enough for reliable Doppler velocity estimates (“good” estimates) and the occurrence of non-reliable “bad” estimates (outlier) was comparatively high from time to time. Such outlier contain no wind information (Stephan et al., 2018, doi: 10.1117/12.2504468) and if not excluded from the measured data set they may contribute to large errors in the retrieved meteorological variables (Dabas, 1999, https://doi.org/10.1175/1520-0426(1999)016<0019:SMFTRO>2.0.CO;2). For that reason prior to product retrieval a careful pre- filtering of the Doppler velocity measurements was necessary to exclude such “bad” estimates from the Level-1 data set. The wind and turbulence variables stored in the Level-2 data set are the direct result of the retrieval approach. To distinguish between reliable and non-reliable turbulence products, additional quality flags (turb_flag_a, turb_flag_b, cov_flag, wind_flag) are provided in the Level-2 data set (where 0 = bad and 1 = good). These flags are the results of a number of different tests which proof whether the assumptions made for the retrieval were fulfilled or not. Further details concerning their meaning and how they should be applied are given by the corresponding variable name attributes in the NetCDF files. The retrieval algorithm has been validated through inter-comparison of the lidar-based wind and turbulence kinetic energy (TKE) values versus data from sonic measurements at 90 m height on the tower at GM Falkenberg. TKE products declared as reliable based on turb_flag_b (turb_flag_a) show a low systematic overestimation of 2.4% (0.7%) with a high variability of differences over the whole value range with possible overestimation of 41.1% (29%) and underestimation of -36.3% (-27.5%). Here, the availability of turb_flag_a proven TKE products was with about 37% much less than turb_flag_b proven TKE products with about 75% data availability. Variables: wind speed, wind_from_direction, turbulence kinetic energy, turbulent eddy dissipation rate, u and v component of wind vector, covariance uw and vw https://www.fdr.uni-hamburg.de/record/10559 10.25592/uhhfdm.10559 oai:fdr.uni-hamburg.de:10559 eng doi:10.25592/uhhfdm.10558 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode atmosphere measurement FESSTVaL Lidar wind turbulence Germany (Deutschland) FESSTVaL Falkenberg Doppler lidar 30 minutes mean wind and turbulence profiles info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:14269 2024-05-20T10:35:34Z user-icdc user-cen user-uhh

Kern, Stefan Sadikni, Remon Becker, Claudia Beyrich, Frank Samtleben, Nadja Rummel, Udo 2024-05-17 Abstract: This data set contains time series of surface pressure (unnormalized) measured (at station level + 28 m) with a Lambrecht RPT410V pressure sensor [Basic meteorological data] precipitation sum measured (at station level + 1 m) with an Lambrecht weighing tipping bucket.[Basic meteorological data] surface radiation flux densities (down-/upward, short-/longwave) and of the radiative surface temperature (at 26 m and 29 m) [Radiation data] air temperature, relative humidity, wind speed, and wind direction at various levels between 2.2 m and 30.6 m at the 30m-mast. [Tower data] mean values and variances of the wind components (u, v, w), sonic temperature, humidity and of the turbulent fluxes of momentum, sensible heat, and latent heat at 30.3 m height above ground [Turbulence data] All these data were measured at Kehrigk forest station (being part of the supersite Lindenberg) above a pine forest canopy during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) from May to August 2021. The Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory supersite is operated by the German national meteorological service (Deutscher Wetterdienst, DWD). See also: Beyrich, F., W. Adam, 2007: Site and Data Report for the Lindenberg Reference Site in CEOP – Phase I. Offenbach a.M. - Selbstverlag des Deutschen Wetterdienstes: Berichte des Deutschen Wetterdienstes. Nr. 230, 55 pp. (ISSN 0072-4130) Turbulence data are level-2 data as 30-minute statistics based on 20 Hz sampling organized in daily files. All other data are level-1 data as 1-minute averages (sums) based on 1 Hz sampling organized in daily files. TableOfContents: Basic Meteorological Data: rainfall amount; rainfall amount quality flag; air pressure; air pressure quality flag Radiation Data: surface downwelling shortwave fllux; surface downwelling shortwave flux quality flag; surface upwelling shortwave flux; surface upwelling shortwave flux quality flag; surface downwelling longwave flux; surface downwelling longwave flux quality flag; surface upwelling longwave flux; surface upwelling longwave flux quality flag; surface temperature; surface temperature quality flag Tower Data: air temperature; air temperature quality flag; relative humidity; relative humidity quality flag; wind speed; wind speed quality flag; wind direction; wind direction quality flag Turbulence Data: eastward wind u; northward wind v; upward air velocity w; standard deviation of u; standard deviation of v; standard deviation of w; sonic temperature; standard deviation of sonic temperature; wind speed; wind direction; friction velocity; friction velocity quality flag; upward momentum flux; tubulent kinetic energy; surface upward sensible heat flux; surface upward sensible heat flux quality flag; absolute humidity; surface upward water vapor flux; surface upward latent heat flux; surface upward latent heat flux quality flag; signal strength Technical Info: Basic Meteorological Data: dimension: 144 x 1; temporalExtent_startDate: 2021-05-01 00:01:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 1; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: rain[e] H3 [precipitation], RPT410V [pressure]; instrumentType: weighing tipping bucket [precipitation], pressure sensor [pressure]; instrumentLocation: both Forst Kehrigk site (station level + 1 m [precipitation], + 28 m [pressure]); instrumentProvider: Lambrecht meteo GmbH [precipitation], Lambrecht meteo GmbH [pressure] Radiation Data: dimension: 144 x 1; temporalExtent_startDate: 2021-05-01 00:01:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 1; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: CM24 (2 x CM21)[shortwave], DDPIR [longwave], KT15.82D [surface temperature]; instrumentType: precision pyranometer [shortwave], precision infrared radiometer [longwave], radiation pyrometer [surface temperature]; instrumentLocation: all Forst Kehrigk measurement site (26 m & 29 m); instrumentProvider: Kipp&Zonen B.V. [shortwave], Eppley Lab Inc [longwave], Heitronics GmbH [surface temperature] Tower Data: dimension: 144 x 8 [air temperature & humidity], 144 x 9 [wind speed], 144 x 1 [wind direction]; temporalExtent_startDate: 2021-05-01 00:01:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 1; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: variable; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; verticalStart: 2.2 [all quantities except wind direction], 30.6 [wind direction]; verticalStartUnit: meters; verticalEnd: 28.3 [air temperature & humidity], 30.6 [wind speed & direction]; verticalEndUnit: meters; instrumentNames: HMP45D [air temperature, humidity], F460 [wind speed], model 05103 [wind direction]; instrumentType: Psychrometer [air temperature, humidity], Cup anemometer [wind speed], Wind monitor [wind direction]; instrumentLocation: all Forst Kehrigk measurement site; instrumentProvider: Vaisala Oy [air temperature, humidity], Climatronics Corp. [wind speed], R.M. Young Company [wind direction] Turbulence Data: dimension: 48 x 1 [turb03: 30.3 m]; temporalExtent_startDate: 2021-05-01 00:30:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 30; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: variable; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; verticalStart: 30.3; verticalStartUnit: meters; verticalEnd: 30.3; verticalEndUnit: meters; instrumentNames: USA-1 [wind and temperature fluctuations], LI-7500RS [water vapor fluctuations] ; instrumentType: Sonic anemometer [wind and temperature fluctuations], Infrared gas analyser [water vapor fluctuations]; instrumentLocation: all boundary layer field site (GM) Falkenberg; instrumentProvider: Metek GmbH [wind and temperature fluctuations], LiCor Inc. [water vapor fluctuations] Methods: Basic Meteorological Data: Air pressure sensor accuracy is specified by the manufacturer with 0.5 hPa at 20 °C. Quality control includes range tests and an intercomparison versus air pressure measurements at neighbouring sites. Precipitation sensor accuracy is specified by the manufacturer with 0.1 mm or 1 % for precipitation rates < 6 mm / min. Quality control includes a comparison with precipitation measurements at a neighbouring site (and the use of radar images if applicable). Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. Radiation Data: Radiation flux sensors are operated in ventilated shields. They are mounted above the forest canopy at 29 m (pyranometers, pyrgeometers) and 26 m (infrared thermometer) above ground, respectively. Mean canopy height was 19 m. The uncertainty is estimated from internal comparisons at ± 5 W/m2 (or 1.5 % - whichever is larger) for shortwave components. The longwave uncertainty is less than ± 5 W/m2. The radiation sensors operated at the forest station are regularly changed and compared versus reference sensors directly traceable to the World Radiometric Reference (WRR) and the World Infrared Standard Group (WISG) for shortwave and longwave radiation, respectively. For the infrared thermometer, sensor accuracy is specified by the manufacturer with 0.5 K. No quality control has been applied to the level-1 data. Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. Tower Data: This data set contains time series of air temperature, relative humidity and wind speed measured at Kehrigk forest station at various levels between 2.2 m and 28.3 m at a 30m-mast. The accuracies of the sensors are specified by the manufacturers as follows: Air temperature: 1/3 DIN IEC 751 Class B // Relative humidity: 2 % (3 % above 90 % relative humidity) // Wind speed: 0.07 m/s or 1 % whichever is greater. Air temperature and relative humidity are measured in actively aspirated radiation shields of type 43408 (R.M. Young Company). An offset correction is applied to the HMP45D air temperature data based on a regular inter-comparison of the HMP45D temperature measurements against psychrometer temperature measurements during night-time. This offset correction typically is in the range 0.05 - 0.20 K, it has been found to be almost constant in time (variations of less than 0.05 K). Air temperature and relative humidity were measured simultaneously by HMP45D and by an aspirated Frankenberger psychrometer (Th. Friedrichs GmbH). A correction for the HMP45D was derived by minimising the rmsd when compared to the psychrometer data. The coefficients of the non-linear (polynomial) regression model for the FESSTVaL period were based on parallel measurements in April 2021 and in July 2021. Relative humidity values > 100% are set equal to 100%. Due to the construction of the mast there are flow distortion effects on the wind speed measurements for winds from the sector between 35 degrees and 85 degrees. Wind speed values smaller than 0.13 m/s have to be interpreted as calm. Quality control includes a regular comparison of the air temperature and relative humidity differences of the HMP45D vs. the psychrometer measurements. No further quality control is applied to this level-1 data. Each measured value is accompanied by a quality where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. Turbulence Data: This data set contains time series of mean values and variances of the wind components (u, v, w), sonic temperature, humidity and of the turbulent fluxes of momentum, sensible heat, and latent heat measured at Kehrigk forest station on the top of a 30m lattice mast above a pine canopy.The accuracies of the sensors forming an eddy-covariance (EC) system are specified by the manufacturers as follows: wind vector components: 0.075 ms-1 or 1.5 % of reading // absolute humidity: <1 % of reading. The EC system at a measuring height of 30.3 m is mounted on top of a lattice mast. Measurements of the sonic are disturbed by lee effects from other sensors (e.g., the LI7500 RS) for wind directions around NNE, i.e., between 355 degrees and 60 degrees. The raw data from the sonic and from the infrared gas analyser (IRGA) based on 20 Hz sampling were processed using the EddyPro V7.0.9 software package provided by LiCor Inc. The following settings were applied: Double rotation of the sonic co-ordinate system acc. to Wilczak et al. (2001, Boundary-Layer Meteorol. 99, 127-150) Despiking and raw data statistical screening (excluding the test for angle of attack and steadiness of horizontal wind) acc. to Vickers and Mahrt (1997, J. Atmos. Ocean. Technol. 14, 512-526) Band pass spectral correction acc. to Moncrieff et al. (1997, J. Hydrol, 188-189, 589-611; 2004, in: Handbook of micrometeorology: a guide for surface flux measurements, eds. Lee, X., W. J. Massman and B. E. Law. Dordrecht: Kluwer Academic, 7-31) Buoyancy and crosswind correction acc. to Schotanus et al. (1983, Boundary-Layer Meteorol. 26, 81-93) Compensation of density fluctuations acc. to Webb et al. (1980, Quart. J. Roy. Meteorol. Soc. 106, 85-100) Quality control of the fluxes includes the stationarity and integral-turbulence-characteristics tests acc. to Mauder et al. (2013, Agric. Forest Meteorol. 169, 122-135), which are implemented in the EddyPro software. These tests are complemented by (i) climatologically-based value range tests, (ii) comparison of net radiation vs. energy fluxes, (iii) validation of ratio of wind speed and friction velocity, and (iv) tests to evaluate the sign of the measured fluxes using gradient measurements (finite differences of the mean variables) provided that both the gradients and the fluxes are not too small. The LI7500 RS signal strength (variable 19) may serve as an additional quality indicator of the IRGA measurement. To validate the quality of the sonic measurements, precipitation measurements are taken into account. All EC system measured quantities such as the wind speed, humidity and temperature are compared to other operational measuring systems in the surrounding area. Each derived flux value is accompanied by a quality flag where 0 = data value missing, 1 = bad quality, 2 = dubious quality, 3 = good quality. Units: (see TableOfContents) Basic Meteorological Data: kg m-2;1;pa;1 Radiation Data: W m-2;1;W m-2;1;W m-2;1;W m-2;1;K;1 Tower Data: K;1;1;1;m s-1;1;degrees;1 Turbulence Data: m s-1;m s-1;m s-1;m s-1;m s-1;m s-1;K;K;m s-1;degrees;m s-1;1;N m-2;J kg-1;W m-2;1;kg m-3;kg m-2s-1;W m-2;1;1 geoLocations: BoundingBox: westBoundLongitude: 14.1221 degrees East; eastBoundLongitude: 14.1223 degrees East; southBoundLatidude: 52.1664 degrees North; northBoundLatitude: 52.1666 degrees North; geoLocationPlace: Germany, UTM zone 33U Locations: Basic Meteorological Data: 52.1817 °N, 13.9525 °E, 49 m above mean sea level, 28.0 m above ground Radiation Data: 52.1817 °N, 13.9525 °E, 49 m above mean sea level, 26.0 m to 29.0 m above ground Tower Data: 52.1817 °N, 13.9525 °E, 49 m above mean sea level, 2.2 m to 30.6 m above ground Turbulence Data: 52.1817 °N, 13.9525 °E, 49 m above mean sea level, 30.3 m above ground Size: Data (mostly level 1, Turbulence Data level 2) are packed into compressed tar-archives. Their sizes range between 1.4 Mbyte and 19.4 Mbyte. Format: netCDF DataSources: Single site ground-based instrument measurements, see "Technical Info" for instruments Contact: claudia.becker (at) dwd.de; nadja.samtleben (at) dwd.de; frank.beyrich (at) dwd.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-forstkehrigk.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html https://www.fdr.uni-hamburg.de/record/14269 10.25592/uhhfdm.14269 oai:fdr.uni-hamburg.de:14269 eng doi:10.25592/uhhfdm.9902 doi:10.25592/uhhfdm.9824 url:https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html url:https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval.html url:https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-forstkehrigk.html doi:10.25592/uhhfdm.14268 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Atmosphere Measurements Temperature Humidity Precipitation Sum Air Pressure Wind Speed Wind Direction Boundary Layer Measurement Tower Longwave Radiation Shortwave Radiation Turbulent Fluxes Eddy Covariance FESSTVal SAMD Standard Meteorology at surface and different heights, Turbulent fluxes, Radiation fluxes (2021) from FESSTVaL in Forst Kehrigk, Germany info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:14449 2024-06-27T09:46:37Z user-icdc user-cliccs user-cen user-uhh

Sadikni, Remon Kern, Stefan 2024-06-27 Abstract: MODIS Collection 6.1 8-day gap-filled Gross Primary Production (GPP) and Net Photosynthesis data on the MODIS sinusoidal grid are taken from the netCDF files produced at ICDC, for which the bit-encoded quality information given in the HDF-files was already decoded, and re-gridded to build a global map of grid-cell mean GPP and net photosynthesis and their variances on a global equirectangular climate modeling grid (CMG). Only those GPP or net photosynthesis values are used where i) the cloud flag indicates either clear sky or assumed clear sky, where the MODLAND quality is good and where the confidence flag suggests best quality or good quality data. The confidence flag is provided as a grid-cell mean rounded value with fractions of the five original flags being provided for convenience. Cloud conditions are included in form of the primary cloud flag and the fraction this primary cloud flag occupies among the valid 500 m sinusoidal grid grid cells. Two separate layers of the number of valid grid cells of the 500 m sinusoidal grid are given, one is for the geophysical data and one is for the flags. TableOfContents: grid cell mean Gross_Primary_Production (GPP); grid cell mean Net Photosynthesis; GPP standard deviation over grid cell; Net Photosynthesis standard deviation over grid cell; number of used GPP or net photosynthesis values per grid cell; number of used confidence and quality flag values per grid cell; grid cell mean confidence flag; fraction of confidence flag 0 in grid cell; fraction of confidence flag 1 in grid cell; fraction of confidence flag 2 in grid cell; fraction of confidence flag 3 in grid cell; fraction of confidence flag 4 in grid cell; primary cloud flag; primary cloud flag fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2023-12-31; temporalResolution: 8-daily; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] Running, S. W., and M. Zhao, Users Guide Daily GPP and Annual NPP (MOD17A2H/A3H) and Year-end Gap-Filled (MOD17A2HGF/A3HGF) Products NASA Earth Observing System MODIS Land Algorithm, (For Collection 6), Version 4.0, January 2, 2019; [2] Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto, A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547-560, 2004; [3] Running, S. W., A measurable planetary boundary layer for the biosphere. Science, 337(6101), 1458-1459, 2012; [4] Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running, Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95(2), 164-176, 2005 Units: Units for all variables (see TableOfContents): kg C m-2; kg C m-2; kg C m-2; kg C m-2; 1; 1; 1; percent; percent; percent; percent; percent; 1; percent geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: (files are packed into one zip-archive per year) 2001-2021 and 2023: 46 files per year, each approximately 12975000 bytes 2000: 40 files of same size Data of the year 2022 are not published because data provided by LPDAAC seemed to be not reliable Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD17A2HGF.061 (last accessed: 2024-05-07), see also https://lpdaac.usgs.gov/products/mod17a2hgfv061/ (last accessed: 2024-05-07) Data on sinusoidal grid tiles in netCDF format: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-05-10) or https://doi.org/10.25592/uhhfdm.14463 (last accessed: 2024-06-25). Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-05-10) https://www.fdr.uni-hamburg.de/record/14449 10.25592/uhhfdm.14449 oai:fdr.uni-hamburg.de:14449 eng doi:10.25592/uhhfdm.14463 url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html url:https://lpdaac.usgs.gov/products/mod17a2hgfv061/ doi:10.25592/uhhfdm.8556 doi:10.25592/uhhfdm.8555 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Gross Primary Production Net Photosynthesis Global maps 8-daily Satellite Remote Sensing MODIS EOS-Terra NTSG UMT ICDC MODIS Collection 6.1 global 8-daily gap-filled Gross Primary Production info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:14633 2024-07-10T21:12:19Z user-icdc user-cen user-uhh

Sadikni, Remon Kern, Stefan 2024-07-10 Abstract: MODIS Collection 6.1 yearly gap-filled Gross Primary Production (GPP) and Net Primary Production (NPP) data on the MODIS sinusoidal grid provided by LPDAAC: https://doi.org/10.5067/MODIS/MOD17A3HGF.061 (last accessed: 2024-07-09) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the NPP data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html). For each tile, latitude and longitude information of the center of each 500 m x 500 m pixel is provided in a separate netCDF file. TableOfContents: gross_primary_production (gpp); net_primary_production (npp); npp_quality_flag; npp_confidence_flag Technical Info: dimension: 2400 columns x 2400 rows x unlimited; temporalExtent_startDate: 2001-01-01; temporalExtent_endDate: 2023-12-31; temporalResolution: yearly; spatialResolution: 500; spatialResolutionUnit: meter; horizontalResolutionXdirection: 500; horizontalResolutionXdirectionUnit: meter; horizontalResolutionYdirection: 500; horizontalResolutionYdirectionUnit: meter; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] Running, S. W., and M. Zhao, Users Guide Daily GPP and Annual NPP (MOD17A2H/A3H) and Year-end Gap-Filled (MOD17A2HGF/A3HGF) Products NASA Earth Observing System MODIS Land Algorithm, (For Collection 6), Version 4.0, January 2, 2019; [2] Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto, A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547-560, 2004; [3] Running, S. W., A measurable planetary boundary layer for the biosphere. Science, 337(6101), 1458-1459, 2012; [4] Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running, Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95(2), 164-176, 2005 Units: kg C m-2; kg C m-2;1; 1 geoLocations: westBoundLongitude: depends on tile; eastBoundLongitude: depends on tile; southBoundLatitude: depends on tile; northBoundLatitude: depends on tile; geoLocationPlace: global on land, see: https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Size: (files are packed into one zip-archive per year) 2001-2023: each zip file is about 2.1 Gbyte large; each single file approximately 34.5 Mbytes, for the number of tiles see https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD17A3HGF.061 (last accessed: 2024-06-03), see also https://lpdaac.usgs.gov/products/mod17a3hgfv061/ (last accessed: 2024-06-03) Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-07-08) https://www.fdr.uni-hamburg.de/record/14633 10.25592/uhhfdm.14633 oai:fdr.uni-hamburg.de:14633 eng doi:10.5067/MODIS/MOD17A3HGF.061 url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html url:https://lpdaac.usgs.gov/products/mod17a3hgfv061/ doi:10.25592/uhhfdm.14635 doi:10.25592/uhhfdm.14632 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Gross Primary Production Net Primary Production Tiles Yearly Satellite Remote Sensing MODIS EOS-Terra NTSG UMT ICDC MODIS Collection 6.1 Sinusoidal Tiles Yearly gap-filled Gross and Net Primary Production info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:13103 2024-12-12T13:07:24Z user-cliccs user-icdc user-cen user-uhh

Sadikni, Remon Kern, Stefan 2023-08-07 Abstract: The globally gridded daily 5-day running mean surface soil moisture product derived at ICDC (https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html , https://doi.org/10.25592/uhhfdm.13101) from soil moisture time series data of the extension of the EUMETSAT H-SAF product H119: H120 based on MetOp-B, and -C ASCAT data, processing version v7 (https://doi.org/10.15570/EUM_SAF_H_0009), are averaged to obtain monthly means of the surface soil moisture (SM) distribution separately for ascending and descending overpasses. The monthly mean SM values include the nominally computed SM values as well as those SM values which were negative (down to -25%, correction flag = 1) or larger than 100% (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. The threshold for the monthly average is (the number of days per Month) 10. If there are fewer values per month, the value is set to the missing_value. For more information see the respective global attribute in the netCDF file. TableOfContents: mean soil moisture extended; mean soil moisture extended noise; number of valid soil moisture extended values per month; mean number of overpasses per grid cell; mean historic probability of snow cover; mean historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD; soil moisture status flag Technical Info: dimensions: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2022-01-01; temporalExtent_endDate: 2022-12-31; temporalResolution: monthly; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.11225; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-B, MetOp-C); instrumentProvider: EUMETSAT, ESA Methods: For a description of the methods used to obtain the daily 5-day running mean / composite data on which these monthly data are based, we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi: 10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.2, 2022; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.1, 2021; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v1.1, 2022. Units: Units for all variables (see TableOfContents): percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3, 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: 24 files per year [12 for ascending, 12 for descending overpasses]; ~56.439 MegaByte per file; ~1.3228 GigaByte in total (data are packed into two zip-archives per year, one for the ascending, one for the descending data) Format: netCDF DataSources: Gridded daily 5-day running mean surface soil moisture maps: https://doi.org/10.25592/uhhfdm.13101; see also https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html Original time-series of the surface soil moisture: https://hsaf.meteoam.it/Products/Detail?prod=H120 Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/13103 10.25592/uhhfdm.13103 oai:fdr.uni-hamburg.de:13103 eng doi:10.25592/uhhfdm.13101 url:http://hsaf.meteoam.it url:https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html doi:10.25592/uhhfdm.10468 doi:10.25592/uhhfdm.13102 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Monthly Satellite Remote Sensing ASCAT MetOp-B/C EUMETSAT HSAF University of Vienna ICDC ASCAT Global Maps of monthly mean surface soil moisture - extension 2022 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8585 2025-02-03T16:51:30Z user-cliccs user-cen user-icdc user-uhh

Sadikni, Remon Jahnke-Bornemann, Annika Kern, Stefan 2021-02-12 Abstract: Original LAI and FAPAR data (see https://lpdaac.usgs.gov/products/mod15a2hv006/) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the LAI and FAPAR data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://icdc.cen.uni-hamburg.de/en/modis-lai-fpar.html). These are subsequently read and re-gridded onto an equi-rectangular climate modeling grid (CMG). Only those LAI and FAPAR values are used where i) the cloud flag indicates a maximum of two cloud influences, and where ii) cloud cover is clearly defined, i.e. "assumed clear sky" is not used. Flag layers are summarized such that there is gridded information about 1) cloud fraction, 2) fraction of average and high aerosol load, 3) primary and secondary land-cover type, and 4) primary and secondary quality flag. Primary and secondary refer to the highest and 2nd-highest pixel count of the respective type or flag within the grid cell. Note that the count of valid values differs for the grid cell mean LAI and FAPAR and their variance, and for the grid-cell mean retrieval standard deviation. TableOfContents: Grid cell mean FAPAR; Grid cell mean LAI; FAPAR variance across grid cell; LAI variance across grid cell; Grid cell mean FAPAR retrieval standard deviation; Grid cel mean LAI retrieval standard deviation; Count of useful FAPAR or LAI values per grid cell; Count of useful FAPAR or LAI retrieval standard deviation values in grid cell; Primary quality flag; Secondary quality flag; Primary land cover; Secondary land cover; Grid cell cloud fraction; Grid cell aerosol fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2020-12-31; temporalResolution: 8-daily; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] MODIS collection 6 (C6) LAI/FPAR Product User Guide, 24-February-2015, https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/mod15_user_guide.pdf; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document (ATBD), v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf; [3] Yang, et al., From validation to algorithm improvement. Trans. Geosci. Rem. Sens., 44, 1885-1898, 2006; [4] Morisette, et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, Trans. Geosci. Rem. Sens., 44, 1804-1817, 2006; [5] Garrigues, et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008; [6] https://icdc.cen.uni-hamburg.de/en/modis-lai-fpar.html Units: Units for all variables (see TableOfContents): percent, m2/m2, percent, m4/m4, percent, m2/m2, 1, 1, 1, 1, 1, 1, percent, percent geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: (files are packed into one zip-file per year) 2000: 40 files, 10901824 byte / file 2001-2018: 46 files / year, 10901824 byte / file 2019: 46 files, 10901856 byte / file 2020: 46 files, 10902528 byte / file Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD15A2H.006 [last access: 2021-01-12], see also: https://lpdaac.usgs.gov/products/mod15a2hv006/ [last access: 2021-01-26] Data on sinusoidal grid tiles in netCDF-format: https://icdc.cen.uni-hamburg.de/en/modis-lai-fpar.html [last access: 2021-01-26] Contact: stefan.kern (at) uni-hamburg.de Web page: https://icdc.cen.uni-hamburg.de/en/modis-lai-fpar.html [last access: 2021-01-27] https://www.fdr.uni-hamburg.de/record/8585 10.25592/uhhfdm.8585 oai:fdr.uni-hamburg.de:8585 eng doi:10.5067/MODIS/MOD15A2H.006 url:https://lpdaac.usgs.gov/products/mod15a2hv006/ url:https://icdc.cen.uni-hamburg.de/en/modis-lai-fpar.html doi:10.25592/uhhfdm.8584 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Leaf Area Index LAI FAPAR Global Maps 8-daily Satellite Remote Sensing MODIS EOS-Terra Boston University ICDC MODIS Collection 6 global 8-daily LAI and FAPAR info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:11413 2025-04-14T11:22:30Z user-icdc user-cen user-cliccs user-uhh

Sadikni, Remon Kern, Stefan Böhner, Jürgen Dietrich, Helge Kawohl, Tobias Wehberg, Jan 2023-10-25 Abstract: Climate time series for Germany derived from observations of the German Meteorological Service (Deutscher Wetterdienst / DWD) provided in daily resolution at a grid width of 250 meters for the period from 1961 to 2020 (current status February 2023). The following variables were processed: Daily total global radiation, separately for a horizontal and an inclined plane; daily total precipitation; daily mean, minimum and maximum 2m-air temperature; daily mean water vapor saturation deficit; daily mean wind speed. The temperature data sets are available in two different versions: V5 including a residual correction and V6 without. TableOfContents: Daily total global radiation at horizontal plane (grhds); daily total global radiation at inclined plane (grids); daily total precipitation (rrds); daily mean water vapor saturation deficit (sddm); daily mean 2m-air temperature (tadm); daily minimum 2m-air temperature (tadn); daily maximum 2m-air temperature; daily mean wind speed (wsdm) TechnicalInfo: dimension: 2578 columns x 3476 rows; temporalExtent_startDate: 1961-01-01 00:00:00; temporalExtent_endDate: 2020-12-31 23:59:59; temporalDuration: 60; temporalDurationUnit: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 250; spatialResolutionUnit: m; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: m; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: m; verticalResolution: none; verticalResolutionUnit: none Methods: Spatialization of gridded climate fields is performed, merging Model Output Statistics (MOS) downscaling with surface parameterization techniques (Böhner and Antonic, 2009; Böhner and Bechtel, 2018) to account for terrain-forced fine-scale topoclimatic variations. For a comprehensive description of the methods, see Wehberg and Böhner (2023). A description of the methods used can be found in: Dietrich, H.; Wolf, T.; Kawohl, T.; Wehberg, J.; Kändler, G.; Mette, T.; & Röder, A. & Böhner, J. (2019). Temporal and Spatial High-Resolution Climate Data from 1961 to 2100 for the German National Forest Inventory (NFI). Annals of Forest Science 76, 6. https://doi.org/10.1007/s13595-018-0788-5 Kawohl, T.; Dietrich, H.; Wehberg, J.; Böhner, J.; Wolf, T. & Röder, A. (2017). Das Klima in 80 Jahren – Wein- statt Waldbau? – AFZ-Der Wald 15: 32-35. For GIS-based Terrain-parameterization methods and their application in statistical-dynamical downscaling see, e.g.: Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., & Böhner, J. (2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015. Böhner, J. & Bechtel, B. (2018): GIS in Climatology and Meteorology. – In: Huang, B. [Ed.]: Comprehensive Geographic Information Systems. – Vol. 2, pp. 196–235. Oxford: Elsevier. http://dx.doi.org/10.1016/B978-0-12-409548-9.09633-0. Quality: -- Units: MJ/m2; MJ/m2; mm; hPa; degC; degC; degC; m/s GeoLocation: westBoundCoordinate: 278750; westBoundCoordinateUnit: m; eastBoundCoordinate: 923000; eastBoundCoordinateUnit: m; southBoundCoordinate: 5234000; southBoundCoordinateUnit: m; northBoundCoordinate: 6102750; northBoundCoordinateUnit: m; ProjectCoordinateSystem: Transverse_Mercator; ProjectionCoordinateSystemParameters: [+proj=utm +datum=WGS84 +zone=32 +no_defs]. geoLocationPlace:Germany; UTMZone: 32 Size: Files are first packed into zip-archives and then further grouped together into one tar-archive per variable and 10-year period. The original file size is between about 4 and 7.5 GB per year and variable. The file size of the tar archives ranges between 3 GB and 70 GB. Format: SAGA-Grid (.sgrd), https://saga-gis.sourceforge.io/en/index.html DataSources: DWD Climate Data Center (CDC): Historical daily station observations (temperature, pressure, precipitation,sunshine duration, etc.) for Germany, version v21.3, 2021. Dataset-ID: urn:x-wmo:md:de.dwd.cdc::obsgermany-climate-daily-kl-historical and DWD Climate Data Center (CDC): Historical daily precipitation observations for Germany, version v21.3,2021. Dataset-ID: urn:x-wmo:md:de.dwd.cdc::obsgermany-climate-daily-more_precip-historical. http://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/ Contact: Prof. Dr. Jürgen Böhner, Universität Hamburg, Center for Earth System Research and Sustainability, Institute of Geography, Bundesstraße 55, 20146 Hamburg, juergen.boehner (at) uni-hamburg.de; https://www.geo.uni-hamburg.de/en/geographie/mitarbeiterverzeichnis/boehner.html Webpage: https://www.waldklimafonds.de/ and https://www.lwf.bayern.de/boden-klima/wasserhaushalt/223446/index.php https://www.fdr.uni-hamburg.de/record/11413 10.25592/uhhfdm.11413 oai:fdr.uni-hamburg.de:11413 eng doi:10.1007/s13595-018-0788-5 doi:10.25592/uhhfdm.11412 info:eu-repo/semantics/restrictedAccess Meteorological observations Global radiation 2m-air temperature Precipitation Saturation deficit 10m wind speed minimum temperature maximum temperature Germany Climate Research Downscaling German Meteorological Service Temporal and spatial high-resolution climate data (1961-2020) for the German National Forest Inventory derived from observations info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:9181 2025-09-10T14:11:29Z user-cen user-icdc user-uhh

Bell, Louisa Zeigermann, Luise Meyer, Maybritt Sadikni, Remon Kern, Stefan 2021-05-21 Abstract: In the framework of the European Space Agency (ESA) Climate Change Initiative (CCI) sea ice essential climate variable (ECV) project (SICCI) [Climate Change Initiative Sea_Ice_cci_Project] a suite of Landsat images of both hemispheres were used to evaluate sea-ice concentration (SIC) products based on satellite microwave radiometry. First, surface broadband albedo values were estimated based on channels 2,3,4 (Landsat-5 or -7) or channels 3,4,5 (Landsat-8). Secondly, a supervised classification was employed, classifying the broadband albedo maps into open water, thin ice and thick ice; note that since this classification is based on simple threshold values, the class thin ice may also include a small fraction of bare thicker sea ice; conversely, the class thick ice may also include a small fraction of thin ice covered by frostflowers or a thin but highly reflective snow cover. Thresholds used for the classification are ~0.07 and 0.4 for the open water / thin ice and thin ice / thick ice transition. Resulting maps have been quality checked for artifacts due to cloudy pixels and double scenes. Note that there might a few maps with a slight overlap from two adjacent Landsat images. TableOfContents: surface type flag (0: open water, 1: thin or bare sea ice, 2: thick or snow-covered ice, 127: missing data or clouds) Technical Info: dimensions: nominal: 6166 columns x 6000 rows x unlimited; dimensions actual: variable, depends on how the Landsat scene fits into a rectangular bounding box determined by the minimum and maximum values of latitude and longitude of each scene; temporalExtent_startDate: 2003-04-02; temporalExtent_endDate: 2015-12-24; temporalResolution: ~28 s / image; spatialResolution: 30; spatialResolutionUnit: meters; horizontalResolutionXdirection: 30; horizontalResolutionXdirectionUnit: meters; horizontalResolutionYdirection: 30; horizontalResolutionYdirectionUnit: meters; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Landsat-5: Thematic Mapper (TM), Landsat-7: Enhanced Thematic Mapper (ETM), Landsat-8: Operational Land Imager (OLI); instrumentType: optical sensor; instrumentLocation: Landsat-5, Landsat-7, Landsat-8; instrumentProvider: NASA Methods: [1] http://esa-cci.nersc.no/?q=documents#/Public/Documents from phase 2/D4.1_SICCI_P2_PVIR-SIC_Issue_1.1.pdf; [2] Knap, W. H., Brock, B. W., Oerlemans, J., and Willis, I. C.: Comparison of Landsat TM-derived and ground-based albedos of Haut Glacier d Arolla, Switzerland. Int. J. Rem. Sens., 20(17), 3293-3310, 1999; [3] Koepke, P., Removal of Atmospheric Effects from AVHRR albedos, J. Appl. Meteorol., 28, 1341-1348, 1989; [4] Barsi, J. A., Kenton, L., Kvaran, G., Markham, B. L., and Pedelty, J. A.: The spectral response of the Landsat-8 operational land imager. Rem. Sens., 6(10), 10232-10251, https://doi.org/10.3390/rs61010232, 2014; [5] Chander, G., Markham, B. L., and Barsi, J. A.: Revised Landsat-5 Thematic Mapper Radiometric Calibration. IEEE Geosci. Rem. Sens. Lett., 4(3), 490-494, 2007; [6] Zatko, M. C., and Warren, S. G.: East Antarctic sea ice in spring: spectral albedo of snow, nilas, frost flowers and slush, and light-absorbing impurities in snow. Ann. Glaciol., 56(69), 53-64, https://doi.org/10.3189/2015AoG69A574, 2015. Units: 1 geoLocations: Northern Hemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: 50.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: Northern Hemisphere over water Southern Hemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -80.0 degrees North; northBoundLatitude: -60.0 degrees North; geoLocationPlace: Southern Hemisphere over water Size: (files are packed into one zip-file per year) Northern Hemisphere: 237 files in total (Landsat-5: 133 files in total from: 2003: 15, 2004: 11, 2005: 31, 2006: 16, 2007: 14, 2008: 14, 2009: 25, 2010: 7; Landsat-7: 12 files from 2003; Landsat-8: 92 files in total from: 2013: 11, 2014: 19, 2015: 62); 142.1 Gbyte (zipped: 76.0Gbyte) in total (Landsat-5: 73.4 Gbyte, zipped: 38.8Gbyte, from: 2003: 8.6Gbyte, 2004: 6.3Gbyte, 2005: 17.6Gbyte, 2006: 9.3Gbyte, 2007: 8.0Gbyte, 2008: 8.0Gbyte, 2009: 14.6Gbyte, 2010: 4.1Gbyte; Landsat-7: 7.2 Gbyte, zipped: 3.6 Gbyte; Landsat-8: 61.5 Gbyte, zipped: 33.6Gbyte, from: 2013: 6.8Gbyte, 2014: 12.5Gbyte, 2015: 42.2Gbyte) Southern Hemisphere: 141 files in total from: 2013: 47, 2014: 61, 2015: 33; 83.5 Gbyte (zipped: 49.6Gbyte) in total from: 2013: 27.3Gbyte, 2014: 37.4Gbyte, 2015: 18.8Gbyte Format: netCDF DataSources: https://earthexplorer.usgs.gov/ [last access: 2021-05-21] Contact: stefan.kern (at) uni-hamburg.de https://www.fdr.uni-hamburg.de/record/9181 10.25592/uhhfdm.9181 oai:fdr.uni-hamburg.de:9181 eng url:https://earthexplorer.usgs.gov/ url:http://esa-cci.nersc.no/?q=documents#/Public/Documents from phase 2/D4.1_SICCI_P2_PVIR-SIC_Issue_1.1.pdf doi:10.25592/uhhfdm.17942 doi:10.25592/uhhfdm.9180 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Polar Oceans Sea Ice Supervised Classification Satellite Remote Sensing Landsat-5 TM Landsat-7 ETM Landsat-8 OLI USGS ICDC Landsat surface type over water from supervised classification of surface broadband albedo estimates info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:17396 2025-11-07T16:23:48Z user-cliccs user-cen user-icdc user-uhh

Sadikni, Remon Kern, Stefan Yakubu, Fuseini Böhner, Jürgen Schickhoff, Udo Scholten, Thomas Hasson, Shabeh Ul 2025-05-15 Abstract: This dataset provides globally consistent, bias-corrected climate data at 0.5° spatial resolution, consisting of a set of seven climate variables derived from three General Circulation Models (GCMs) participating in CMIP5 downscaled by 10 CORDEX Regional Climate Model (RCM) simulations and bias-corrected globally for the period 1950/1960–2099. It includes data from three climate change scenarios, namely RCP2.6, RCP4.5 and RCP8.5. The three GCMs are: ICHEC-EC-EARTH, MPI-M-MPI-ESM-LR, NOAA-GFDL-GFDL-ESM2M. Data are originally available as one netCDF file per GCM (3) per variable (7, NOAA-GFDL-GFDL-ESM2M: 5) per run (4, NOAA-GFDL-GFDL-ESM2M: 3). Available here are zip-archives of all netCDF files of one run, i.e. only rcp26 or only rcp45, per GCM (see Size for the overall sum per GCM). TableOfContents: daily mean 2m-air temperature (tas); daily minimum 2m-air temperature (tasmin), daily maximum 2m-air temperature (tasmax); daily sum of precipitation (pr); daily mean surface downwelling longwave radiation (rlds)*; daily mean 10m wind speed (sfcWind)*; daily mean relative humidity (hurs) *: These variables are NOT included in the NOAA-GFDL-GFDL-ESM2M driven data. TechnicalInfo: dimension: 720 columns x 360 rows; temporalExtent_startDate_Historlcal: 1950-01-01 00:00:00; temporalExtent_endDate_Historical: 2019-12-31 23:59:59; temporalDuration_Historical: 70; temporalDurationUnit_Historical: a; temporalExtent_startDate_RCPs: 2020-01-01 00:00:00; temporalExtent_endDate_RCPs: 2099-12-31 23:59:59; temporalDuration_RCPs: 80; temporalDurationUnit_RCPs: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none *) For MPI-M-MPI-ESM-LR: temporalExtent_startDate_Historlcal: 1960-01-01 00:00:00; temporalExtent_endDate_Historical: 2019-12-31 23:59:59; temporalDuration_Historical: 60; Methods: The ISIMIP3BASD v2.5 bias correction method (see Lange [2019; 2021]) was applied to adjust systematic biases using the GSWP3-W5E53 observational dataset. The regional climate models (RCMs) used are: (listed are Institution/working group, RCM Model, Driving GCM): Climate Service Center Germany (GERICS), REMO2009, MPI-ESM-LR Swedish Meteorological and Hydrological Institute (SMHI), RCA4, MPI-ESM-LR Climate Limited-area Modelling Community (CLMcom), CCLM4-8-17-CLM3-5, MPI-ESM-LR Climate Limited-area Modelling Community (CLMcom), CCLM5-0-2, MPI-ESM-LR Universite du Quebec a Montreal, CRCM5, MPI-ESM-LR Swedish Meteorological and Hydrological Institute (SMHI), RCA4, ICHEC-EC-EARTH Climate Limited-area Modelling Community (CLMcom), CCLM4-8-17-CLM3-5, ICHEC-EC-EARTH Climate Limited-area Modelling Community (CLMcom), CCLM5-0-2, ICHEC-EC-EARTH Swedish Meteorological and Hydrological Institute (SMHI), RCA4, NOAA-GFDL-GFDL-ESM2M National Center for Atmospheric Research, WRF, NOAA-GFDL-GFDL-ESM2M The historical runs begin 1950-01-01 (ICHEC-EC-EARTH and NOAA-GFDL-GFDL-ESM2M) or 1960-01-01 (MPI-M-MPI-ESM-LR) and end 2005-12-31. Historical runs are appended by rcp85 runs for years 2006-01-01 to 2019-12-31. All projection runs begin 2020-01-01 and end 2099-12-31. Quality: Not all of the domains have been downscaled by CORDEX RCMs. Therefore, data files for scenario rcp26 only contain 7 CORDEX domains; all other files contain 8 domains (see also https://cordex.org/domains/cordex-domain-description/) Units: K; K; K; kg m-2 s-1; W m-2; m s-1; percent GeoLocation: westBoundCoordinate: -180.0; westBoundCoordinateUnit: degrees East; eastBoundCoordinate: 180.0; eastBoundCoordinateUnit: degrees East; southBoundCoordinate: -90.0; southBoundCoordinateUnit: degrees North; northBoundCoordinate: 90.0; northBoundCoordinateUnit: degrees North Size: ICHEC-EC-EARTH: 137.7 GByte, MPI-M-MPI-ESM-LR: 130.6 GByte, NOAA-GFDL-GFDL-ESM2M: 55.5 GByte Format: netCDF DataSources: See the file "DataSources_RCM_Table.pdf" Contact: fuseini.yakubu (at) uni-hamburg.de; shabeh.hasson (at) uni-hamburg.de Webpage: https://www.geo.uni-hamburg.de/geographie/abteilungen/physische-geographie/arbeitsgruppen/ag-hareme.html https://www.fdr.uni-hamburg.de/record/17396 10.25592/uhhfdm.17396 oai:fdr.uni-hamburg.de:17396 eng doi:10.5281/zenodo.4686991 doi:10.5194/gmd-12-3055-2019 doi:10.25592/uhhfdm.17395 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Model Downscaling CORDEX Air Temperature Relative Humidity Surface Wind Speed Precipitation Amount Surface Downwelling Longwave Radiation CMIP5 ICDC GloBCORD-HD: Global Bias-Corrected CORDEX Datasets at Half Degree Resolution info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:18163 2025-12-05T09:29:13Z user-cen user-icdc user-cliccs

Thomae, Sarah Rauschenbach, Quentin Doerr, Jakob Notz, Dirk Kern, Stefan 2025-12-05 Abstract: This data set comprises time series of the monthly sea-ice area (SIA) in the Northern Hemisphere (1 file) and in the Southern Hemisphere (1 file). SIA is derived from sea-ice concentration (SIC, also sea-ice area fraction) data of the following products: OSI SAF OSI-450a SIC climate data record / OSI-430a SIC interim climate data record, NOAA-NSIDC CDR, NASA-Team and Comiso-Bootstrap - all three from the NOAA/NSIDC SIC climate data record (version 4.0). The monthly SIA is either directly computed from monthly SIC data or is derived as the mean of all daily SIA values. If required, the observational gap at the pole is interpolated. If required, temporal interpolation is applied - both to fill temporal (up to a maximum of 7 consecutive days) and spatial (if less than 1000 non-contiguous missing grid cells per day) gaps. Table of contents: Northern Hemisphere: Comiso-Bootstrap monthly sea-ice area; NOAA-NSIDC CDR monthly sea-ice area; NASA-Team monthly sea-ice area; OSI SAF monthly sea-ice area Southern Hemisphere: Comiso-Bootstrap monthly sea-ice area; NOAA-NSIDC CDR monthly sea-ice area; NASA-Team monthly sea-ice area; OSI SAF monthly sea-ice area Technical Info: standard_name: sea-ice area; long_name: algorithm_specific hemispheric sea-ice area time-series; dimension: 2100; temporalExtent_startDate: 1850-01-01; temporalExtent_endDate: 2024-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: various SMMR SSM/I SSMIS; instrumentType: various multifrequency_microwave_radiometer; instrumentLocation: various Nimbus-7 DMSP-f8 DMSP-f11 DMSP-f13 DMSP-17; instrumentProvider: various Methods: See description on https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/uhh-sea-ice-area-product.html Units (all variables): 1e6 km2 geoLocations: Northern Hemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: 35.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: Northern Hemisphere Southern Hemisphere: westBoundLongitude: -180.0 degrees east; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: -35.0 degrees North; geoLocationPlace: Southern Hemisphere Size: Northern Hemisphere: 1 file, 4 variables, 2100 elements each (latest month with valid data is element 2099) Southern Hemisphere: 1 file, 4 variables, 2100 elements each (latest month with valid data is element 2099) Format: netCDF DataSources: https://doi.org/10.15770/EUM_SAF_OSI_0013 [last accessed: 2025-01-17] https://doi.org/10.15770/EUM_SAF_OSI_0014 [last accessed: 2025-01-17] https://nsidc.org/data/g02202 [last accessed: 2025-04-16] Contact: uhhsia.ifm (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/uhh-sea-ice-area-product.html https://www.fdr.uni-hamburg.de/record/18163 10.25592/uhhfdm.18163 oai:fdr.uni-hamburg.de:18163 eng info:eu-repo/semantics/altIdentifier/doi/10.25592/uhhfdm.8525 url:https://navigator.eumetsat.int/product/EO:EUM:DAT:0645 url:https://nsidc.org/data/g02202 url:https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/uhh-sea-ice-area-product.html doi:10.15770/EUM_SAF_OSI_0013 url:https://zenodo.org/records/10014535 doi:10.25592/uhhfdm.16126 doi:10.25592/uhhfdm.11346 doi:10.25592/uhhfdm.8525 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Marine Cryosphere Sea ice area Arctic Antarctic Observational data Time series monthly University of Hamburg University of Bergen ICDC UHH Sea Ice Area Product info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8508 2021-02-25T09:53:03Z user-icdc user-cen user-uhh

Kern, Stefan 2021-01-14 Abstract: ESA-CCI sea-ice-ecv phase2 project Envisat and CryoSat-2 monthly mean gridded Arctic sea-ice thickness and uncertainty estimates are combined with daily gridded Arctic satellite passive microwave sea-ice concentration observations and their uncertainties using the SICCI-2 algorithm derived within the same project to obtain an estimate of the Arctic Ocean monthly mean sea-ice volume during winter (October through April). The observational data gap centred at the pole is filled via interpolation as described in the retrieval report: https://icdc.cen.uni-hamburg.de/fileadmin/user_upload/ESA_Sea-Ice-ECV_Phase2/SICCI_Phase2_SIV-Retrieval_Report_v02.pdf. This report does also describe how uncertainties are estimated, and how the Envisat data are bias-corrected with respect to the CryoSat-2 data. The NSIDC sector Arctic Ocean can be viewed, e.g. from the following netCDF file https://icdc.cen.uni-hamburg.de/fileadmin/user_upload/icdc_Bilder/asi_sic/NSIDC-25km-grid__Arctic-SeaIceOutlook_region_masks__GridCellArea__UHAM-ICDC__fv0.01.nc or https://www.arcus.org/files/page/documents/28201/sio_mask.nc. Table of contents: sea ice volume; sea ice volume retrieval error Technical Info: dimension: 105 rows resulting from 15 winters times seven months (October through April); temporalExtent_startDate: 2002-10-01; temporalExtent_endDate: 2017-04-30; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: SIRAL, RA-2; instrumentType: radar altimeter; instrumentLocation: Environmental Satellite (Envisat), CryoSat-2; instrumentProvider: various, ESA Methods: https://icdc.cen.uni-hamburg.de/fileadmin/user_upload/ESA_Sea-Ice-ECV_Phase2/SICCI_Phase2_SIV-Retrieval_Report_v02.pdf. Units: km3; km3 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: 60.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: Arctic Ocean Size: 1 file with three semikolon-separated columns; 105 rows Format: ascii text DataSources: http://catalogue.ceda.ac.uk/uuid/f4c34f4f0f1d4d0da06d771f6972f180 (last accessed: 2018-05-20) http://catalogue.ceda.ac.uk/uuid/ff79d140824f42dd92b204b4f1e9e7c2 (last accessed: 2018-05-20) https://doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5 (last accessed: 2018-05-20) https://doi.org/10.15770/EUM_SAF_OSI_0008 (last accessed: 2018-05-20) Contact: stefan.kern (at) uni-hamburg.de Web page: https://icdc.cen.uni-hamburg.de/en/esa-cci-sea-ice-ecv0.html https://www.fdr.uni-hamburg.de/record/8508 10.25592/uhhfdm.8508 oai:fdr.uni-hamburg.de:8508 eng url:http://catalogue.ceda.ac.uk/uuid/f4c34f4f0f1d4d0da06d771f6972f180 url:http://catalogue.ceda.ac.uk/uuid/ff79d140824f42dd92b204b4f1e9e7c2 doi:10.5285/f17f146a31b14dfd960cde0874236ee5 doi:10.15770/EUM_SAF_OSI_0008 doi:10.25592/uhhfdm.8507 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Marine cryosphere Arctic ocean Sea ice Satellite remote sensing Sea ice volume ESA-CCI SICCI ICDC ESA-SICCI2 Arctic Ocean Winter-Time Monthly Sea-Ice Volume Timeseries info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8967 2023-01-25T09:34:29Z user-cen user-icdc

Kirsch, Bastian Hohenegger, Cathy Klocke, Daniel Senke, Rainer Offermann, Michael Ament, Felix 2021-03-18 This data set contains meteorological measurement data collected during the Field Experiment on Sub-mesoscale Spatio-Temporal variability at Hamburg (FESST@HH) between June and August 2020. The observational set up of FESST@HH consisted of a ground-based network of 103 autonomous measurement stations, that covered the greater area (50 km x 35 km) of Hamburg (Germany; 53.5 °N 10.0 °E) with the primary goal to observe the spatial dimension of convective cold pools. During the experiment 82 low-cost and self-designed APOLLO (Autonomous cold POoL LOgger) stations sampled air temperature and pressure at 1-s resolution, while 21 WXT weather stations with commercial sensors provided additional information on relative humidity, wind speed and precipitation at 10-s resolution. All variables are sampled at a height of 3 m above ground, if not indicated otherwise. https://www.fdr.uni-hamburg.de/record/8967 10.25592/uhhfdm.8967 oai:fdr.uni-hamburg.de:8967 eng doi:10.25592/uhhfdm.8966 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-sa/4.0/legalcode atmosphere meteorological measurements urban measurements network cold pool temperature pressure FESST@HH meteorological network measurements info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10273 2023-08-02T09:21:36Z user-icdc user-cen

Mol, Wouter Heusinkveld, Bert 2022-07-13 This dataset contains measurements of downwelling short wave irradiance, measured in a small scale grid setup at Falkenberg: 20 sensors in 4 by 5 grid with a 50 meter grid spacing. Another 4 sensors were placed in all direction about 5 km away from the main grid at Falkenberg. The sampling rate is 10 Hz, to catch all irradiance variability, and is calibrated against a high quality sun tracker. The strength of this dataset is not the absolute accuracy, but rather the spatial measurements and ability to catch variability. Quality: Accuracy is estimated to be within 5% of a conventional pyranometer. Quality varies depending on weather type, but is best for high solar elevation angles (solar noon +/- 4 hours). Data is manually quality controlled, with detailed quality flags included in the dataset. Some anomalous data is not caught, in particular noisy data due to many insects on the sensor or small dirt from birds that reduces the signal slightly. These effects are much smaller than the driving weather patterns. The data is unsuitable for calculating radiation balances, but it is particularly useful for studying variability and patterns of solar irradiance on small scales. Funding: Dutch Research Council (NWO), Shedding Light On Cloud Shadows: VI.Vidi.192.068 https://www.fdr.uni-hamburg.de/record/10273 10.25592/uhhfdm.10273 oai:fdr.uni-hamburg.de:10273 eng doi:10.25592/12548 doi:10.25592/uhhfdm.10272 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Solar radiation network variability FESSTVaL Falkenberg measurement radiometer Radiometer grid at Falkenberg and surroundings, downwelling shortwave radiation, FESSTVaL campaign info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10468 2023-08-07T13:33:41Z user-cliccs user-icdc user-cen

Sadikni, Remon Jahnke-Bornemann, Annka Kern, Stefan 2022-11-09 Abstract: The globally gridded daily 5-day running mean surface soil moisture product derived at ICDC (https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html , https://doi.org/10.25592/uhhfdm.10467) from soil moisture time series data of the EUMETSAT H-SAF product H119 and its extension H120 based on MetOp-A, -B, and -C ASCAT data, processing version v7 (https://doi.org/10.15570/EUM_SAF_H_0009), are averaged to obtain monthly means of the surface soil moisture (SM) distribution separately for ascending and descending overpasses. The monthly mean SM values include the nominally computed SM values as well as those SM values which were negative (down to -25%, correction flag = 1) or larger than 100% (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. The threshold for the monthly average is (the number of days per Month) 10. If there are fewer values per month, the value is set to the missing_value. For more information see the respective global attribute in the netCDF file. TableOfContents: mean soil moisture extended; mean soil moisture extended noise; number of valid soil moisture extended values per month; mean number of overpasses per grid cell; mean historic probability of snow cover; mean historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD; soil moisture status flag Technical Info: dimensions: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2007-01-01; temporalExtent_endDate: 2021-12-31; temporalResolution: monthly; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.11225; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-A, MetOp-B, MetOp-C); instrumentProvider: EUMETSAT, ESA Methods: For a description of the methods used to obtain the daily 5-day running mean / composite data on which these monthly data are based, we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi: 10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.2, 2022; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.1, 2021; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v1.1, 2022. Units: Units for all variables (see TableOfContents): percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3, 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: 24 files per year [12 for ascending, 12 for descending overpasses]; ~56.439 MegaByte per file; ~19.873 GigaByte in total (data are packed into two zip-archives per year, one for the ascending, one for the descending data) Format: netCDF DataSources: Gridded daily 5-day running mean surface soil moisture maps: https://doi.org/10.25592/uhhfdm.10467; see also https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html Original time-series of the surface soil moisture: https://hsaf.meteoam.it/Products/Detail?prod=H119 and https://hsaf.meteoam.it/Products/Detail?prod=H120 Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/10468 10.25592/uhhfdm.10468 oai:fdr.uni-hamburg.de:10468 eng doi:10.25592/uhhfdm.10467 url:http://hsaf.meteoam.it url:https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html doi:10.25592/uhhfdm.10196 doi:10.25592/uhhfdm.13103 doi:10.25592/uhhfdm.8682 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Monthly Satellite Remote Sensing ASCAT MetOp-A/B/C EUMETSAT HSAF ICDC ASCAT Global Maps of monthly mean surface soil moisture info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:12548 2023-09-11T09:25:27Z user-icdc user-cen user-uhh

van Heerwaarden, Chiel Kern, Stefan Sadikni, Remon Mol, Wouter Heusinkveld, Bert 2023-08-04 Abstract: This data set contains observations of the down-welling short wave irradiance spectrum from a network of custom-made multi-band radiometers at the Falkenberg site from June 14 to 30, 2021. The main variable is the global horizontal irradiance, which is calibrated against a high quality sun tracker at Falkenberg. Additional data includes raw measurements, pre-calibrated spectra (with limitations, see quality), and total column water vapor from spectra. Within the two weeks, we provide (with varying temporal coverage) cloud camera images at 5 seconds interval, which is helpful for interpreting the irradiance measurements (among other things). The strength of this data set is not the absolute accuracy, but rather the spatial measurements and ability to catch variability with high resolution. Note: This entry includes the level0 radiometer data, the level1 radiometer data / spectra and, as the main product for this the descriptions given here applies, the level2 product. TableOfContents: (for level2 only) Irradiance: solar irradiance; quality water vapor: atmosphere_mass_content_of_water_vapor; quality Technical Info: dimension: 864001 x N [10 Hz sampling] 86400 x N [1 second sampling] 1440 x N [1 minute sampling]; temporalExtent_startDate: 2021-06-14 00:00:00; temporalExtent_endDate: 2021-06-30 00:00:00; temporalResolution: 0.1 [10 Hz sampling] 1 [1 second sampling] 60 [1 minute sampling]; temporalResolutionUnit: seconds; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: FROST version 1; instrumentType: https://doi.org/10.5194/egusphere-2022-726; instrumentLocation: Falkenberg; instrumentProvider: none Methods: Measurements were taken in a small scale grid setup at Falkenberg: 20 sensors in 4 by 5 grid with a 50 meter grid spacing. Another 4 sensors were placed in all direction about 5 km away from the main grid at Falkenberg. The sampling rate is 10 Hz, with accurate GPS clock synchronisation, to catch all cloud-induced irradiance variability. Measurements were taken for 2 weeks, from June 14 to 30, 2021. For more details on the sensor itself, please see the (accepted for publication) pre-print here: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-726/. Quality: Data gathering, quality, calibration (The global horizontal irradiance is calibrated against a high quality sun tracker at Falkenberg.), and performance is covered in detail in the manuscript https://doi.org/10.5194/egusphere-2022-726 and other to be publications specified later. Units: (for level2 only) Irradiance: W/m²; 1 water vapor: kg/m²; 1 geoLocations: BoundingBox: westBoundLongitude: 14.071 degrees East; eastBoundLongitude: 14.204 degrees East; southBoundLatidude: 52.1534 degrees North; northBoundLatitude: 52.1704 degrees North; geoLocationPlace: Germany, UTM zone 33U Location: 52.165 °N; 14.120 °E; between 0.4 m and 0.9 m above ground; between 53 m and 67 m above mean sea level Size: Level0: ~1.2 GB (zip archive) Level1: ~0.115 GB (zip archive, radgrid data); 23.843 GB zip-archives, cloudcam images) Level2: ~0.292 GB (4 zip-archives) Format: netCDF; cloudcam images: jpg DataSources: Single site ground-based measurements. Contact: Wouter Mol, email: wbmol (at) wur.nl Webpage: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/fval-wur-radgrid-l2-v1.html Funding: Dutch Research Council (NWO), Shedding Light On Cloud Shadows: VI.Vidi.192.068 Provenance & History: This new version 2 includes updated metadata for the existing data set, and additional level 0, 1, and 2 data + cloud camera images. https://www.fdr.uni-hamburg.de/record/12548 10.25592/uhhfdm.12548 oai:fdr.uni-hamburg.de:12548 eng info:eu-repo/semantics/altIdentifier/doi/10.25592/uhhfdm.10272 doi:10.25592/uhhfdm.10273 doi:10.5194/egusphere-2022-726 url:https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/fval-wur-radgrid-l2-v1.html doi:10.25592/uhhfdm.10272 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Solar radiation network variability FESSTVaL Falkenberg measurement radiometer Radiometer grid at Falkenberg and surroundings, spectral solar irradiance and cloud imagery, FESSTVaL campaign info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10239 2024-02-27T08:21:01Z user-cen user-icdc user-uhh

Sienz, Frank 2011-08-11 The "Standardized Precipitation Index" (SPI) is used to describe extremely dry or wet climate situations. The World Meteorological Organization (WMO) recommends, that all national meteorological and hydrological services should use the SPI for monitoring of dry spells (Press report December 2009, WMO No. 872). The advantages of SPI usage are: Only precipitation data are needed for the calculation of the index. The index is a standardized measure for precipitation in different climatic regions and for seasonal differences. Calculated for different time scales: meteorological, agricultural-economic and hydrological. SPI Classes: SPI ≤ -2: Extremely dry, -2 < SPI ≤ -1.5: Severely dry, -1.5 < SPI ≤ -1: Moderately dry, -1 < SPI ≤ 1: Near normal, 1 < SPI ≤ 1.5: Moderately wet, 1.5 < SPI ≤ 2: Severely wet, SPI ≥ 2: Extremely wet. Calculation: The SPI, presented here, is different from the original SPI definition of McKee et al. 1993. An enhanced SPI is used, that significantly reduces errors resulting from the determination of the precipitation's distribution (Sienz et al. 2011). MC Kee et al. 1993 shifted the time series of the SPI one time step into the future, but this is not done for the calculation of the SPI presented here. The SPI was calculated from two precipitation data sets: European Climate and Data Assessment (ECA&D), E-OBS gridded dataset Version 4.0 (1951 - 2010) for Europe Climate Research Unit (CRU), Version: CRU TS 2.1 (1901 - 2002) for Europe and USA https://www.fdr.uni-hamburg.de/record/10239 10.25592/uhhfdm.10239 oai:fdr.uni-hamburg.de:10239 eng doi:10.25592/uhhfdm.10238 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode climate index SPI precipitation drought SPI - Standardized Precipitation Index from CRU / ECAD for EU and USA info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10198 2024-02-27T09:04:55Z user-icdc user-uhh

Löhnert, Ulrich Knist, Christine Böck, Tobias Pospichal, Bernhard 2022-05-12 This data set contains level1 (brightness temperatures) and level2 (retrieved meteorological variables) of the four ground-based microwave radiometers (MWR) measuring during FESSTVaL 2021 (May-August) at Lindenberg (2 MWR, dwd and uzk), Falkenberg (1 MWR, uzk) and Birkholz (1 MWR, uhh). For each MWR you find up to seven data file types. Up to two level1 data file types: arbritrary viewing direction (e.g. *mwr00_l1_tb_*) and boundary layer scans (e.g. *mwrBL00_l1_tb_*) Up to five level2 data file types: path integrated liquid water path (e.g. *_mwr00_l2_clwvi_*), path integrated water vapor (e.g. *_mwr00_l2_prw_*), coarse vertical resolution water vapor profiles (e.g. *_mwr00_l2_hua_*), coarse vertical resolution temperature profiles (*_mwr00_l2_ta_*), ABL temperature profiles (e.g. *_mwrBL00_l2_ta_*) mwr00 (mwr01) file types are on a typical temporal resolution of 1-2 seconds, whereas mwrBL00 (mwrBL01) are on a temporal resolution on the order of minutes (3-15), depending on the instrument. The level2 data sets have been derived by means of multi-variate regression. They rely on long-term radiosonde data sets for training. Note, that ocasionally liquid water path values can be slightly negative due to statistical error. Please reach out Ulrich Löhnert the contact person in case you detect inconsistencies in the data. Quality: All data files are provided with quality flags that are described in the netcdf file headers. Exclude flagged data from automatic analyses, for case studies, use flagged data with care and contact the responsible person named in the file header in case of any doubt. Integrated liquid water and water vapor from ground-based MWR are amongst the most accurate methods available. Temperature and humidity profiles are only coarsly resolved in the vertical, are, however, continuously available. Temperature vertical resolution decrease quickly with height from tens of meters close to the surface to hundreds of meters at the top of the ABL. Use *_mwrBL00_l2_ta_* profiles for the most accurate temperature profile in the ABL. Humidity profiles contain only roughly two independent pieces of information throughout the whole troposhere. All level2 data products are delivered with an uncertainty specification. https://www.fdr.uni-hamburg.de/record/10198 10.25592/uhhfdm.10198 oai:fdr.uni-hamburg.de:10198 eng doi:10.25592/uhhfdm.10197 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode FESSTVaL microwave radiometers thermodynamic profiles liquid water path integrated water vapor mwr_pro SAMD measurement Microwave Radiometer Observations during FESSTVaL 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:14218 2024-04-29T19:08:38Z user-icdc user-cen user-uhh

Kern, Stefan Sadikni, Remon Becker, Claudia Wacker, Stefan Beyrich, Frank 2024-04-26 Abstract: This data set contains time series of air pressure, precipitation sum, wind speed, wind direction, air temperature, and relative humidiy, measured at the synoptic Lindenberg weather station (10393) during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) from May to August 2021. The Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory supersite is operated by the German national meteorological service (Deutscher Wetterdienst, DWD). Data are level-1 data as 10-minute averages (sums) based on 1 Hz sampling organized in daily files. This data set further contains time series of the downward surface radiation flux densities (short-/longwave irradiance) measured at the radiation platform in Lindenberg during FESSTVaL from May to August 2021. Data are level-1 data as 1-minute averages based on 1 Hz sampling organized in daily files. TableOfContents: Basic Meteorological Data: rainfall amount; rainfall amount quality flag; air pressure; air pressure quality flag; air temperature; air temperature quality flag; relative humidity; relative humidity quality flag; wind speed; wind speed quality flag; wind from direction; wind from direction quality flag Radiation Data: global irradiance at the surface; global irradiance at the surface standard deviation; global irradiance at the surface quality flag; diffuse irradiance at the surface; diffuse irradiance at the surface standard deviation; diffuse irradiance at the surface quality flag; direct irradiance at the surface; direct irradiance at the surface standard deviation; direct irradiance at the surface quality flag; long-wave irradiance at the surface; long-wave irradiance at the surface standard deviation; long-wave irradiance at the surface quality flag; solar zenith angle Technical Info: Basic Meteorological Data: dimension: 144 x 1; temporalExtent_startDate: 2021-05-01 00:00:00; temporalExtent_endDate: 2021-08-31 23:59:59; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: rain[e] H3, PTB220, LTS2000, EE33 DWD, 2D-ultrasonic anemometer, LAM630; instrumentType: weighing tipping bucket, capacitive digital barometer, platinum resistance thermometer, heated capacitive hygrometer, 2D-ultrasonic anemometer, sensor shield; instrumentLocation: all Lindenberg synoptic weather station; instrumentProvider: Lambrecht GmbH, Vaisala Oy, Vaisala Oy, e+e Elektronik GmbH, Thies GmbH, Eigenbrodt GmbH Radiation Data: dimension: 1440 x 1; temporalExtent_startDate: 2021-05-01 00:00:00; temporalExtent_endDate: 2021-08-31 23:59:59; temporalResolution: 1; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: CMP22, CMP22, CH1, CGR4; instrumentType: ventilated and heated pyranometer, shaded ventilated and heated pyranometer on solar tracker, ventilated pyrheliometer on solar tracker, shaded ventilated and heated pyrgeometer on solar tracker; instrumentLocation: all Lindenberg radiation platform; instrumentProvider: all Kipp&Zonen B.V. Methods: Basic Meteorological Data: Data undergo standard quality checks implemented in the DWD synoptic station network. This includes range tests, plausibility tests with respect to neighbouring stations and to temporal changes. For temperature, a second sensor is operated for comparison. Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. The wind measurements are performed at the top of a hill at the observatory site, the measurement place is surrounded by forest edges at distances of a few decametres to about 100 metres except for winds from SSW to NW, they cannot be considered as representative. Radiation Data: Radiation flux sensors are operated in ventilated shields. The uncertainty in the observational period (given as 95 % confidence intervals) is estimated from internal comparisons at ± 4.5 W/m2 (or 2.5 %), ± 5 W/m2 (or 1.5 %), and ± 6.5 W/m2 (or 2 %) for the diffuse, global and direct component, respectively. The longwave uncertainty is less than ± 5 W/m2. In situ calibrations were frequently conducted during the observational period using reference sensors directly traceable to the World Radiometric Reference (WRR) and the World Infrared Standard Group (WISG) for shortwave and longwave radiation, respectively. Quality control follows the recommendations of the WMO baseline surface radiation network (BSRN). It includes absolute value range tests and inter-comparison versus a second independent radiation flux measurement at the same site. The temperature of the emitting sensor surface of the pyrgeometer is checked for plausibility vs. ambient air temperature. Standard deviations are given for all variables listed below. Each measured value is accompanied by a quality flag where 0 = valid, 2 = invalid, 5 = value between extremely rare limits and physically possible limits, 6 = value out of physically possible limits, 9 = value missing. Units: (see TableOfContents) Basic Meteorological Data: kg m-2;1;pa;1;K;1;1;1;m s-1;1;degrees;1 Radiation Data: W m-2;W m-2;1;W m-2;W m-2;1;W m-2;W m-2;1;W m-2;W m-2;1;degrees geoLocations: BoundingBox: westBoundLongitude: 14.118 degrees East; eastBoundLongitude: 14.1220 degrees East; southBoundLatidude: 52.208 degrees North; northBoundLatitude: 52.209 degrees North; geoLocationPlace: Germany, UTM zone 33U Locations: Basic meteorological data: 52.118 °N, 14.120 °E, 98 m to 125 m above mean sea level, 1 m to 10.4 m above ground Radiation data: 52.208 °N, 14.122 °E, 125 m above mean sea level, 1.7 m to 1.9 m above ground Size: Data (level 1 only) are packed into two compressed tar-archives. Their sizes are 0.9 Mbyte for the basic meteorological data and 3.9Mbyte for the radiation data. Format: netCDF DataSources: Single site ground-based instrument measurements, see "Technical Info" for instruments Contact: claudia.becker (at) dwd.de; stefan.wacker (at) dwd.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html https://www.fdr.uni-hamburg.de/record/14218 10.25592/uhhfdm.14218 oai:fdr.uni-hamburg.de:14218 eng url:https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html doi:10.25592/uhhfdm.9824 doi:10.25592/uhhfdm.9902 url:https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval.html doi:10.25592/uhhfdm.14217 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Atmosphere Measurements Temperature Humidity Wind Air Pressure Precipitation Sum Shortwave Irradiance Longwave Irradiance FESSTVAL SAMD Standard meteorology Pressure, Temperature, Humidity, Rain and Wind, and Radiation fluxes (2021) from FESSTVaL in Lindenberg, Germany info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:14516 2024-07-02T13:30:16Z user-icdc user-cen user-uhh

Kern, Stefan Sadikni, Remon Becker, Claudia Samtleben, Nadja Beyrich, Frank Rummel, Udo 2024-06-03 Abstract: This data set contains time series of surface pressure measured (at station level + 1 m) with a Vaisala PTB220A capacitive pressure sensor. [Basic meteorological data] precipitation sum measured (at station level + 1 m) with an OTT Pluvio weighing rain gauge.[Basic meteorological data] surface radiation flux densities (down-/upward, short-/longwave) and of the radiative surface temperature (at station level + 2 m) [Radiation data] soil temperature and soil moisture measured at various depth levels down to – 1.5 m below short grass. [Soil data] soil heat flux densities measured below short grass. [Soil data] air temperature, relative humidity, and wind speed at various levels between 0.5 m and 10 m at the 10m-mast. [Tower data] air temperature, relative humidity, wind speed, and wind direction at various levels between 10 m and 98 m at the 98m-tower. [Tower data] mean values and variances of the wind components (u, v, w), sonic temperature, humidity and of the turbulent fluxes of momentum, sensible heat, and latent heat at three height levels (2.3 m, 50 m, 90 m). [Turbulence data] All these data were measured at the boundary layer field site (GM) Falkenberg during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) from May to August 2021. The Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory supersite is operated by the German national meteorological service (Deutscher Wetterdienst, DWD). See also: Beyrich, F., W. Adam, 2007: Site and Data Report for the Lindenberg Reference Site in CEOP – Phase I. Offenbach a.M. - Selbstverlag des Deutschen Wetterdienstes: Berichte des Deutschen Wetterdienstes. Nr. 230, 55 pp. (ISSN 0072-4130) Turbulence data are level-2 data as 30-minute statistics based on 20 Hz sampling organized in daily files. All other data are level-1 data as 10-minute averages (sums) based on 1 Hz sampling organized in daily files. TableOfContents: Basic Meteorological Data: rainfall amount; rainfall amount quality flag; air pressure; air pressure quality flag Radiation Data: surface downwelling shortwave fllux; surface downwelling shortwave flux quality flag; surface upwelling shortwave flux; surface upwelling shortwave flux quality flag; surface downwelling longwave flux; surface downwelling longwave flux quality flag; surface upwelling longwave flux; surface upwelling longwave flux quality flag; surface temperature; surface temperature quality flag Soil Data: volumetric soil moisture content; volumetric soil moisture content quality flag; soil temperature; soil temperature quality flag; downward heat flux in soil; downward heat flux in soil quality flag Tower Data: 10-m tower: air temperature; air temperature quality flag; relative humidity; relative humidity quality flag; wind speed; wind speed quality flag; wind direction; wind direction quality flag 99-m tower: air temperature; air temperature quality flag; relative humidity; relative humidity quality flag; wind speed; wind speed quality flag; wind direction; wind direction quality flag Turbulence Data: eastward wind u; northward wind v; upward air velocity w; standard deviation of u; standard deviation of v; standard deviation of w; sonic temperature; standard deviation of sonic temperature; wind speed; wind direction; friction velocity; friction velocity quality flag; upward momentum flux; tubulent kinetic energy; surface upward sensible heat flux; surface upward sensible heat flux quality flag; absolute humidity; surface upward water vapor flux; surface upward latent heat flux; surface upward latent heat flux quality flag; signal strength Technical Info: Basic Meteorological Data: dimension: 144 x 1; temporalExtent_startDate: 2021-05-01 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: OTT Pluvio [precipitation], PTB220A [pressure]; instrumentType: weighing rain gauge [precipitation], capacitive pressure sensor [pressure]; instrumentLocation: both boundary layer field site (GM) Falkenberg (station level + 1 m); instrumentProvider: OTT Messtechnik GmbH [precipitation], Vaisala Oy [pressure] Radiation Data: dimension: 144 x 1; temporalExtent_startDate: 2021-05-01 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 1; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: CM24 (2 x CM21) [shortwave], DDPIR [longwave], KT15.82D [surface temperature]; instrumentType: precision pyranometer [shortwave], precision infrared radiometer [longwave], radiation pyrometer [surface temperature]; instrumentLocation: all boundary layer field site (GM) Falkenberg (station level + 2 m); instrumentProvider: Kipp&Zonen B.V. [shortwave], Eppley Lab Inc [longwave], Heitronics GmbH [surface temperature] Soil Data: dimension: 144 x 9 [soil moisture], 144 x 11 [soil temperature], 144 x 3 [downward heatflux]; temporalExtent_startDate: 2021-05-01 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: variable; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; verticalStart: -0.05 [soil moisture], -0.02 [soil temperature], 0.0 [downward heatflux]; verticalStartUnit: meters; verticalEnd: 1.5 [soil moisture], -1.5 [soil temperature], -0.1 [downward heatflux]; verticalEndUnit: meters; instrumentNames: Trime-Pico-32/64 [soil moisture], WK 63.7 [soil temperature], HP3 [downward heatflux] ; instrumentType: TDR sonde [soil moisture], Platinum resistance thermometer [soil temperature], Soil heat flux plate [downward heatflux]; instrumentLocation: all boundary layer field site (GM) Falkenberg; instrumentProvider: IMKO GmbH [soil moisture], TMG GmbH [soil temperature], Rimco [downward heatflux] Tower Data: 10-m tower: dimension: 144 x 7 [all quantities except wind direction], 144 x 1 [wind direction]; temporalExtent_startDate: 2021-05-01 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: variable; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; verticalStart: 0.5 [all quantities except wind direction], 10.0 [wind direction]; verticalStartUnit: meters; verticalEnd: 10.0 [all quantities]; verticalEndUnit: meters; instrumentNames: HMP45D [air temperature, humidity], F460 [wind speed], model 05103 [wind direction]; instrumentType: Psychrometer [air temperature, humidity], Cup anemometer [wind speed], Wind monitor [wind direction]; instrumentLocation: all boundary layer field site (GM) Falkenberg; instrumentProvider: Vaisala Oy [air temperature, humidity], Climatronics Corp. [wind speed], R.M. Young Company [wind direction] 99-m tower: 144 x 6 [all quantities except wind direction], 144 x 2 [wind direction]; temporalExtent_startDate: 2021-05-01 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: variable; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; verticalStart: 10.0 [all quantities except wind direction], 40.0 [wind direction]; verticalStartUnit: meters; verticalEnd: 98.0 [all quantities]; verticalEndUnit: meters; instrumentNames: HMP45D [air temperature, humidity], 4.3303.22.000 [wind speed], 4.3121.32.000 [wind direction]; instrumentType: Psychrometer [air temperature, humidity], Cup anemometer [wind speed], Wind vane [wind direction]; instrumentLocation: all boundary layer field site (GM) Falkenberg; instrumentProvider: Vaisala Oy [air temperature, humidity], Thies Klima [wind speed], Thies Klima [wind direction] Turbulence Data: dimension: 48 x 1 x 3 [turb00: 2.3 m, turb01: 50.0 m, turb02: 90.0 m]; temporalExtent_startDate: 2021-05-01 00:30:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 30; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: variable; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; verticalStart: 2.3; verticalStartUnit: meters; verticalEnd: 90.0; verticalEndUnit: meters; instrumentNames: USA-1 [wind and temperature fluctuations], LI-7500RS [water vapor fluctuations] ; instrumentType: Sonic anemometer [wind and temperature fluctuations], Infrared gas analyser [water vapor fluctuations]; instrumentLocation: all boundary layer field site (GM) Falkenberg; instrumentProvider: Metek GmbH [wind and temperature fluctuations], LiCor Inc. [water vapor fluctuations] Methods: Basic Meteorological Data: Air pressure sensor accuracy is specified by the manufacturer with 0.1 hPa at 20 °C. Quality control includes range tests and an intercomparison versus a second independent pressure measurement. Precipitation sensor accuracy is specified by the manufacturer with 0.04 mm. Quality control includes intercomparison versus a second independent precipitation measurement at the same site using a Lambrecht rain[e] H3 sensor. Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. Radiation Data: Radiation flux sensors are operated in ventilated shields. The uncertainty is estimated from internal comparisons at ± 5 W/m2 (or 1.5 % - whichever is larger) for shortwave components. The longwave uncertainty is less than ± 5 W/m2. The radiation sensors operated at GM Falkenberg are regularly changed and compared versus reference sensors directly traceable to the World Radiometric Reference (WRR) and the World Infrared Standard Group (WISG) for shortwave and longwave radiation, respectively. For the infrared thermometer, sensor accuracy is specified by the manufacturer with 0.5 K. Quality control follows the recommendations of the WMO baseline surface radiation network (BSRN). It includes absolute value and albedo range tests, intercomparison versus a second independent radiation flux measurement at the same site, cross-checks of the surface temperature derived from the outgoing longwave radiation versus the direct surface temperature measurement and of the shortwave radiation components vs. photosynthetically active radiation (PAR) measured with a LI190SZ photodiode sensor. Temperatures of the emitting surfaces derived from the longwave radiation components are checked for plausibility vs. surface and ambient air temperature. Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. Soil Data: Soil temperature and volumetric soil moisture content data are based on measurements with platinum resistance thermometers and TDR sondes buried at various depth levels below short grass. Up to four sensors are available at each depth. The values reported represent an average of all measurements at a given depth that do not deviate by more than 1 K for soil temperature (2 K at -0.05 m) and the maximum of either 5 Vol-% or 50 % of the mean value for the volumetric soil moisture content. Based on long-term inter-comparison experiments, the uncertainty of the volumetric soil moisture content values can be estimated less than 3 Vol-% in the upper 0.4 m and less then 5 Vol-% below 1 m. In the intermediate layer (0.4 m to 1 m) heterogeneity of the soil may cause local differences up to 10 Vol-%. Soil heat flux data are based on measurements with flux plates HP3 (Rimco) buried at -0.05 m and -0.1 m below short grass. The values reported represent an average of measurements with up to four sensors at each depth. Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. Flag = 1 is assigned if the difference between the sensors is less than 10 W/m2 or 30 % of the mean value. Tower Data: At the 10-m tower, air temperature and relative humidity are measured simultaneously by HMP45D and by an aspirated Frankenberger psychrometer in actively aspirated radiation shields. The accuracies of the sensors are specified by the manufacturers as follows: Air temperature: 1/3 DIN IEC 751 Class B // Relative humidity: 2 % (3 % above 90 % relative humidity) // Wind speed: 0.07 m/s or 1 % whichever is greater. An offset correction is applied to the HMP45D air temperature data based on a regular inter-comparison of the HMP45D temperature measurements against psychrometer temperature measurements during night-time. This offset correction typically is in the range 0.05 - 0.20 K, it has been found to be almost constant in time (variations of less than 0.05 K). A correction for the HMP45D was derived by minimising the rmsd when compared to the psychrometer data. The coefficients of the non-linear (polynomial) regression model for the FESSTVaL period were based on parallel measurements in April 2021 and in July 2021. Relative humidity values > 100% are set equal to 100%. Due to the construction of the 10m mast there are flow distortion effects on the wind speed measurements for winds from the sector between 35 degree and 85 degree, these data are flagged correspondingly. Wind speed values smaller than 0.13 m/s are interpreted as calm and set to zero. In this case corresponding wind direction is set equal to zero as well. Note that wind direction equal to zero marks calm conditions, while wind from North is indicated by a wind direction of 360 degree. Quality control includes a regular comparison of the air temperature and relative humidity differences of the HMP45D vs. the psychrometer measurements. For wind speed, an increase with height is assumed except for very low winds. Each measured value is accompanied by a quality where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available At the 99-m tower air temperature and relative humidity are measured simultaneously in actively aspirated radiation shields For humidity a HMP45D and a Thygan VTP06 dewpoint mirror are used. The accuracies of the sensors are specified by the manufacturers as follows: Air temperature: 1/3 DIN IEC 751 Class B // Relative humidity: 2 % (3 % above 90 % relative humidity) // Wind speed: 0.3 ms-1 (or: 2 %) rms. An offset correction is applied to the HMP45D air temperature data based on a regular inter-comparison of the HMP45D temperature measurements against psychrometer temperature measurements during night-time. This offset correction typically is in the range 0.05 - 0.20 K, it has been found to be almost constant in time (variations of less than 0.05 K). A polynomial correction for the HMP45D was derived by minimising the rmsd when compared to the dewpoint mirror data. The coefficients of the non-linear (polynomial) regression model for the FESSTVaL period were based on parallel measurements in April 2021 and in July 2021. Relative humidity values > 100% are set equal to 100%. Wind measurements at the Falkenberg tower are performed on three booms roughly pointing towards N, S, and W, respectively, at each level. Wind direction is measured at the 40 m and 98 m levels only. The representative wind direction is determined from the measurements at the three booms by vector averaging of those measurements which differ by less than 10 degree - if all three measurements differ by > 10 degree, a comparison with the wind direction from the level below (40 m vs. 11 m at the 10m-mast, 98 m vs. 40 m) is performed and the wind direction closest to that is selected. Wind direction is linearly interpolated between 11 m and 40 m, and between 40 m and 98 m to obtain an estimate at the intermediate levels for wind speed sensor selection. The representative wind speed measurement is then selected in dependence on wind direction. For wind speeds < 2 m/s, the maximum of the three values at a given level is taken as the representative one. Wind speed values smaller than 0.3 m/s are interpreted as calm and set to zero. In this case corresponding wind direction is set equal to zero as well. Note that wind direction equal to zero marks calm conditions, while wind from North is indicated by a wind direction of 360 degree. Quality control includes a regular comparison of the air temperature and relative humidity differences of the HMP45D vs. the psychrometer temperature and dewpoint mirror humidity measurements. For wind speed, an increase with height is assumed except for low winds. Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. Turbulence Data: Two eddy-covariance (EC) systems at a measuring height of 2.3 m are mounted on top of thin pile masts, which are installed at the eastern and western sides of the GM Falkenberg, respectively. In dependence on the prevailing wind direction either the one or the other represents the characteristics of the grassland surface. The two EC systems at 50 m and 90 m on the 99m meteorological tower are each mounted at the tip of a boom pointing towards 190 degree at a distance of 5 m from the tower construction. The booms are fixed to the west side of the lattice tower which has a quadratic cross-section of 1.1 m side length. The measurements are disturbed by lee effects of the tower for wind directions between 0 degree and 55 degree. Weaker upstream effects cannot be ruled out for wind directions between 170 degree and 230 degree, but are difficult to prove. The accuracies of the sensors forming an eddy-covariance (EC) system are specified by the manufacturers as follows: wind vector components: 0.075 ms-1 or 1.5 % of reading // absolute humidity: <1 % of reading. The raw data from the sonic and from the infrared gas analyser (IRGA) based on 20 Hz sampling were processed using the EddyPro V7.0.9 software package provided by LiCor Inc. The following settings were applied: Double rotation of the sonic co-ordinate system acc. to Wilczak et al. (2001, Boundary-Layer Meteorol. 99, 127-150) Despiking and raw data statistical screening (excluding the test for angle of attack and steadiness of horizontal wind) acc. to Vickers and Mahrt (1997, J. Atmos. Ocean. Technol. 14, 512-526) Band pass spectral correction acc. to Moncrieff et al. (1997, J. Hydrol, 188-189, 589-611; 2004, in: Handbook of micrometeorology: a guide for surface flux measurements, eds. Lee, X., W. J. Massman and B. E. Law. Dordrecht: Kluwer Academic, 7-31) Buoyancy and crosswind correction acc. to Schotanus et al. (1983, Boundary-Layer Meteorol. 26, 81-93) Compensation of density fluctuations acc. to Webb et al. (1980, Quart. J. Roy. Meteorol. Soc. 106, 85-100) Quality control of the fluxes includes the stationarity and integral-turbulence-characteristics tests acc. to Mauder et al. (2013, Agric. Forest Meteorol. 169, 122-135), which are implemented in the EddyPro software. These tests are complemented by (i) climatologically-based value range tests, (ii) comparison of net radiation vs. energy fluxes, (iii) validation of ratio of wind speed and friction velocity, and (iv) tests to evaluate the sign of the measured fluxes using gradient measurements (finite differences of the mean variables) provided that both the gradients and the fluxes are not too small. The LI7500 RS signal strength (variable 19) may serve as an additional quality indicator of the IRGA measurement. To validate the quality of the sonic measurements, precipitation measurements are taken into account. All EC system measured quantities such as the wind speed, humidity and temperature are compared to other operational measuring systems in the surrounding area. Each derived flux value is accompanied by a quality flag where 0 = data value missing, 1 = bad quality, 2 = dubious quality, 3 = good quality. Units: (see TableOfContents) Basic Meteorological Data: kg m-2;1;pa;1 Radiation Data: W m-2;1;W m-2;1;W m-2;1;W m-2;1;K;1 Soil Data: Vol-%;1;K;1;W m-2;1 Tower Data [both towers]: K;1;1;1;m s-1;1;degrees;1 Turbulence Data: m s-1;m s-1;m s-1;m s-1;m s-1;m s-1;K;K;m s-1;degrees;m s-1;1;N m-2;J kg-1;W m-2;1;kg m-3;kg m-2s-1;W m-2;1;1 geoLocations: BoundingBox: westBoundLongitude: 14.1221 degrees East; eastBoundLongitude: 14.1223 degrees East; southBoundLatidude: 52.1664 degrees North; northBoundLatitude: 52.1666 degrees North; geoLocationPlace: Germany, UTM zone 33U Locations: Basic Meteorological Data: 52.1665 °N, 14.1222 °E, 73 m above mean sea level, 1 m above ground Radiation Data: 52.1665 °N, 14.1222 °E, 73 m above mean sea level, 2.0 m above ground Soil Data: 52.1665 °N, 14.1222 °E, 73 m above mean sea level, 0.05 m to 1.5 m below ground Tower Data: 52.1665 °N, 14.1222 °E, 73 m above mean sea level, 0.5 m to 98.0 m above ground Turbulence Data: 52.1665 °N, 14.1222 °E, 73 m above mean sea level, 2.3 m to 90.0 m above ground Size: Data (mostly level 1, Turbulence Data level 2) are packed into compressed tar-archives. Their sizes range between 0.2 Mbyte and 2.2 Mbyte. Format: netCDF DataSources: Single site ground-based instrument measurements, see "Technical Info" for instruments Contact: claudia.becker (at) dwd.de; nadja.samtleben (at) dwd.de; frank.beyrich (at) dwd.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-falkenberg.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html https://www.fdr.uni-hamburg.de/record/14516 10.25592/uhhfdm.14516 oai:fdr.uni-hamburg.de:14516 eng doi:10.25592/uhhfdm.9824 url:https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html doi:10.25592/uhhfdm.9902 url:https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-falkenberg.html doi:10.25592/uhhfdm.14240 doi:10.25592/uhhfdm.14239 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Atmosphere Measurements Temperature Humidity Precipitation Sum Air Pressure Wind Speed Wind Direction Boundary Layer Measurement Tower Longwave Radiation Shortwave Radiation Turbulent Fluxes Eddy-Covariance Soil Moisture Surface Temperature Soil Temperature Heatflux in soil FESSTVal SAMD Standard Meteorology at surface and different heights, Turbulent fluxes, Radiation fluxes, Soil temperature (2021) from FESSTVaL in Falkenberg, Germany info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:13101 2024-12-12T13:06:52Z user-cliccs user-icdc user-cen user-uhh

Sadikni, Remon Kern, Stefan 2023-08-07 Abstract: The soil moisture time series data of the extension of the EUMETSAT H-SAF product H119: H120 based on MetOp-B, and -C ASCAT data, processing version v7 (https://doi.org/10.15770/EUM_SAF_H_0009), are converted into geographic maps (cartesian grid) of daily running 5-day average/composite soil moisture (SM) distribution separately for ascending and descending overpasses. Two different 5-day SM distributions are given: one is based solely on nominally computed SM, the other one includes also those SM values which were negative (down to -25%, correction flag = 1) or positive (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. All data are interpolated into a cartesian grid of x- and y-dimensions of original grid. For more information see the respective global attribute in the netCDF file. TableOfContents: soil moisture; soil moisture noise; soil moisture extended; soil moisture extended noise; soil moisture status flag; number of overpasses per grid cell; historic probability of snow cover; historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD Technical Info: dimensons: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2022-01-01; temporalExtent_endDate: 2022-12-31; temporalResolution: daily; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.1125; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-B, MetOp-C); instrumentProvider: EUMETSAT, ESA; License: The following applies to the original product: All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products. Methods: For a description of the methods used to obtain the 5-day average / composite data we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi:10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.2, 2022; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.1, 2021; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v1.1, 2022. Units: units for all variables (see TableOfContents): percent, percent, percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: -90.0 degrees North; northBoundLongitude: 90.0 degrees North; geoLocationPlace: global over land Size: 730 files per year [note: there are 2 files per day, one for the ascending, one for the descending overpasses]; ~61.569 MegaByte per file; ~43.892 GigaByte per year (provided as two zip-files per year) Format: netCDF DataSources: Original Data as time series on a 12.5 km DGG Grid: https://hsaf.meteoam.it/Products/Detail?prod=H120 (last access: 2023-07-04); this original product comes with the following notion: "All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products." See also: http://hsaf.meteoam.it; https://hsaf.meteoam.it/Products/Detail?prod=H120 Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/13101 10.25592/uhhfdm.13101 oai:fdr.uni-hamburg.de:13101 eng info:eu-repo/semantics/altIdentifier/doi/10.25592/uhhfdm.8680 doi:10.15770/EUM_SAF_H_0009 url:http://hsaf.meteoam.it url:https://www.cen.uni-hamburg.de/en/icdc/land/ascat-soilmoisture.html doi:10.25592/uhhfdm.10467 doi:10.25592/uhhfdm.13103 doi:10.25592/uhhfdm.13100 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Daily Satellite Remote Sensing ASCAT MetOp-B/C EUMETSAT HSAF University of Vienna ICDC ASCAT Global Maps of daily running 5-day mean surface soil moisture - extension 2022 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:11198 2025-01-14T15:21:22Z user-icdc user-cen user-cliccs

Sadikni, Remon Kern, Stefan 2022-12-27 Abstract: Original forest cover fraction, vegetation cover fraction and fraction of non-vegetated area on 250m grid resolution sinusoidal grid were obtained in HDF file format from https://lpdaac.usgs.gov/mod44bv061/, read together with the bit-encoded quality information and converted into netCDF file format with latitude/longitude coordinates of every 250m x 250m pixel, and decoded quality flag information included (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html). TableOfContents: forest cover fraction; other vegetation cover fraction; non-vegetated land cover fraction; forest cover fraction standard deviation; quality flag Technical Info: dimension: 4800 columns x 4800 rows x unlimited; temporalExtent_startDate: 2000-03-05; temporalExtent_endDate: 2022-03-05; temporalResolution: yearly; spatialResolution: 250; spatialResolutionUnit: meters; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: meters; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: meters; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] https://lpdaac.usgs.gov/products/mod44bv061/; [2] Townshend, J., et al., User Guide for the MODIS Vegetation Continuous Fields product Collection 6.1, verison 1, https://lpdaac.usgs.gov/documents/1494/MOD44B_User_Guide_V61pdf; [3] Algorithm Theoretical Basis Document (ATBD), https://lpdaac.usgs.gov/documents/113/MOD44B_ATBD.pdf; [4] Carroll, M., et al., 2011. Vegetative Cover Conversion and Vegetation Continuous Fields. In: Ramachandran, B., C. O. Justice, and M. Abrams (eds.), Land Remote Sensing and Global Environment Change: NASA's Earth Observing System and the Science of ASTER and MODIS. Springer Verlag.; [5] Hansen, M., et al., 2005. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Rem. Sens., 26(19), 4359-4380. Units: Units for all variables (see TableOfContents): percent; percent; percent; percent; 1 geoLocations: westBoundLongitude:depends on tile; eastBoundLongitude: depends on tile; southBoundLatitude: depends on tile; northBoundLatitude: depends on tile; geoLocationPlace: global on land, see: https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Size: files are packed into one zip-archive per year with an average size of about 25.6 GByte. Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD44B.061 (last accessed 2022-11-01), see also https://lpdaac.usgs.gov/products/mod44bv061/ (last accessed: 2022-11-01) Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html https://www.fdr.uni-hamburg.de/record/11198 10.25592/uhhfdm.11198 oai:fdr.uni-hamburg.de:11198 eng doi:10.5067/MODIS/MOD44B.061 doi:10.25592/uhhfdm.11196 url:https://lpdaac.usgs.gov/products/mod44bv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html doi:10.25592/uhhfdm.11197 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Forest Cover Fraction Vegetation Cover Fraction Sinusoidal grid tiles Yearly Satellite Remote Sensing MODIS EOS-Terra University of Maryland ICDC MODIS Collection 6.1 sinusoidal tiles yearly Forest and Vegetation Cover Fraction info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8561 2025-01-14T15:21:28Z user-icdc user-cen user-cliccs user-uhh

Sadikni, Remon Jahnke-Bornemann, Annika Kern, Stefan 2021-01-29 Abstract: NetCDF files of the forest cover fraction, vegetation cover fraction and fraction of non-vegetated area on 250m grid resolution sinusoidal grid generated at ICDC from the original HDF files obtained from https://lpdaac.usgs.gov/mod44bv006/ are used to compute globally gridded maps of these parameters at 0.5 degree grid resolution on an equi-rectangular climate modeling grid (CMG). The global maps contain the grid-cell mean fractions of the three mentioned parameters, their variance within the grid cells, and - for the forest cover fraction - the grid-cell mean standard deviation. In addition, the data set includes maps of the number of valid forest cover fraction values at 250 m resolution per 0.5 degree grid cell, a grid cell mean quality flag and fractions of the two most abundant quality flags (primary and secondary). Generally all valid data are used; the user is advised to check the quality flags to eventually discard data of low quality. TableOfContents: grid cell mean forest cover fraction; grid cell mean forest cover fraction standard deviation; forest cover fraction variance within grid cell; grid cell mean vegetation cover fraction; vegetation cover fraction variance within grid cell; non-vegetated area cover fraction; non-vegetated area cover fraction variance within grid cell; number of useful vegetation cover fraction values per grid cell; grid cell mean quality flag; primary quality flag fraction; secondary quality flag fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-03-05; temporalExtent_endDate: 2020-03-04; temporalResolution: yearly; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] https://lpdaac.usgs.gov/products/mod44bv006/; [2] Townshend, J., et al., User Guide for the MODIS Vegetation Continuous Fields product Collection 6, verison 1, https://lpdaac.usgs.gov/documents/112/MOD44B_User_Guide_V6.pdf; [3] Algorithm Theoretical Basis Document (ATBD), https://lpdaac.usgs.gov/documents/113/MOD44B_ATBD.pdf; [4] Carroll, M., et al., 2011. Vegetative Cover Conversion and Vegetation Continuous Fields. In: Ramachandran, B., C. O. Justice, and M. Abrams (eds.), Land Remote Sensing and Global Environment Change: NASA's Earth Observing System and the Science of ASTER and MODIS. Springer Verlag.; [5] Hansen, M., et al., 2005. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Rem. Sens., 26(19), 4359-4380. Units: Units for all variables (see TableOfContents): percent; percent; 1; percent; 1; percent; 1; 1; 1; 1; 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: 1 file per year, 2001-2018: 5197276 bytes, 2019: 5197696 bytes; all packed into one zip-archive Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD44B.006 (last accessed 2020-12-14), see also https://lpdaac.usgs.gov/products/mod44bv006/ (last accessed: 2020-12-14) Reprocessed data on sinusoidal grid tiles in netCDF format: https://icdc.cen.uni-hamburg.de/en/modis-vcf-forest.html (last accessed 2021-01-20) Contact: stefan.kern (at) uni-hamburg.de Web page: https://icdc.cen.uni-hamburg.de/en/modis-vcf-forest.html https://www.fdr.uni-hamburg.de/record/8561 10.25592/uhhfdm.8561 oai:fdr.uni-hamburg.de:8561 eng doi:10.5067/MODIS/MOD44B.006 url:https://lpdaac.usgs.gov/products/mod44bv006/ url:https://icdc.cen.uni-hamburg.de/en/modis-vcf-forest.html doi:10.25592/uhhfdm.8560 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Forest Cover Fraction Vegetation Cover Fraction Global maps Yearly Satellite Remote Sensing MODIS EOS-Terra University of Maryland ICDC MODIS Collection 6 global yearly Forest and Vegetation Cover Fraction info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:11776 2025-02-03T16:51:21Z user-uhh user-cliccs user-cen user-icdc

Sadikni, Remon Kern, Stefan 2023-03-17 Abstract: Original LAI and FAPAR data (see https://lpdaac.usgs.gov/products/mod15a2hv061/) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the LAI and FAPAR data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html). For each tile, latitude and longitude information of the center of each 500 m x 500 m pixel is provided in a separate netCDF file. TableOfContents: FAPAR; LAI; FAPAR retrieval standard deviation; LAI retrieval standard deviation; Detailed quality flag; Quality flag for land; Quality flag for cloud and aerosol; Quality flag for method Technical Info: dimension: 2400 columns x 2400 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2022-12-31; temporalResolution: 8-daily; spatialResolution: 500; spatialResolutionUnit: meter; horizontalResolutionXdirection: 500; horizontalResolutionXdirectionUnit: meter; horizontalResolutionYdirection: 500; horizontalResolutionYdirectionUnit: meter; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] MODIS collection 6.1 (C61) LAI/FPAR Product User Guide, https://lpdaac.usgs.gov/documents/926/MOD15_User_Guide_V61.pdf ; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document (ATBD), v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf; [3] Yang, et al., From validation to algorithm improvement. Trans. Geosci. Rem. Sens., 44, 1885-1898, 2006; [4] Morisette, et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, Trans. Geosci. Rem. Sens., 44, 1804-1817, 2006; [5] Garrigues, et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008; [6] https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html Units: Units for all variables (see TableOfContents): percent, m2/m2, percent, m2/m2, 1, 1, 1, 1 geoLocations: westBoundLongitude:depends on tile; eastBoundLongitude: depends on tile; southBoundLatitude: depends on tile; northBoundLatitude: depends on tile; geoLocationPlace: global on land, see: https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Size: (files are packed into one zip-file per year) 2000: 270 x 40 files, 92.169 Mbyte / file 2001: 270 x 44 files (20010618 and 20010626 are missing) 2002: 270 x 35 files (20020101 until 20020322 are missing) 2003-2022: 270 x 46 files / year Latitude/Longitude: 291 files, 46.08 Mbyte / file Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD15A2H.061 [last access: 2023-02-20], see also: https://lpdaac.usgs.gov/products/mod15a2hv061/ [last access: 2023-02-20] Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2023-03-16] https://www.fdr.uni-hamburg.de/record/11776 10.25592/uhhfdm.11776 oai:fdr.uni-hamburg.de:11776 eng doi:10.5067/MODIS/MOD15A2H.061 url:https://lpdaac.usgs.gov/products/mod15a2hv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html doi:10.25592/uhhfdm.10867 doi:10.25592/uhhfdm.11777 doi:10.25592/uhhfdm.10866 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Leaf Area Index LAI FAPAR Sinusoidal Grid Tiles 8-daily Satellite Remote Sensing MODIS EOS-Terra Boston University ICDC MODIS Collection 6.1 Sinusoidal Tiles 8-daily LAI and FAPAR info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:11777 2025-02-03T16:51:31Z user-uhh user-cliccs user-cen user-icdc

Sadikni, Remon Kern, Stefan 2023-03-17 Abstract: Original LAI and FAPAR data (see https://lpdaac.usgs.gov/products/mod15a2hv061/) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the LAI and FAPAR data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html and https://doi.org/10.25592/uhhfdm.10867). These are subsequently read and re-gridded onto an equi-rectangular climate modeling grid (CMG). Only those LAI and FAPAR values are used where i) the cloud flag indicates a maximum of two cloud influences, and where ii) cloud cover is clearly defined, i.e. "assumed clear sky" is not used. Flag layers are summarized such that there is gridded information about 1) cloud fraction, 2) fraction of average and high aerosol load, 3) primary and secondary land-cover type, and 4) primary and secondary quality flag. Primary and secondary refer to the highest and 2nd-highest pixel count of the respective type or flag within the grid cell. Note that the count of valid values differs for the grid cell mean LAI and FAPAR and their variance, and for the grid-cell mean retrieval standard deviation. TableOfContents: Grid cell mean FAPAR; Grid cell mean LAI; FAPAR variance across grid cell; LAI variance across grid cell; Grid cell mean FAPAR retrieval standard deviation; Grid cel mean LAI retrieval standard deviation; Count of useful FAPAR or LAI values per grid cell; Count of useful FAPAR or LAI retrieval standard deviation values in grid cell; Primary quality flag; Secondary quality flag; Primary land cover; Secondary land cover; Grid cell cloud fraction; Grid cell aerosol fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2022-12-31; temporalResolution: 8-daily; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] MODIS collection 6.1 (C61) LAI/FPAR Product User Guide, https://lpdaac.usgs.gov/documents/926/MOD15_User_Guide_V61.pdf ; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document (ATBD), v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf; [3] Yang, et al., From validation to algorithm improvement. Trans. Geosci. Rem. Sens., 44, 1885-1898, 2006; [4] Morisette, et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, Trans. Geosci. Rem. Sens., 44, 1804-1817, 2006; [5] Garrigues, et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008; [6] https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html Units: Units for all variables (see TableOfContents): percent, m2/m2, percent, m4/m4, percent, m2/m2, 1, 1, 1, 1, 1, 1, percent, percent geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: (files are packed into one zip-file per year) 2000: 40 files, 9864980 byte / file 2001: 44 files (20010618 and 20010626 are missing) 2002: 35 files (20020101 until 20020322 are missing) 2003-2022: 46 files / year Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD15A2H.061 [last access: 2023-02-20], see also: https://lpdaac.usgs.gov/products/mod15a2hv061/ [last access: 2023-02-20] Data on sinusoidal grid tiles in netCDF-format: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2023-02-20] and https://doi.org/10.25592/uhhfdm.10867 [last access: 2023-02-20] Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2023-03-16] https://www.fdr.uni-hamburg.de/record/11777 10.25592/uhhfdm.11777 oai:fdr.uni-hamburg.de:11777 eng doi:10.25592/uhhfdm.11776 url:https://lpdaac.usgs.gov/products/mod15a2hv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html doi:10.25592/uhhfdm.10863 doi:10.25592/uhhfdm.8584 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Leaf Area Index LAI FAPAR Global Maps 8-daily Satellite Remote Sensing MODIS EOS-Terra Boston University ICDC MODIS Collection 6.1 global 8-daily LAI and FAPAR info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8526 2025-12-05T09:29:13Z user-cen user-icdc user-cliccs

Doerr, Jakob Notz, Dirk Kern, Stefan 2021-01-18 Abstract: This data set comprises time series of the monthly sea-ice area (SIA) in the Northern Hemisphere (1 file) and in the Southern Hemisphere (1 file). SIA is derived from sea-ice concentration (SIC, also sea-ice area fraction) data of the following products: Walsh (version 2, only Northern Hemisphere), OSI-450 / OSI-430-b, HadISST (version 2), NASA-Team and Comiso-Bootstrap - both from the NSIDC/NOAA SIC climate data record (version 3.1). The monthly SIA is either directly computed from monthly SIC data or is derived as the mean of all daily SIA values. If required, the observational gap at the pole is interpolated. If required, temporal interpolation is applied - both to fill temporal (up to a maximum of 7 consecutive days) and spatial (if less than 1000 non-contiguous missing grid cells per day) gaps. Table of contents: Northern Hemisphere: HadISST_nsidc monthly sea-ice area; HadISST_orig monthly sea-ice area; Comiso-Bootstrap monthly sea-ice area; NASA-Team monthly sea-ice area; OSI-SAF monthly sea-ice area; Walsh monthly sea-ice area; Southern Hemisphere: HadISST_nsidc monthly sea-ice area; HadISST_orig monthly sea-ice area; Comiso-Bootstrap monthly sea-ice area; NASA-Team monthly sea-ice area; OSI-SAF monthly sea-ice area Technical Info: standard_name: sea-ice area; long_name: algorithm_specific hemispheric sea-ice area time-series; dimension: 2040; temporalExtent_startDate: 1850-01-01; temporalExtent_endDate: 2019-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: various SMMR SSM/I SSMIS; instrumentType: various multifrequency_microwave_radiometer; instrumentLocation: various Nimbus-7 DMSP-f8 DMSP-f11 DMSP-f13 DMSP-17; instrumentProvider: various Methods: See description on https://icdc.cen.uni-hamburg.de/en/cryosphere/uhh-sea-ice-area-product.html Units (all variables): 1e6 km2 geoLocations: Northern Hemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: 35.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: Northern Hemisphere Southern Hemisphere: westBoundLongitude: -180.0 degrees east; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: -35.0 degrees North; geoLocationPlace: Southern Hemisphere Size: Northern Hemisphere: 1 file, 6 variables, 2040 elements each Southern Hemisphere: 1 file, 5 variables, 2040 elements each Format: netCDF DataSources: https://nsidc.org/data/g10010 [last accessed: 2019-09-13] http://osisaf.met.no/p/ice/#conc-reproc-v2 [last accessed: 2020-08-14] https://nsidc.org/data/g02202 [last accessed: 2020-08-14] https://www.metoffice.gov.uk/hadobs/hadisst2/data/HadISST.2.2.0.0_sea_ice_concentration.nc.gz [last accessed: 2020-08-14] Contact: uhhsia.ifm (at) uni-hamburg.de Web page: https://icdc.cen.uni-hamburg.de/en/cryosphere/uhh-sea-ice-area-product.html https://www.fdr.uni-hamburg.de/record/8526 10.25592/uhhfdm.8526 oai:fdr.uni-hamburg.de:8526 eng url:https://nsidc.org/data/g10010 url:http://osisaf.met.no/p/ice/#conc-reproc-v2 url:https://nsidc.org/data/g02202 url:https://www.metoffice.gov.uk/hadobs/hadisst2/data/HadISST.2.2.0.0_sea_ice_concentration.nc.gz url:https://icdc.cen.uni-hamburg.de/en/cryosphere/uhh-sea-ice-area-product.html doi:10.25592/uhhfdm.8525 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Marine Cryosphere Sea ice area Arctic Antarctic Time series monthly University of Hamburg University of Bergen ICDC UHH Sea Ice Area Product info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:18335 2026-02-24T09:51:24Z user-icdc user-cliccs user-uhh

Sadikni, Remon Kern, Stefan Yakubu, Fuseini Böhner, Jürgen Bouwer, Laurens M. ul Hasson, Shabeh 2026-02-23 Abstract: The BC-HiRMIP dataset provides globally consist, comprehensive bias-corrected climate datat of High Resolution Model Intercomparison Project (HighResMIP) experiments at daily temporal and 0.5° spatial resolution, covering the period 1979-2050 for hist-1950 and highres-future scenario. Across four global climate models (MPI-ESM1-2-XR, EC-Earth3P-HR, CNRM-CM6-1-HR, HadGEM3-GC31-HM), BC-HiRMIP includes up to 11 essential meteorological variables (temperature suite, precipitation, humidity, radiation, wind, and pressure), spanning equilibrium climate sensitivities of 2.99-5.62°C. For more information see "BC-HiRMIP.pdf" and "Data Description-BC-HiRMIP.pdf" The data are available in netCDF file format per model, run (historical or future), and variable. TableOfContents: daily mean near-surface air temperature (tas); daily minimum near-surface air temperature (tasmin), daily maximum near-surface air temperature (tasmax); daily sum of precipitation (pr); daily sum of precipitation in form of snow (prsn)*; daily mean surface downwelling shortwave radiation (rsds). daily mean surface downwelling longwave radiation (rlds); daily mean near-surface wind speed (sfcWind); daily mean near-surface relative humidity (hurs); daily mean near-surface specific humidity (huss)**; daily mean surface air pressure (ps)** *) Only CNRM-CM6-1-HR and MPI-ESM1-2-XR **) Only EC-Earth3P-HR and HadGEM3-GC31-HM TechnicalInfo: dimension: 720 columns x 360 rows; temporalExtent_startDate_hist-1950: 1979-01-01 00:00:00; temporalExtent_endDate_hist-1950: 2014-12-31 23:59:59; temporalDuration_hist-1950: 36; temporalDurationUnit_hist-1950: a; temporalExtent_startDate_highres-future: 2015-01-01 00:00:00; temporalExtent_endDate_highres-future: 2050-12-31 23:59:59; temporalDuration_highres-future: 36; temporalDurationUnit_highres-future: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 0.5; spatialResolutionUnit: degree; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degree; horizontalResolutionYdirection: 0.25; horizontalResolutionYdirectionUnit: degree; verticalResolution: none; verticalResolutionUnit: none Methods: The ISIMIP3BASD v2.5 bias correction method (see Lange [2019; 2021]) was applied to adjust systematic biases while preserving the climate change signals. This parametric quantile mapping approach: • Corrects biases across all percentiles of variable distributions • Preserves trends in these percentiles • Applies variable-specific treatments (e.g., handling drizzle issues for precipitation) • Maintains physical relationships between variables (particularly for temperature variables) using the W5E5 V2.0 (Lange et al., 2021) observational reference dataset at 0.5 degree resolution. The four climate models participating in the HighResMIP experiment that are used here are: MPI-ESM1-2-XR EC-Earth3P-HR CNRM-CM6-1-HR HadGEM3-GC31-HM The historical runs "hist-1950" begin 1979-01-01 and end 2014-12-31. The future projection runs "highres-future" begin 2015-01-01, end 2050-12-31, and follow the ssp5-8.5 scenario pathway. The dataset was remapped from their native grids resolution to match the reference dataset grid using CDO: conservative remapping for flux variables (precipitation and radiation) and bilinear remapping for other continuous variables. The routines (python) used to create and work with the data sets are available from this web page as well: Bias_CorrectonScript.pr and DeriveHUSS.py Quality: The bias-correction method used is of high quality and has been used for similar datasets multiple times. The performance of the bias correction in replicating the same distribution as that of the W5E5 v2.0 dataset was evaluated for diverse areas located in five different climate types after Köppen-Geiger with convincing results, illustrating the skill of the bias-correction method especially with respect to the distribution of values across the expected range of variation (see also the document BC-HiRMIP.pdf) Units: K; K; K; kg m-2 s-1; kg m-2 s-1; W m-2; W m-2; m s-1; percent; kg kg-1; pa GeoLocation: westBoundCoordinate: -180.0; westBoundCoordinateUnit: degrees East; eastBoundCoordinate: 180.0; eastBoundCoordinateUnit: degrees East; southBoundCoordinate: -90.0; southBoundCoordinateUnit: degrees North; northBoundCoordinate: 90.0; northBoundCoordinateUnit: degrees North Size: CNRM-CM6-1-HR: 203.9 GByte, MPI-ESM1-2-XR: 204.2 GByte, EC-Earth3P-HR: 249.9 GByte, HadGCM3-GC31-HM: 250.1 GByte Format: netCDF DataSources: See the file "Data Description-BC-HiRMIP.pdf" Contact: fuseini.yakubu (at) uni-hamburg.de; shabeh.hasson (at) uni-hamburg.de Webpage: https://www.geo.uni-hamburg.de/geographie/abteilungen/physische-geographie/arbeitsgruppen/ag-hareme.html https://www.fdr.uni-hamburg.de/record/18335 10.25592/uhhfdm.18335 oai:fdr.uni-hamburg.de:18335 eng doi:10.5281/zenodo.4686991 doi:10.5194/gmd-12-3055-2019 doi:10.48364/ISIMIP.342217 doi:10.25592/uhhfdm.18334 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode HighResMip Climate Modeling Bias Correction Air Temperature Near Surface Humidity Precipitation Amount Near Surface Wind Speed Shortwave Radiation Longwave Radiation HAREME GERICS ICDC A Bias-Corrected HighResMIP Dataset for Impact Assessment Studies info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:16752 2026-02-24T13:45:24Z user-cen user-icdc user-uhh user-cliccs

Sadikni, Remon Kern, Stefan 2025-02-04 Abstract: Original LAI and FAPAR data (see https://lpdaac.usgs.gov/products/mod15a2hv061/) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the LAI and FAPAR data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html and https://doi.org/10.25592/uhhfdm.16751). These are subsequently read and re-gridded onto an equi-rectangular climate modeling grid (CMG). Only those LAI and FAPAR values are used where i) the cloud flag indicates a maximum of two cloud influences, and where ii) cloud cover is clearly defined, i.e. "assumed clear sky" is not used. Flag layers are summarized such that there is gridded information about 1) cloud fraction, 2) fraction of average and high aerosol load, 3) primary and secondary land-cover type, and 4) primary and secondary quality flag. Primary and secondary refer to the highest and 2nd-highest pixel count of the respective type or flag within the grid cell. Note that the count of valid values differs for the grid cell mean LAI and FAPAR and their variance, and for the grid-cell mean retrieval standard deviation. TableOfContents: Grid cell mean FAPAR; Grid cell mean LAI; FAPAR variance across grid cell; LAI variance across grid cell; Grid cell mean FAPAR retrieval standard deviation; Grid cel mean LAI retrieval standard deviation; Count of useful FAPAR or LAI values per grid cell; Count of useful FAPAR or LAI retrieval standard deviation values in grid cell; Primary quality flag; Secondary quality flag; Primary land cover; Secondary land cover; Grid cell cloud fraction; Grid cell aerosol fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2024-12-31; temporalResolution: 8-daily; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] MODIS collection 6.1 (C61) LAI/FPAR Product User Guide, https://lpdaac.usgs.gov/documents/926/MOD15_User_Guide_V61.pdf ; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document (ATBD), v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf; [3] Yang, et al., From validation to algorithm improvement. Trans. Geosci. Rem. Sens., 44, 1885-1898, 2006; [4] Morisette, et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, Trans. Geosci. Rem. Sens., 44, 1804-1817, 2006; [5] Garrigues, et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008; [6] https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html Units: Units for all variables (see TableOfContents): percent, m2/m2, percent, m4/m4, percent, m2/m2, 1, 1, 1, 1, 1, 1, percent, percent geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: (files are packed into one zip-file per year) 2000: 40 files, 9864980 byte / file 2001: 44 files (20010618 and 20010626 are missing) 2002: 35 files (20020101 until 20020322 are missing) 2003-2024: 46 files / year Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD15A2H.061 [last access: 2025-01-13], see also: https://lpdaac.usgs.gov/products/mod15a2hv061/ [last access: 2025-01-13] Data on sinusoidal grid tiles in netCDF-format: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2025-02-03] and https://doi.org/10.25592/uhhfdm.16751 [last access: 2025-02-03] Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2025-02-03] https://www.fdr.uni-hamburg.de/record/16752 10.25592/uhhfdm.16752 oai:fdr.uni-hamburg.de:16752 eng doi:10.25592/uhhfdm.16751 url:https://lpdaac.usgs.gov/products/mod15a2hv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html doi:10.25592/uhhfdm.14185 doi:10.25592/uhhfdm.8584 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Leaf Area Index LAI FAPAR Global Maps 8-daily Satellite Remote Sensing MODIS EOS-Terra Boston University ICDC MODIS Collection 6.1 global 8-daily LAI and FAPAR info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:16751 2026-02-24T13:42:18Z user-cen user-icdc user-uhh user-cliccs

Sadikni, Remon Kern, Stefan 2025-02-04 Abstract: Original LAI and FAPAR data (see https://lpdaac.usgs.gov/products/mod15a2hv061/) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the LAI and FAPAR data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html). For each tile, latitude and longitude information of the center of each 500 m x 500 m pixel is provided in a separate netCDF file. TableOfContents: FAPAR; LAI; FAPAR retrieval standard deviation; LAI retrieval standard deviation; Detailed quality flag; Quality flag for land; Quality flag for cloud and aerosol; Quality flag for method Technical Info: dimension: 2400 columns x 2400 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2024-12-31; temporalResolution: 8-daily; spatialResolution: 500; spatialResolutionUnit: meter; horizontalResolutionXdirection: 500; horizontalResolutionXdirectionUnit: meter; horizontalResolutionYdirection: 500; horizontalResolutionYdirectionUnit: meter; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] MODIS collection 6.1 (C61) LAI/FPAR Product User Guide, https://lpdaac.usgs.gov/documents/926/MOD15_User_Guide_V61.pdf ; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document (ATBD), v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf; [3] Yang, et al., From validation to algorithm improvement. Trans. Geosci. Rem. Sens., 44, 1885-1898, 2006; [4] Morisette, et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, Trans. Geosci. Rem. Sens., 44, 1804-1817, 2006; [5] Garrigues, et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008; [6] https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html Units: Units for all variables (see TableOfContents): percent, m2/m2, percent, m2/m2, 1, 1, 1, 1 geoLocations: westBoundLongitude:depends on tile; eastBoundLongitude: depends on tile; southBoundLatitude: depends on tile; northBoundLatitude: depends on tile; geoLocationPlace: global on land, see: https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Size: (files are packed into one zip-file per year) 2000: 270 x 40 files, 92.169 Mbyte / file 2001: 270 x 44 files (20010618 and 20010626 are missing) 2002: 270 x 35 files (20020101 until 20020322 are missing) 2003-2024: 270 x 46 files / year Latitude/Longitude: 291 files, 46.08 Mbyte / file Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD15A2H.061 [last access: 2025-01-13], see also: https://lpdaac.usgs.gov/products/mod15a2hv061/ [last access: 2025-01-13] Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2025-01-13] https://www.fdr.uni-hamburg.de/record/16751 10.25592/uhhfdm.16751 oai:fdr.uni-hamburg.de:16751 eng doi:10.5067/MODIS/MOD15A2H.061 url:https://lpdaac.usgs.gov/products/mod15a2hv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html doi:10.25592/uhhfdm.14184 doi:10.25592/uhhfdm.16752 doi:10.25592/uhhfdm.10866 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Leaf Area Index LAI FAPAR Sinusoidal Grid Tiles 8-daily Satellite Remote Sensing MODIS EOS-Terra Boston University ICDC MODIS Collection 6.1 Sinusoidal Tiles 8-daily LAI and FAPAR info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:638 2023-01-25T09:18:58Z user-uhh user-cen user-icdc

Jahnke-Bornemann, Annika 2019-09-17 The weather and climate change in the North Atlantic has a strong influence on Europe's weather and especially the position of steering lows is of great interest. In the long-term mean of atmospheric pressure in the North Atlantic appeared two regions with increased activity. These lows have a significant impact on the meridional heat flows to Europe. The two activity centres are located on the one hand over the Irminger Sea near Iceland and on the other over the Norwegian Sea near the Lofoten Islands. There is also a primary and secondary minimum in the long-term mean air pressure field at sea level. To study these minima, a climate index of the Iceland-Lofoten pressure difference (ILD index) was defined [Jahnke-Bornemann 2008, 2010]. The calculation of the index was carried out, analogous to the station-based NAO index [Hurrell, 1995], as the difference of the standardized atmospheric pressure anomalies of a defined Iceland and Lofoten region. This data set contains the monthly ILD-Index, calculated from mean sea level pressure (MSLP) fields from ECMWF Reanalysis data ERA 40 (9/1957-12/1978) and ERA Interim (1/1979-1/2017) for the period September 1957 to January 2017, with the reference period 01/1979-12/2000. The areas used for the calculation of pressure means are 70N to 72.5N, 13.5E to 15.75 E for Lofoten, and 60N to 63N, 35W to 38W for Iceland. https://www.fdr.uni-hamburg.de/record/638 10.25592/uhhfdm.638 oai:fdr.uni-hamburg.de:638 eng doi:10.1111/j.1600-0870.2009.00401.x urn:urn:nbn:de:gbv:18-44949 doi:10.25592/uhhfdm.636 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode climate index Iceland Lofoten North Atlantic ILD pressure low Iceland-Lofotes-Difference-Index (ILD) 1957-2017 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8683 2022-11-07T14:05:52Z user-cliccs user-cen user-icdc

Sadikni, Remon Jahnke-Bornemann, Annka Kern, Stefan 2021-02-12 Abstract: The globally gridded daily 5-day running mean surface soil moisture product derived at ICDC (https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html , https://doi.org/10.25592/uhhfdm.8681) from soil moisture time series data of the EUMETSAT H-SAF product H115 and its extension H116 based on MetOp-A and -B ASCAT data, processing version v5, are averaged to obtain monthly means of the surface soil moisture (SM) distribution separately for ascending and descending overpasses. The monthly mean SM values include the nominally computed SM values as well as those SM values which were negative (down to -25%, correction flag = 1) or larger than 100% (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. The threshold for the monthly average is (the number of days per Month) 10. If there are fewer values per month, the value is set to the missing_value. For more information see the respective global attribute in the netCDF file. TableOfContents: mean soil moisture extended; mean soil moisture extended noise; number of valid soil moisture extended values per month; mean number of overpasses per grid cell; mean historic probability of snow cover; mean historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD; soil moisture status flag Technical Info: dimensons: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2007-01-01; temporalExtent_endDate: 2020-06-30; temporalResolution: monthly; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.11225; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-A, MetOp-B); instrumentProvider: EUMETSAT, ESA Methods: For a description of the methods used to obtain the daily 5-day running mean / composite data on which these monthly data are based, we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi: 10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.1, 2019; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling ( H115) and Extension (H116), v0.1, 2019; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.3, 2019. Units: Units for all variables (see TableOfContents): percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3, 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: 24 files per year [12 for ascending, 12 for descending overpasses]; ~56.4393 MegaByte per file; ~17.8592 GigaByte in total (data are packed into two zip-archives per year, one for the ascending, one for the descending data) Format: netCDF DataSources: Gridded daily 5-day running mean surface soil moisture maps: https://doi.org/10.25592/uhhfdm.8681; see also https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html Original time-series of the surface soil moisture: https://doi.org/10.15770/EUM_SAF_H_0006 Contact: stefan.kern (at) uni-hamburg.de Web page: https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/8683 10.25592/uhhfdm.8683 oai:fdr.uni-hamburg.de:8683 eng doi:10.25592/uhhfdm.8681 doi:10.15770/EUM_SAF_H_0006 url:http://hsaf.meteoam.it url:https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html doi:10.25592/uhhfdm.8682 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Monthly Satellite Remote Sensing ASCAT MetOp-A/B EUMETSAT HSAF ICDC ASCAT Global Maps of monthly mean surface soil moisture info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8987 2021-05-20T21:25:22Z user-cen user-icdc user-uhh

Kern, Stefan Sadikni, Remon Gouretski, Viktor Koltermann, Klaus Peter 2021-04-01 Abstract: The World Ocean Circulation Experiment (WOCE) Global Hydrographic Climatology (WGHC) provides the basis for all maps of the WOCE Atlantic Atlas. This climatology consists of optimally analyzed gridded fields of temperature, salinity, dissolved oxygen and nutrients with 0.5-degree spatial resolution for up to 44 depth levels down to 6000 m water depth. The original quality controlled set of vertical property profiles includes both the WOCE hydrographic data and selected historical data after 1970, in order to fill areas between the WOCE sections. The optimal interpolation was performed on density surfaces. This allows to avoid production of artificial water masses in the gridded data and results in sharper horizontal gradients. Another important feature of the WGHC is the hydrostatic stability of the climatologic density profiles. TableOfContents: bottom depth; pressure; salinity; temperature; potential temperature; oxygen content; silicate content; nitrate content; phosphate content; neutral density; potential density; potential_density_range_two_standard_deviations; potential_density_range_four_standard_deviations; error_temperature_salinity; temperature_standard_deviation; salinity_standard_deviation; oxygen_content_standard_deviation; silicate_content_standard_deviation; nitrate_content_standard_deviation; phosphate_content_standard_deviation TechnicalInfo: dimensions: 341 rows x 720 columns x 44 depth levels; temporalExtent_startDate: none; temporalExtent_endDate: none; temporalResolution: none; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: variable; verticalResolutionUnit: m; verticalResolutionSign: positive downwards; verticalStart: 0; verticalEnd: 6000 Methods: Technical Report, Gouretski and Koltermann (2004) Units (in the order given in TableOfContents): m; decibars; degrees C; degrees C; 1; µmol/kg; µmol/kg; µmol/kg; µmol/kg; kg/m³; kg/m³; kg/m³; kg/m³; 1; degrees C; 1; µmol/kg; µmol/kg; µmol/kg; µmol/kg GeoLocations: westBoundLongitude: 0.0 degrees East; eastBoundLongitude: 360.0 degrees East; southBoundLatitude: -80.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global ocean Size: 2 files; 548.596 MB (variables bottom depth until including potential_density_four_standard_deviations) and 295.402 MB (variables error_temperature_salinity until including phosphate_content_standard_deviation) Format: netCDF DataSources: see Technical Report Contact: viktor.gouretski (at) uni-hamburg.de; peter.koltermann (at) gmail.com; remon.sadikni (at) uni-hamburg.de; stefan.kern (at) uni-hamburg.de Web page: https://icdc.cen.uni-hamburg.de/en/woce-climatology.html https://www.fdr.uni-hamburg.de/record/8987 10.25592/uhhfdm.8987 oai:fdr.uni-hamburg.de:8987 eng issn:0946-6010 url:https://icdc.cen.uni-hamburg.de/en/woce-climatology.html url:https://icdc.cen.uni-hamburg.de/en/woce.html url:https://icdc.cen.uni-hamburg.de/en/waghc.html doi:10.25592/uhhfdm.8986 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Ocean Salinity Temperature Density Oxygen Content Nutrients Content Global maps Climatology Hydrographic Observations CTD BSH ICDC WOCE Global Ocean Climatology 1990-1998 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:9766 2023-01-25T09:34:30Z user-icdc

Kirsch, Bastian Hohenegger, Cathy Klocke, Daniel Ament, Felix 2021-12-20 This data set contains meteorological network observations collected during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) from May to August 2021. The observational set up consisted of a ground-based network of 99 autonomous measurement stations that covered an circular area of 30 km in diameter centered around the Meteorological Observatory Lindenberg (eastern Germany; 52.16°N, 14.12°E). The primary goal of the network measurements was to observe the spatial structure of convective cold pools at sub-mesoscale resolution (100 m - 10 km). During the experiment, 82 low-cost and custom-designed APOLLO (Autonomous cold POoL LOgger) stations sampled air temperature and pressure at 1-s resolution, while 21 WXT weather stations based on commercial sensors provided additional information on relative humidity, wind speed and precipitation at 10-s resolution. The data of all network stations is stored in daily files separated after station type and measurement variable. Quality: Absolute accuracy of temperature (pressure) generally better than +/- 0.5 K (1 hPa), however, the instrument design focused on the relative accuracy of measurements. The overall data availability of temperature measurements for APOLLO (WXT) stations is 92.0 % (98.1 %). Maintenance logbook and site picutures are attached for further interpretation of measurements. Instruments: 80x APOLLO (Autonomous cold POoL LOgger) station 19x WXT weather station Location: Circular area of about 30 km in diameter centered around the Meteorological Observatory Lindenberg (eastern Germany; latitude 52.16°N, longitude 14.12°E) https://www.fdr.uni-hamburg.de/record/9766 10.25592/uhhfdm.9766 oai:fdr.uni-hamburg.de:9766 eng doi:10.25592/uhhfdm.9765 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode observation network temperature pressure cold pool convection mesoscale FESSTVal Meteorological network observations by APOLLO and WXT weather stations during FESSTVaL 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10172 2023-01-25T09:34:29Z user-icdc user-cen

Kirsch, Bastian Hohenegger, Cathy Klocke, Daniel Senke, Rainer Offermann, Michael Ament, Felix 2022-05-02 This data set contains meteorological measurement data collected during the Field Experiment on Sub-mesoscale Spatio-Temporal variability at Hamburg (FESST@HH) between June and August 2020. The observational set up of FESST@HH consisted of a ground-based network of 103 autonomous measurement stations (see photos), that covered the greater area (50 km x 35 km) of Hamburg (Germany; 53.5 °N 10.0 °E) with the primary goal to observe the spatial dimension of convective cold pools. During the experiment 82 low-cost and self-designed APOLLO (Autonomous cold POoL LOgger) stations sampled air temperature and pressure at 1-s resolution, while 21 WXT weather stations with commercial sensors provided additional information on relative humidity, wind speed and precipitation at 10-s resolution. All variables are sampled at a height of 3 m above ground, if not indicated otherwise. https://www.fdr.uni-hamburg.de/record/10172 10.25592/uhhfdm.10172 oai:fdr.uni-hamburg.de:10172 eng doi:10.25592/uhhfdm.8966 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode atmosphere meteorological measurements urban measurements network cold pool temperature pressure FESSTVaL SAMD measurement FESST@HH meteorological network measurements info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10181 2023-01-25T09:34:32Z user-icdc user-uhh user-cen

Sarah, Wiesner 2022-05-03 Data Policy for FESSTVaL campaign data This policy holds for all FESSTVaL campaign data. As it is provided via the SAMD archive, SAMD archive data policy is applicable. https://www.fdr.uni-hamburg.de/record/10181 10.25592/uhhfdm.10181 oai:fdr.uni-hamburg.de:10181 doi:10.25592/uhhfdm.9890 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode SAMD FESSTVAL FESST Data Policy for FESSTVaL campaign data info:eu-repo/semantics/other publication-other

oai:fdr.uni-hamburg.de:10195 2022-11-07T13:42:25Z user-icdc user-cen user-cliccs

Sadikni, Remon Jahnke-Bornemann, Annika Kern, Stefan 2022-05-12 Abstract: The soil moisture time series data of the EUMETSAT H-SAF product H115 and its extension H116 based on MetOp-A and -B ASCAT data, processing version v5, are converted into geographic maps (cartesian grid) of daily running 5-day average/composite soil moisture (SM) distribution separately for ascending and descending overpasses. Two different 5-day SM distributions are given: one is based solely on nominally computed SM, the other one includes also those SM values which were negative (down to -25%, correction flag = 1) or positive (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. All data are interpolated into a cartesian grid of x- and y-dimensions of original grid. For more information see the respective global attribute in the netCDF file. TableOfContents: soil moisture; soil moisture noise; soil moisture extended; soil moisture extended noise; soil moisture status flag; number of overpasses per grid cell; historic probability of snow cover; historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD Technical Info: dimensons: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2007-01-01; temporalExtent_endDate: 2021-12-31; temporalResolution: daily; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.1125; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-A, MetOp-B); instrumentProvider: EUMETSAT, ESA; License: The following applies to the original product: All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products. Methods: For a description of the methods used to obtain the 5-day average / composite data we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi:10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.1, 2019; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling ( H115) and Extension (H116), v0.1, 2019; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.3, 2019. Units: units for all variables (see TableOfContents): percent, percent, percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: -90.0 degrees North; northBoundLongitude: 90.0 degrees North; geoLocationPlace: global over land Size: 730 (leap year: 732) files per year [note: there are 2 files per day, one for the ascending, one for the descending overpasses]; ~61.569 MegaByte per file; ~43.892 GigaByte per year; ~658.860 GigaByte in total (provided as two zip-files per year) Format: netCDF DataSources: Original Data as time series on a 12.5 km DGG Grid: https://doi.org/10.15770/EUM_SAF_H_0006 (last access: 2022-04-22); this original product comes with the following notion: "All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products." See also: http://hsaf.meteoam.it; https://navigator.eumetsat.int/product/EO:EUM:DAT:METOP:H115; https://navigator.eumetsat.int/product/EO:EUM:DAT:METOP:H116 Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/10195 10.25592/uhhfdm.10195 oai:fdr.uni-hamburg.de:10195 eng doi:10.15770/EUM_SAF_H_0006 url:http://hsaf.meteoam.it url:https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html doi:10.25592/uhhfdm.8988 doi:10.25592/uhhfdm.8680 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Daily Satellite Remote Sensing ASCAT MetOp-A/B EUMETSAT HSAF University of Vienna ICDC ASCAT Global Maps of daily running 5-day mean surface soil moisture info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10180 2023-01-25T09:15:50Z user-icdc user-cen

Jahnke-Bornemann, Annika 2022-05-02 The SAMD light data-product description-document includes the conventions for file names, variables and NetCDF-files. The standardized XML-file convention is included as well as all necessary abbreviations for institutes, instruments, variables, etc. https://www.fdr.uni-hamburg.de/record/10180 10.25592/uhhfdm.10180 oai:fdr.uni-hamburg.de:10180 eng doi:10.25592/uhhfdm.9902 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode SAMD FESSTVaL data standard measurement The SAMD Product Standard (Standardized Atmospheric Measurement Data) info:eu-repo/semantics/technicalDocumentation publication-technicalnote

oai:fdr.uni-hamburg.de:10409 2024-02-26T10:16:37Z user-icdc user-cliccs user-cen user-uhh

Kern, Stefan 2022-08-19 Abstract: Spatial correlation length scales derived from ESA-CCI SICCI-2 project sea-ice concentration data products at 25.0km grid resolution based on AMSR-E brightness temperature measurements for the period June 2002 through September 2011. TableOfContents: sea_ice_area_fraction_error_correlation_length; total_standard_error_correlation_length; minimum_rmsd_for_sea_ice_area_fraction_correlation_length; minimum_rmsd_for_total_standard_error_correlation_length; surface_type_flag Technical Info: dimensions: 432 columns x 432 rows x unlimited; temporalExtent_startDate: 2002-06-16; temporalExtent_endDate: 2011-09-19; temporalResolution: daily; spatialResolution: 25.0; spatialResolutionUnit: km; horizontalResolutionXdirection: 25.0; horizontalResolutionXdirectionUnit: km; horizontalResolutionYdirection: 25.0; horizontalResolutionYdirectionUnit: km; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E); instrumentType: multi-channel microwave radiometer; instrumentLocation: Earth Observation Satellite (EOS) - Aqua; instrumentProvider: JAXA; License: CC-BY-SA-NC-4.0 Methods: See Kern, S., Spatial correlation length scales of sea-ice concentration errors of high-concentration pack ice, Remote Sensing, 13(21), 4421, 2021, https://doi.org/10.3390/rs13214421 Units: units for all variables (see TableOfContents): km,km,1,1,1 geoLocations: NorthernHemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: 16.62393 degrees North; northBoundLongitude: 90.0 degrees North; geoLocationPlace: northernHemisphere SouthernHemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: -90.0 degrees North; northBoundLongitude: -16.62393 degrees North; geoLocationPlace: southernHemisphere Size: 730 (leap year 732) files per year, one for each hemisphere, ~4.67 MegaBype per file, ~3.3295 GigaByte per year, ~30.823 GigaByte in total (provided as annual zip archives for each hemisphere = 20 files) Format: netCDF DataSources: Pedersen, L. T., Dybkjaer, G., Eastwood, S., Heygster, G., Ivanova, N., Kern, S., Lavergne, T., Saldo, R., Sandven, S., Soerensen, A., and Tonboe, R. T.: ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR2 instruments at 25km grid spacing, version 2.1, Centre for Environmental Data Analysis [data set], 5 October 2017, https://doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5 Contact: stefan.kern (at) uni-hamburg.de https://www.fdr.uni-hamburg.de/record/10409 10.25592/uhhfdm.10409 oai:fdr.uni-hamburg.de:10409 eng doi:10.3390/rs13214421 doi:10.5285/f17f146a31b14dfd960cde0874236ee5 doi:10.5285/f17f146a31b14dfd960cde0874236ee5 doi:10.25592/uhhfdm.10408 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Spatial correlation length scales Sea ice concentration Hemispheric maps Satellite remote sensing AMSR-E Daily ESA-CCI ICDC Spatial correlation length scales of sea-ice concentration errors of high-concentration pack ice for ESA-CCI-SICCI2-25km info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10415 2024-02-26T10:16:31Z user-icdc user-cliccs user-cen user-uhh

Kern, Stefan 2022-08-19 Abstract: Spatial correlation length scales derived from Eumetsat OSI SAF OSI-450 sea-ice concentration data products at 25.0km grid resolution based on SSM/I and SSMIS brightness temperature measurements for the period January 2002 through December 2011. TableOfContents: sea_ice_area_fraction_error_correlation_length; total_standard_error_correlation_length; minimum_rmsd_for_sea_ice_area_fraction_correlation_length; minimum_rmsd_for_total_standard_error_correlation_length; surface_type_flag Technical Info: dimensions: 432 columns x 432 rows x unlimited; temporalExtent_startDate: 2002-01-01; temporalExtent_endDate: 2011-12-31; temporalResolution: daily; spatialResolution: 25.0; spatialResolutionUnit: km; horizontalResolutionXdirection: 25.0; horizontalResolutionXdirectionUnit: km; horizontalResolutionYdirection: 25.0; horizontalResolutionYdirectionUnit: km; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Special Sensor Microwave / Imager (SSM/I), Special Sensor Microwave Imager / Sounder (SSMIS); instrumentType: multi-channel microwave radiometer; instrumentLocation: NOAA/NASA DMSP; instrumentProvider: NOAA/NASA; License: CC-BY-SA-NC-4.0 Methods: See Kern, S., Spatial correlation length scales of sea-ice concentration errors of high-concentration pack ice, Remote Sensing, 13(21), 4421, 2021, https://doi.org/10.3390/rs13214421 Units: units for all variables (see TableOfContents): km,km,1,1,1 geoLocations: NorthernHemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: 16.62393 degrees North; northBoundLongitude: 90.0 degrees North; geoLocationPlace: northernHemisphere SouthernHemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: -90.0 degrees North; northBoundLongitude: -16.62393 degrees North; geoLocationPlace: southernHemisphere Size: 730 (leap year 732) files per year, one for each hemisphere, ~4.67 MegaBype per file, ~3.3295 GigaByte per year, ~33.295 GigaByte in total (provided as annual zip archives for each hemisphere = 20 files) Format: netCDF DataSources: EUMETSAT Ocean and Sea Ice Satellite Application Facility. (OSI SAF) Global sea ice concentration climate data record 1979-2015 (v2.0, 2017). Norwegian and Danish Meteorological Institutes. Available from osisaf.met.no., doi.org/10.15770/EUM_SAF_OSI_0008 [last accessed January 18 2022]. Contact: stefan.kern (at) uni-hamburg.de https://www.fdr.uni-hamburg.de/record/10415 10.25592/uhhfdm.10415 oai:fdr.uni-hamburg.de:10415 eng doi:10.3390/rs13214421 doi:10.15770/EUM_SAF_OSI_0008 doi:10.15770/EUM_SAF_OSI_0008 doi:10.25592/uhhfdm.10414 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Spatial correlation length scales Sea ice concentration Hemispheric maps Satellite remote sensing SSM/I SSMIS Daily Eumetsat OSI SAF ICDC Spatial correlation length scales of sea-ice concentration errors of high-concentration pack ice for Eumetsat OSI SAF product OSI-450 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:9824 2024-02-28T09:47:06Z user-icdc user-cen user-uhh

Annika Jahnke-Bornemann Andrea Lammert Felix Ament 2022-01-19 Standardized Atmospheric Measurement Data - SAMD: Data Policy for Data USERS This policy holds for all data that is provided by the SAMD archive. 1. Creative Commons License All data in the SAMD archive are licensed under a Creative Commons License CC-BYNC-SA (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) as far as those conditions are not in any way modified by the following conditions or by any conditions specific to data (especially Cloudnet data, see 7.). 2. Used freely for non-commercial only Data from the SAMD archive may be used freely for research only (non-commercial). 3. Audit All downloads from the SAMD archive are auditable. Information will be used for statistical analyses, such as download statistics 4. Publications Articles, papers, or written scientific works of any form, based in whole or in part on data supplied by the SAMD archive, will contain a reference including the title, author, and PID number of each used data set, as given in the meta data file. 5. Share alike Any person extracting data from this server will accept responsibility for informing all data users of these conditions. 6. Liability / Warranty The data are delivered to the user without a warranty of any kind. The user is aware that the data were generated in keeping with the current state of science and technology. 7. Data originating from other data bases, e.g. Cloudnet Data in the frame of ACTRIS Here, the data policy of the source data base applies in addition which is stored in the meta data of the data set. Please make sure that you use these data in agreement with the corresponding conditions of use. https://www.fdr.uni-hamburg.de/record/9824 10.25592/uhhfdm.9824 oai:fdr.uni-hamburg.de:9824 eng doi:10.25592/uhhfdm.9823 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode SAMD Policy measurement SAMD: Data Policy for Data USERS info:eu-repo/semantics/other publication-other

oai:fdr.uni-hamburg.de:14240 2024-07-02T13:30:16Z user-icdc user-cen user-uhh

Kern, Stefan Sadikni, Remon Becker, Claudia Samtleben, Nadja Beyrich, Frank Rummel, Udo 2024-06-03 Abstract: This data set contains time series of surface pressure measured (at station level + 1 m) with a Vaisala PTB220A capacitive pressure sensor. [Basic meteorological data] precipitation sum measured (at station level + 1 m) with an OTT Pluvio weighing rain gauge.[Basic meteorological data] surface radiation flux densities (down-/upward, short-/longwave) and of the radiative surface temperature (at station level + 2 m) [Radiation data] soil temperature and soil moisture measured at various depth levels down to – 1.5 m below short grass. [Soil data] soil heat flux densities measured below short grass. [Soil data] air temperature, relative humidity, and wind speed at various levels between 0.5 m and 10 m at the 10m-mast. [Tower data] air temperature, relative humidity, wind speed, and wind direction at various levels between 10 m and 98 m at the 98m-tower. [Tower data] mean values and variances of the wind components (u, v, w), sonic temperature, humidity and of the turbulent fluxes of momentum, sensible heat, and latent heat at three height levels (2.3 m, 50 m, 90 m). [Turbulence data] All these data were measured at the boundary layer field site (GM) Falkenberg during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) from May to August 2021. The Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory supersite is operated by the German national meteorological service (Deutscher Wetterdienst, DWD). See also: Beyrich, F., W. Adam, 2007: Site and Data Report for the Lindenberg Reference Site in CEOP – Phase I. Offenbach a.M. - Selbstverlag des Deutschen Wetterdienstes: Berichte des Deutschen Wetterdienstes. Nr. 230, 55 pp. (ISSN 0072-4130) Turbulence data are level-2 data as 30-minute statistics based on 20 Hz sampling organized in daily files. All other data are level-1 data as 10-minute averages (sums) based on 1 Hz sampling organized in daily files. TableOfContents: Basic Meteorological Data: rainfall amount; rainfall amount quality flag; air pressure; air pressure quality flag Radiation Data: surface downwelling shortwave fllux; surface downwelling shortwave flux quality flag; surface upwelling shortwave flux; surface upwelling shortwave flux quality flag; surface downwelling longwave flux; surface downwelling longwave flux quality flag; surface upwelling longwave flux; surface upwelling longwave flux quality flag; surface temperature; surface temperature quality flag Soil Data: volumetric soil moisture content; volumetric soil moisture content quality flag; soil temperature; soil temperature quality flag; downward heat flux in soil; downward heat flux in soil quality flag Tower Data: 10-m tower: air temperature; air temperature quality flag; relative humidity; relative humidity quality flag; wind speed; wind speed quality flag; wind direction; wind direction quality flag 99-m tower: air temperature; air temperature quality flag; relative humidity; relative humidity quality flag; wind speed; wind speed quality flag; wind direction; wind direction quality flag Turbulence Data: eastward wind u; northward wind v; upward air velocity w; standard deviation of u; standard deviation of v; standard deviation of w; sonic temperature; standard deviation of sonic temperature; wind speed; wind direction; friction velocity; friction velocity quality flag; upward momentum flux; tubulent kinetic energy; surface upward sensible heat flux; surface upward sensible heat flux quality flag; absolute humidity; surface upward water vapor flux; surface upward latent heat flux; surface upward latent heat flux quality flag; signal strength Technical Info: Basic Meteorological Data: dimension: 144 x 1; temporalExtent_startDate: 2021-05-01 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: OTT Pluvio [precipitation], PTB220A [pressure]; instrumentType: weighing rain gauge [precipitation], capacitive pressure sensor [pressure]; instrumentLocation: both boundary layer field site (GM) Falkenberg (station level + 1 m); instrumentProvider: OTT Messtechnik GmbH [precipitation], Vaisala Oy [pressure] Radiation Data: dimension: 144 x 1; temporalExtent_startDate: 2021-05-01 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 1; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: CM24 (2 x CM21) [shortwave], DDPIR [longwave], KT15.82D [surface temperature]; instrumentType: precision pyranometer [shortwave], precision infrared radiometer [longwave], radiation pyrometer [surface temperature]; instrumentLocation: all boundary layer field site (GM) Falkenberg (station level + 2 m); instrumentProvider: Kipp&Zonen B.V. [shortwave], Eppley Lab Inc [longwave], Heitronics GmbH [surface temperature] Soil Data: dimension: 144 x 9 [soil moisture], 144 x 11 [soil temperature], 144 x 3 [downward heatflux]; temporalExtent_startDate: 2021-05-01 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: variable; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; verticalStart: -0.05 [soil moisture], -0.02 [soil temperature], 0.0 [downward heatflux]; verticalStartUnit: meters; verticalEnd: 1.5 [soil moisture], -1.5 [soil temperature], -0.1 [downward heatflux]; verticalEndUnit: meters; instrumentNames: Trime-Pico-32/64 [soil moisture], WK 63.7 [soil temperature], HP3 [downward heatflux] ; instrumentType: TDR sonde [soil moisture], Platinum resistance thermometer [soil temperature], Soil heat flux plate [downward heatflux]; instrumentLocation: all boundary layer field site (GM) Falkenberg; instrumentProvider: IMKO GmbH [soil moisture], TMG GmbH [soil temperature], Rimco [downward heatflux] Tower Data: 10-m tower: dimension: 144 x 7 [all quantities except wind direction], 144 x 1 [wind direction]; temporalExtent_startDate: 2021-05-01 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: variable; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; verticalStart: 1.0 [all quantities except wind direction], 10.0 [wind direction]; verticalStartUnit: meters; verticalEnd: 10.0 [all quantities]; verticalEndUnit: meters; instrumentNames: HMP45D [air temperature, humidity], F460 [wind speed], model 05103 [wind direction]; instrumentType: Psychrometer [air temperature, humidity], Cup anemometer [wind speed], Wind monitor [wind direction]; instrumentLocation: all boundary layer field site (GM) Falkenberg; instrumentProvider: Vaisala Oy [air temperature, humidity], Climatronics Corp. [wind speed], R.M. Young Company [wind direction] 99-m tower: 144 x 6 [all quantities except wind direction], 144 x 2 [wind direction]; temporalExtent_startDate: 2021-05-01 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: variable; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; verticalStart: 10.0 [all quantities except wind direction], 40.0 [wind direction]; verticalStartUnit: meters; verticalEnd: 98.0 [all quantities]; verticalEndUnit: meters; instrumentNames: HMP45D [air temperature, humidity], 4.3303.22.000 [wind speed], 4.3121.32.000 [wind direction]; instrumentType: Psychrometer [air temperature, humidity], Cup anemometer [wind speed], Wind vane [wind direction]; instrumentLocation: all boundary layer field site (GM) Falkenberg; instrumentProvider: Vaisala Oy [air temperature, humidity], Thies Klima [wind speed], Thies Klima [wind direction] Turbulence Data: dimension: 48 x 1 x 3 [turb00: 2.3 m, turb01: 50.0 m, turb02: 90.0 m]; temporalExtent_startDate: 2021-05-01 00:30:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 30; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: variable; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; verticalStart: 2.3; verticalStartUnit: meters; verticalEnd: 90.0; verticalEndUnit: meters; instrumentNames: USA-1 [wind and temperature fluctuations], LI-7500RS [water vapor fluctuations] ; instrumentType: Sonic anemometer [wind and temperature fluctuations], Infrared gas analyser [water vapor fluctuations]; instrumentLocation: all boundary layer field site (GM) Falkenberg; instrumentProvider: Metek GmbH [wind and temperature fluctuations], LiCor Inc. [water vapor fluctuations] Methods: Basic Meteorological Data: Air pressure sensor accuracy is specified by the manufacturer with 0.1 hPa at 20 °C. Quality control includes range tests and an intercomparison versus a second independent pressure measurement. Precipitation sensor accuracy is specified by the manufacturer with 0.04 mm. Quality control includes intercomparison versus a second independent precipitation measurement at the same site using a Lambrecht rain[e] H3 sensor. Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. Radiation Data: Radiation flux sensors are operated in ventilated shields. The uncertainty is estimated from internal comparisons at ± 5 W/m2 (or 1.5 % - whichever is larger) for shortwave components. The longwave uncertainty is less than ± 5 W/m2. The radiation sensors operated at GM Falkenberg are regularly changed and compared versus reference sensors directly traceable to the World Radiometric Reference (WRR) and the World Infrared Standard Group (WISG) for shortwave and longwave radiation, respectively. For the infrared thermometer, sensor accuracy is specified by the manufacturer with 0.5 K. Quality control follows the recommendations of the WMO baseline surface radiation network (BSRN). It includes absolute value and albedo range tests, intercomparison versus a second independent radiation flux measurement at the same site, cross-checks of the surface temperature derived from the outgoing longwave radiation versus the direct surface temperature measurement and of the shortwave radiation components vs. photosynthetically active radiation (PAR) measured with a LI190SZ photodiode sensor. Temperatures of the emitting surfaces derived from the longwave radiation components are checked for plausibility vs. surface and ambient air temperature. Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. Soil Data: Soil temperature and volumetric soil moisture content data are based on measurements with platinum resistance thermometers and TDR sondes buried at various depth levels below short grass. Up to four sensors are available at each depth. The values reported represent an average of all measurements at a given depth that do not deviate by more than 1 K for soil temperature (2 K at -0.05 m) and the maximum of either 5 Vol-% or 50 % of the mean value for the volumetric soil moisture content. Based on long-term inter-comparison experiments, the uncertainty of the volumetric soil moisture content values can be estimated less than 3 Vol-% in the upper 0.4 m and less then 5 Vol-% below 1 m. In the intermediate layer (0.4 m to 1 m) heterogeneity of the soil may cause local differences up to 10 Vol-%. Soil heat flux data are based on measurements with flux plates HP3 (Rimco) buried at -0.05 m and -0.1 m below short grass. The values reported represent an average of measurements with up to four sensors at each depth. Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. Flag = 1 is assigned if the difference between the sensors is less than 10 W/m2 or 30 % of the mean value. Tower Data: At the 10-m tower, air temperature and relative humidity are measured simultaneously by HMP45D and by an aspirated Frankenberger psychrometer in actively aspirated radiation shields. The accuracies of the sensors are specified by the manufacturers as follows: Air temperature: 1/3 DIN IEC 751 Class B // Relative humidity: 2 % (3 % above 90 % relative humidity) // Wind speed: 0.07 m/s or 1 % whichever is greater. An offset correction is applied to the HMP45D air temperature data based on a regular inter-comparison of the HMP45D temperature measurements against psychrometer temperature measurements during night-time. This offset correction typically is in the range 0.05 - 0.20 K, it has been found to be almost constant in time (variations of less than 0.05 K). A correction for the HMP45D was derived by minimising the rmsd when compared to the psychrometer data. The coefficients of the non-linear (polynomial) regression model for the FESSTVaL period were based on parallel measurements in April 2021 and in July 2021. Relative humidity values > 100% are set equal to 100%. Due to the construction of the 10m mast there are flow distortion effects on the wind speed measurements for winds from the sector between 35 degree and 85 degree, these data are flagged correspondingly. Wind speed values smaller than 0.13 m/s are interpreted as calm and set to zero. In this case corresponding wind direction is set equal to zero as well. Note that wind direction equal to zero marks calm conditions, while wind from North is indicated by a wind direction of 360 degree. Quality control includes a regular comparison of the air temperature and relative humidity differences of the HMP45D vs. the psychrometer measurements. For wind speed, an increase with height is assumed except for very low winds. Each measured value is accompanied by a quality where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available At the 99-m tower air temperature and relative humidity are measured simultaneously in actively aspirated radiation shields For humidity a HMP45D and a Thygan VTP06 dewpoint mirror are used. The accuracies of the sensors are specified by the manufacturers as follows: Air temperature: 1/3 DIN IEC 751 Class B // Relative humidity: 2 % (3 % above 90 % relative humidity) // Wind speed: 0.3 ms-1 (or: 2 %) rms. An offset correction is applied to the HMP45D air temperature data based on a regular inter-comparison of the HMP45D temperature measurements against psychrometer temperature measurements during night-time. This offset correction typically is in the range 0.05 - 0.20 K, it has been found to be almost constant in time (variations of less than 0.05 K). A polynomial correction for the HMP45D was derived by minimising the rmsd when compared to the dewpoint mirror data. The coefficients of the non-linear (polynomial) regression model for the FESSTVaL period were based on parallel measurements in April 2021 and in July 2021. Relative humidity values > 100% are set equal to 100%. Wind measurements at the Falkenberg tower are performed on three booms roughly pointing towards N, S, and W, respectively, at each level. Wind direction is measured at the 40 m and 98 m levels only. The representative wind direction is determined from the measurements at the three booms by vector averaging of those measurements which differ by less than 10 degree - if all three measurements differ by > 10 degree, a comparison with the wind direction from the level below (40 m vs. 11 m at the 10m-mast, 98 m vs. 40 m) is performed and the wind direction closest to that is selected. Wind direction is linearly interpolated between 11 m and 40 m, and between 40 m and 98 m to obtain an estimate at the intermediate levels for wind speed sensor selection. The representative wind speed measurement is then selected in dependence on wind direction. For wind speeds < 2 m/s, the maximum of the three values at a given level is taken as the representative one. Wind speed values smaller than 0.3 m/s are interpreted as calm and set to zero. In this case corresponding wind direction is set equal to zero as well. Note that wind direction equal to zero marks calm conditions, while wind from North is indicated by a wind direction of 360 degree. Quality control includes a regular comparison of the air temperature and relative humidity differences of the HMP45D vs. the psychrometer temperature and dewpoint mirror humidity measurements. For wind speed, an increase with height is assumed except for low winds. Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. Turbulence Data: Two eddy-covariance (EC) systems at a measuring height of 2.3 m are mounted on top of thin pile masts, which are installed at the eastern and western sides of the GM Falkenberg, respectively. In dependence on the prevailing wind direction either the one or the other represents the characteristics of the grassland surface. The two EC systems at 50 m and 90 m on the 99m meteorological tower are each mounted at the tip of a boom pointing towards 190 degree at a distance of 5 m from the tower construction. The booms are fixed to the west side of the lattice tower which has a quadratic cross-section of 1.1 m side length. The measurements are disturbed by lee effects of the tower for wind directions between 0 degree and 55 degree. Weaker upstream effects cannot be ruled out for wind directions between 170 degree and 230 degree, but are difficult to prove. The accuracies of the sensors forming an eddy-covariance (EC) system are specified by the manufacturers as follows: wind vector components: 0.075 ms-1 or 1.5 % of reading // absolute humidity: <1 % of reading. The raw data from the sonic and from the infrared gas analyser (IRGA) based on 20 Hz sampling were processed using the EddyPro V7.0.9 software package provided by LiCor Inc. The following settings were applied: Double rotation of the sonic co-ordinate system acc. to Wilczak et al. (2001, Boundary-Layer Meteorol. 99, 127-150) Despiking and raw data statistical screening (excluding the test for angle of attack and steadiness of horizontal wind) acc. to Vickers and Mahrt (1997, J. Atmos. Ocean. Technol. 14, 512-526) Band pass spectral correction acc. to Moncrieff et al. (1997, J. Hydrol, 188-189, 589-611; 2004, in: Handbook of micrometeorology: a guide for surface flux measurements, eds. Lee, X., W. J. Massman and B. E. Law. Dordrecht: Kluwer Academic, 7-31) Buoyancy and crosswind correction acc. to Schotanus et al. (1983, Boundary-Layer Meteorol. 26, 81-93) Compensation of density fluctuations acc. to Webb et al. (1980, Quart. J. Roy. Meteorol. Soc. 106, 85-100) Quality control of the fluxes includes the stationarity and integral-turbulence-characteristics tests acc. to Mauder et al. (2013, Agric. Forest Meteorol. 169, 122-135), which are implemented in the EddyPro software. These tests are complemented by (i) climatologically-based value range tests, (ii) comparison of net radiation vs. energy fluxes, (iii) validation of ratio of wind speed and friction velocity, and (iv) tests to evaluate the sign of the measured fluxes using gradient measurements (finite differences of the mean variables) provided that both the gradients and the fluxes are not too small. The LI7500 RS signal strength (variable 19) may serve as an additional quality indicator of the IRGA measurement. To validate the quality of the sonic measurements, precipitation measurements are taken into account. All EC system measured quantities such as the wind speed, humidity and temperature are compared to other operational measuring systems in the surrounding area. Each derived flux value is accompanied by a quality flag where 0 = data value missing, 1 = bad quality, 2 = dubious quality, 3 = good quality. Units: (see TableOfContents) Basic Meteorological Data: kg m-2;1;pa;1 Radiation Data: W m-2;1;W m-2;1;W m-2;1;W m-2;1;K;1 Soil Data: Vol-%;1;K;1;W m-2;1 Tower Data [both towers]: K;1;1;1;m s-1;1;degrees;1 Turbulence Data: m s-1;m s-1;m s-1;m s-1;m s-1;m s-1;K;K;m s-1;degrees;m s-1;1;N m-2;J kg-1;W m-2;1;kg m-3;kg m-2s-1;W m-2;1;1 geoLocations: BoundingBox: westBoundLongitude: 14.1221 degrees East; eastBoundLongitude: 14.1223 degrees East; southBoundLatidude: 52.1664 degrees North; northBoundLatitude: 52.1666 degrees North; geoLocationPlace: Germany, UTM zone 33U Locations: Basic Meteorological Data: 52.1665 °N, 14.1222 °E, 73 m above mean sea level, 1 m above ground Radiation Data: 52.1665 °N, 14.1222 °E, 73 m above mean sea level, 2.0 m above ground Soil Data: 52.1665 °N, 14.1222 °E, 73 m above mean sea level, 0.05 m to 1.5 m below ground Tower Data: 52.1665 °N, 14.1222 °E, 73 m above mean sea level, 0.5 m to 98.0 m above ground Turbulence Data: 52.1665 °N, 14.1222 °E, 73 m above mean sea level, 2.3 m to 90.0 m above ground Size: Data (mostly level 1, Turbulence Data level 2) are packed into compressed tar-archives. Their sizes range between 0.2 Mbyte and 2.2 Mbyte. Format: netCDF DataSources: Single site ground-based instrument measurements, see "Technical Info" for instruments Contact: claudia.becker (at) dwd.de; nadja.samtleben (at) dwd.de; frank.beyrich (at) dwd.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-falkenberg.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html https://www.fdr.uni-hamburg.de/record/14240 10.25592/uhhfdm.14240 oai:fdr.uni-hamburg.de:14240 eng doi:10.25592/uhhfdm.9824 url:https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html doi:10.25592/uhhfdm.9902 url:https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-falkenberg.html doi:10.25592/uhhfdm.14239 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Atmosphere Measurements Temperature Humidity Precipitation Sum Air Pressure Wind Speed Wind Direction Boundary Layer Measurement Tower Longwave Radiation Shortwave Radiation Turbulent Fluxes Eddy-Covariance Soil Moisture Surface Temperature Soil Temperature Heatflux in soil FESSTVal SAMD Standard Meteorology at surface and different heights, Turbulent fluxes, Radiation fluxes, Soil temperature (2021) from FESSTVaL in Falkenberg, Germany info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:14463 2024-07-08T18:00:25Z user-icdc user-cen user-uhh

Sadikni, Remon Kern, Stefan 2024-07-08 Abstract: MODIS Collection 6.1 8-day gap-filled Gross Primary Production (GPP) and Net Photosynthesis data on the MODIS sinusoidal grid provided by LPDAAC: https://doi.org/10.5067/MODIS/MOD17A2HGF.061 (last accessed: 2024-05-07) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the GPP and Net Photosynthesis data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html). For each tile, latitude and longitude information of the center of each 500 m x 500 m pixel is provided in a separate netCDF file. TableOfContents: gross_primary_production (gpp); net_photosynthesis; gpp_quality_flag; gpp_confidence_flag Technical Info: dimension: 2400 columns x 2400 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2023-12-31; temporalResolution: 8-daily; spatialResolution: 500; spatialResolutionUnit: meter; horizontalResolutionXdirection: 500; horizontalResolutionXdirectionUnit: meter; horizontalResolutionYdirection: 500; horizontalResolutionYdirectionUnit: meter; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] Running, S. W., and M. Zhao, Users Guide Daily GPP and Annual NPP (MOD17A2H/A3H) and Year-end Gap-Filled (MOD17A2HGF/A3HGF) Products NASA Earth Observing System MODIS Land Algorithm, (For Collection 6), Version 4.0, January 2, 2019; [2] Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto, A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547-560, 2004; [3] Running, S. W., A measurable planetary boundary layer for the biosphere. Science, 337(6101), 1458-1459, 2012; [4] Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running, Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95(2), 164-176, 2005 Units: kg C m-2; kg C m-2;1; 1 geoLocations: westBoundLongitude: depends on tile; eastBoundLongitude: depends on tile; southBoundLatitude: depends on tile; northBoundLatitude: depends on tile; geoLocationPlace: global on land, see: https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Size: (files are packed into one zip-archive per year) 2000-2021 and 2023: each zip file is about 47.5 Gbyte large; each single file approximately 34.5 Mbytes, for the number of tiles see https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Data of the year 2022 are not published because data provided by LPDAAC seemed to be not reliable Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD17A2HGF.061 (last accessed: 2024-05-07), see also https://lpdaac.usgs.gov/products/mod17a2hgfv061/ (last accessed: 2024-05-07) Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-05-10) https://www.fdr.uni-hamburg.de/record/14463 10.25592/uhhfdm.14463 oai:fdr.uni-hamburg.de:14463 eng doi:10.5067/MODIS/MOD17A2HGF.061 url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html url:https://lpdaac.usgs.gov/products/mod17a2hgfv061/ doi:10.25592/uhhfdm.14449 doi:10.25592/uhhfdm.14462 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Gross Primary Production Net Photosynthesis Tiles 8-daily Satellite Remote Sensing MODIS EOS-Terra NTSG UMT ICDC MODIS Collection 6.1 Sinusoidal Tiles 8-daily gap-filled Gross Primary Production info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:11227 2024-11-14T10:56:24Z user-icdc user-cen user-uhh

Sadikni, Remon Kern, Stefan Detring, Carola Beyrich, Frank Steinheuer, Julian Kayser, Markus Leinweber, Ronny Löhnert, Ulrich Päschke, Eileen 2023-01-17 Abstract: dlidcsm_level1: This data set contains the level-1 data of the Doppler LIDAR measurements at the three supersites (Falkenberg, Lindenberg, Birkholz) operated during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) during the period May 17 to August 31, 2021 (please also see the "Additional Notes" further down on this web page). dlidcsm_level2: This data set contains two wind products: (i) vertical profiles of the mean wind vector and (ii) vertical profiles of wind gusts derived with two different kinds of processing (Level2dwd and Level2uzk, see "Methods") from the Doppler LIDAR level 1 data dlidcsm_level1. sonic: This data set contains level 2 data of the mean wind vector and of the maximum gust wind speed derived from ultrasonic anemometer measurements at heights of 2.4 m, 50.3 m and 90.3 m at the Grenzschichtmessfeld (GM) Falkenberg during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) over the period May 17 to August 31, 2021. The GM Falkenberg as part of the Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory supersite is operated by the German national meteorological service (Deutscher Wetterdienst, DWD). TableOfContents: dlidcsm_level1: sensor azimuth angle; attenuated backscatter coefficient; radial velocity of scatterers away from instrument (doppler velocity); error of doppler velocity; backscatter intensity; range bands; zenith angle dlidcsm_level2: Level2dwd: wind speed; wind direction; eastward wind component u; northward wind component v; upward air velocity w; wind speed of gust; wind direction of gust; eastward wind component of gust u_max; northward wind component of gust v_max; upward air velocity of gust w_max; condition number of gust; coefficient of determination of gust; number of radial velocities of gust; index of gust; autocorrelation function; condition number; coefficient of determination; number of radial velocities; wind quality flag; relative number of good circulations; relative number of good radial velocities Level2uzk: eastward wind component u; northward wind component v; upward air velocity w; eastward wind gust u_max; eastward weakest wind u_min; northward wind gust v_max; northward weakest wind v_min; upward air velocity of weakest wind w_min; wind speed; wind speed of gust; wind speed of weakest wind; wind direction; wind direction of gust; wind direction of weakest wind; covariance of wind; covariance of wind gust; covariance of weakest wind; standard deviation of wind speed; standard deviation of gust wind speed; standard deviation of weakest wind speed sonic: eastward wind component u; northward wind component v; upward air velocity w; quality flag for eastward wind component qc_u; quality flag for northward wind component qc_v; quality flag for upward air velocity qc_w; quality flag for wind speed and direction qc_wind; wind speed; wind direction; wind speed of gust; wind speed of weakest wind; gust factor Technical Info: (see also "Additional Notes" further down) dlidcsm_level1: dimension01: 259200 (nominal maximum number of timesteps per day) x 63 (Stream Line XR); dimension02: 259200 (nominal maximum number of timesteps per day) x 100 (Stream Line); temporalExtent_startDate: 2021-05-18 00:00:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 1/3; temporalResolutionUnit: seconds; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; rangeResolution01: 48 (Stream Line XR); rangeResolutionUnit01: meters; rangeResolution02: 30 (Stream Line); rangeResolutionUnit02: meters; verticalResolution01: 26.5; verticalResolutionUnit01: meters; verticalResolution02: 42.4; verticalResolutionUnit02: meters; verticalStart: 90; verticalStartUnit: meters; verticalEnd01: 2600; verticalEndUnit01: meters; verticalEnd02: 4200; verticalEndUnit02: meters; instrumentNames: Stream Line S/N 78, Stream Line S/N 172, Stream Line S/N 178; Stream Line XR S/N 44, Stream Line XR S/N 161; instrumentType: Doppler LIDAR; instrumentLocation: Birkholz, Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd. dlidcsm_level2: Level2dwd: dimension01: 144 timesteps x 100 (Stream Line); dimension02: 144 timesteps x 63 (Stream Line XR); temporalExtent_startDate: 2021-05-18 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: 42.4.; verticalResolutionUnit: meters; verticalStart: 90; verticalStartUnit: meters; verticalEnd: 4250; verticalEndUnit: meters; instrumentNames: Stream Line S/N 78, Stream Line S/N 172, Stream Line S/N 178; Stream Line XR S/N 44, Stream Line XR S/N 161; instrumentType: Doppler LIDAR; instrumentLocation: Birkholz, Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd.. Level2uzk: dimension01: 144 timesteps x 101 (Stream Line); dimension02: 144 timesteps x 64 (Stream Line XR); temporalExtent_startDate: 2021-05-18 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: 42.4.; verticalResolutionUnit: meters; verticalStart: 90; verticalStartUnit: meters; verticalEnd: 4250; verticalEndUnit: meters; instrumentNames: Stream Line S/N 78, Stream Line S/N 172, Stream Line S/N 178; Stream Line XR S/N 44, Stream Line XR S/N 161; instrumentType: Doppler LIDAR; instrumentLocation: Birkholz, Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd.. sonic: dimension: 144 timesteps per day x 3 heights; temporalExtent_startDate: 2021-05-17 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: 2.4; verticalStartUnit: meters; verticalEnd: 90.3; verticalEndUnit: meters; instrumentName: usa1_standard_1; instrumentType: Ultrasonic anemometer; instrumentLocation: Grenzschichtmessfeld Falkenberg at 2.4, 50.3 and 90.3 meters above ground; instrumentProvider: Metek GmbH. Methods: dlidcsm_level1: Doppler LIDAR profiles extend throughout the lower atmospheric boundary layer from 90 m up to a maximum height typically above 1500 m dependent on the atmospheric backscatter conditions. The Doppler LIDAR measurements were based on a conically Doppler lidar scanning geometry with high temporal resolution (~3.4s for one full scan, azimuth resolution of approx. ~33 deg) and a constant zenith angle of 28 deg. The realization of such a scanning strategy was possible via the continuous scan mode option of the Doppler LIDAR system with a number of accumulated pulses per beam Npa = 3000. Two different types of Halo Photonics DL systems were used at the three sites during the campaign: (i) a Halo Photonics Streamline XR with a range gate length of 48 m and Halo Photonics Streamline with a range gate length of 30 m. For the non-XR systems the focus was set to 500 m, for XR systems it is set to infinity per default. For more details concerning the scan configuration see also Steinheuer et al. (2022). These level 1 data are provided by DWD using the dl_toolbox (https://github.com/mkay-atm/dl_toolbox) and include both the instantaneous Doppler LIDAR measurements and related values (e.g. radial velocity and signal-to-noise ratio as function of range gate, time, and azimuth direction) and relevant information concerning the system’s specific parameters which are either fixed by the manufacturer (e.g. wavelength, pulse repetition frequency, pulse length) or can be configured by the user (e.g. range gate length, number of pulse accumulation, focus). Due to the short sampling time per ray, regular time synchronization vs. a reference at prescribed intervals occasionally resulted in a jump back of the time stamp assigned to each vector of radial velocity data. We did not correct that since we wanted to keep the original level-1 data as they were provided from the instrument. Note that the physical range resolution depends on the pulse length (see, e.g., Frehlich, R., 1997, https://doi.org/10.1175/1520-0426(1997)014<0054:EOWTOC>2.0.CO;2) which is set at a fixed value by the manufacturer, this value is different for each system. To harmonize the output, we configured the Streamline and Streamline XR systems each in the same way. This may imply that the range and height bounds given in the level 1 and level 2 data, respectively, may show a positive or negative overlap between neighbouring range / height gates. dlidcsm_level2: Level-2 data represent 10-min averages of the derived mean wind vector and of wind gust speeds. Usually, gusts are defined as a 3s moving average (WMO). We try to match this by calculating first for each scan (sampling time 3.4 s) a wind estimate and then we search for the maximum value (= gust) within a pre-defined 10min interval. Reliability of both the derived mean wind and the gust wind speeds has been assessed for both methods (see below) by comparison with the sonic wind and gust product data at a reference level of 90 m for a several-months data set. RMSD values are in the order of 0.3 m/s for the mean wind speed, and 0.7 m/s for the maximum gust speed, respectively. The Level2dwd product contains results based on the DWD processing (publication is in preparation and will be added to this description, for first information see Detring, C. et al., 2022). The quality control procedures implemented for the level2dwd product include an assessment of the signal-to-noise ratio of the backscattered lidar signal (snr), various statistical tests to remove outliers (acf, r2), and completeness tests concerning the availability of both single beam data per scan and single-scan wind values per 10-minute interval (n_good_data, n_good_circulation). Each lidar-based value is accompanied by a quality flag (qwind) where 0 = bad, and 1 = good. The Level2uzk product contains results based on the Uni Cologne processing (see Steinheuer et al., 2022) and https://github.com/JSteinheuer/DWL_retrieval). Here, the gust is provided if at least 50% of the individual scans within the 10 minutes have been processed. The quality control procedures implemented for the level2uzk product omits an assessment of the signal-to-noise ratio of the backscattered lidar signal (snr), but are instead based on statistical coherence. This involves fitting a wind vector to the radial observations and iteratively eliminating outliers that are inconsistent with the fit. The iteration stops when the fit has small uncertainties (wind is returned) or too many rejected observations (no wind is returned). The amount of included observations and the quality of the fit is combined to a covariance matrix for the wind vector describing the uncertainty of each estimate. Please check Steinheuer et al. (2022) for more details. sonic: The sonic measurements are recorded at 20Hz and are quality checked. After quality control, both the 10min mean wind and the maximum gust speed for each interval are calculated. Here, the maximum of the 3s moving average is chosen as the maximum gust speed. This goes with the definition of wind gusts according to the WMO standard. Quality control of the sonic raw data follows Vickers and Mahrt (1997). It includes tests for non-physical values, constant values, and spikes. Constant values are eliminated, and spikes are detected and replaced by linearly interpolated values. Note that this spike-detection does not affect the identification of wind gusts, since it only removes significant outliers of at maximum three consecutive data values (corresponding to a duration of less than 0.2 s which is more than one magnitude shorter than the duration of a gust). Each sonic value is accompanied by a quality flag (qc_wind) where 0 = bad, and 1 = good. Units: Units for all variables (see TableOfContents): dlidcsm_level1: degrees; 1/ (m sr), m/s; m/s; 1; m; degrees dlidcsm_level2: Level2dwd: m/s; degrees; m/s; m/s; m/s; m/s; degrees; m/s; m/s; m/s; 1; 1; 1; 1; 1; 1; 1; 1; 1; percent; percent Level2uzk: m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; degrees; degrees; degrees; m²/s²; m²/s²; m²/s²; m/s; m/s; m/s sonic: m/s; m/s; m/s; 1; 1; 1; 1; m/s; degrees; m/s; m/s; 1 geoLocations: BoundingBox: westBoundLongitude: 14.122 degrees East; eastBoundLongitude: 14.192 degrees East; southBoundLatidude: 52.167 degrees North; northBoundLatitude: 52.209 degrees North; geoLocationPlace: Germany, UTM zone 33U Locations: Birkholz: 52.200 degrees North, 14.192 degrees East, 70 meters above mean sea level Falkenberg: 52.167 degrees North, 14.123 degrees East, 73 meters above mean sea level Lindenberg: 52.209 degrees North, 14.122 degrees East, 115 meters above mean sea level Size: All data are organized in daily files. For the ease of downloading all sonic, all Doppler LIDAR level-2, and Doppler LIDAR level-1 data of the three supersites are packed into one tar archive each; the total number of tar archives is hence 1 + 1 + 3. Files sizes of these archives are: sonic: ~3.6 MByte, Doppler LIDAR level-2: ~0.5 GByte, Doppler LIDAR level-1 Birkholz: ~26.1 GByte, Doppler LIDAR level-1 Falkenberg: ~34.4 GByte, Doppler LIDAR level-1 Lindenberg: ~13.1 GByte; the total amount is about 74 GByte. Format: netCDF DataSources: dlidcsm: Single site ground-based remote sensing, see "Technical Info" for instruments sonic: Single site tower-based in situ observations, see "Technical Info" for instruments Contact: general: carola.detring (at) dwd.de sonic: carola.detring (at) dwd.de Doppler LIDAR DWD processing: frank.beyrich (at) dwd.de Doppler LIDAR UzK processing: julian.steinheuer (at) uni-koeln.de Web page: LIDAR: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/fval-dlidcsm-wind-and-gust.html and SONIC: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-turb-l2-wind-and-gust.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html https://www.fdr.uni-hamburg.de/record/11227 10.25592/uhhfdm.11227 oai:fdr.uni-hamburg.de:11227 eng doi:10.25592/uhhfdm.9824 doi:10.25592/uhhfdm.9902 doi:10.5194/amt-15-3243-2022 doi:10.25592/uhhfdm.9757 url:https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html doi:10.5194/ems2022-184 doi:10.25592/uhhfdm.16243 doi:10.25592/uhhfdm.16243 doi:10.25592/uhhfdm.11226 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Doppler LIDAR Ultrasonic Anemometer Wind speed Wind direction Wind gust Measurements FESSTVAL SAMD Ultrasonic anemometer and doppler lidar wind and gust data products during FESSTVAL 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:16243 2024-11-18T08:21:37Z user-cen user-icdc user-uhh

Sadikni, Remon Kern, Stefan Detring, Carola Beyrich, Frank Steinheuer, Julian Kayser, Markus Leinweber, Ronny Löhnert, Ulrich Päschke, Eileen 2024-11-15 Abstract: dlidcsm_level1: This data set contains the level-1 data of the Doppler LIDAR measurements at the three supersites (Falkenberg, Lindenberg, Birkholz) operated during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) during the period May 17 to August 31, 2021 (please also see the "Additional Notes" further down on this web page). dlidcsm_level2: This data set contains two wind products: (i) vertical profiles of the mean wind vector and (ii) vertical profiles of wind gusts derived with two different kinds of processing (Level2dwd and Level2uzk, see "Methods") from the Doppler LIDAR level 1 data dlidcsm_level1. sonic: This data set contains level 2 data of the mean wind vector and of the maximum gust wind speed derived from ultrasonic anemometer measurements at heights of 2.4 m, 50.3 m and 90.3 m at the Grenzschichtmessfeld (GM) Falkenberg during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) over the period May 17 to August 31, 2021. The GM Falkenberg as part of the Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory supersite is operated by the German national meteorological service (Deutscher Wetterdienst, DWD). TableOfContents: dlidcsm_level1: sensor azimuth angle; attenuated backscatter coefficient; radial velocity of scatterers away from instrument (doppler velocity); error of doppler velocity; backscatter intensity; range bands; zenith angle dlidcsm_level2: Level2dwd: wind speed; wind direction; eastward wind component u; northward wind component v; upward air velocity w; wind speed of gust; wind direction of gust; eastward wind component of gust u_max; northward wind component of gust v_max; upward air velocity of gust w_max; condition number of gust; coefficient of determination of gust; number of radial velocities of gust; index of gust; autocorrelation function; condition number; coefficient of determination; number of radial velocities; wind quality flag; relative number of good circulations; relative number of good radial velocities Level2uzk: eastward wind component u; northward wind component v; upward air velocity w; eastward wind gust u_max; eastward weakest wind u_min; northward wind gust v_max; northward weakest wind v_min; upward air velocity of weakest wind w_min; wind speed; wind speed of gust; wind speed of weakest wind; wind direction; wind direction of gust; wind direction of weakest wind; covariance of wind; covariance of wind gust; covariance of weakest wind; standard deviation of wind speed; standard deviation of gust wind speed; standard deviation of weakest wind speed sonic: eastward wind component u; northward wind component v; upward air velocity w; quality flag for eastward wind component qc_u; quality flag for northward wind component qc_v; quality flag for upward air velocity qc_w; quality flag for wind speed and direction qc_wind; wind speed; wind direction; wind speed of gust; wind speed of weakest wind; gust factor Technical Info: (see also "Additional Notes" further down) dlidcsm_level1: dimension01: 259200 (nominal maximum number of timesteps per day) x 63 (Stream Line XR); dimension02: 259200 (nominal maximum number of timesteps per day) x 100 (Stream Line); temporalExtent_startDate: 2021-05-18 00:00:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 1/3; temporalResolutionUnit: seconds; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; rangeResolution01: 48 (Stream Line XR); rangeResolutionUnit01: meters; rangeResolution02: 30 (Stream Line); rangeResolutionUnit02: meters; verticalResolution01: 26.5; verticalResolutionUnit01: meters; verticalResolution02: 42.4; verticalResolutionUnit02: meters; verticalStart: 90; verticalStartUnit: meters; verticalEnd01: 2600; verticalEndUnit01: meters; verticalEnd02: 4200; verticalEndUnit02: meters; instrumentNames: Stream Line S/N 78, Stream Line S/N 172, Stream Line S/N 178; Stream Line XR S/N 44, Stream Line XR S/N 161; instrumentType: Doppler LIDAR; instrumentLocation: Birkholz, Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd. dlidcsm_level2: Level2dwd: dimension01: 144 timesteps x 100 (Stream Line); dimension02: 144 timesteps x 63 (Stream Line XR); temporalExtent_startDate: 2021-05-18 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: 42.4.; verticalResolutionUnit: meters; verticalStart: 90; verticalStartUnit: meters; verticalEnd: 4250; verticalEndUnit: meters; instrumentNames: Stream Line S/N 78, Stream Line S/N 172, Stream Line S/N 178; Stream Line XR S/N 44, Stream Line XR S/N 161; instrumentType: Doppler LIDAR; instrumentLocation: Birkholz, Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd.. Level2uzk: dimension01: 144 timesteps x 101 (Stream Line); dimension02: 144 timesteps x 64 (Stream Line XR); temporalExtent_startDate: 2021-05-18 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: 42.4.; verticalResolutionUnit: meters; verticalStart: 90; verticalStartUnit: meters; verticalEnd: 4250; verticalEndUnit: meters; instrumentNames: Stream Line S/N 78, Stream Line S/N 172, Stream Line S/N 178; Stream Line XR S/N 44, Stream Line XR S/N 161; instrumentType: Doppler LIDAR; instrumentLocation: Birkholz, Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd.. sonic: dimension: 144 timesteps per day x 3 heights; temporalExtent_startDate: 2021-05-17 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: 2.4; verticalStartUnit: meters; verticalEnd: 90.3; verticalEndUnit: meters; instrumentName: usa1_standard_1; instrumentType: Ultrasonic anemometer; instrumentLocation: Grenzschichtmessfeld Falkenberg at 2.4, 50.3 and 90.3 meters above ground; instrumentProvider: Metek GmbH. Methods: dlidcsm_level1: Doppler LIDAR profiles extend throughout the lower atmospheric boundary layer from 90 m up to a maximum height typically above 1500 m (for level2dwd V01 processing: 1000 m) dependent on the atmospheric backscatter conditions. The Doppler LIDAR measurements were based on a conically Doppler lidar scanning geometry with high temporal resolution (~3.4s for one full scan, azimuth resolution of approx. ~33 deg) and a constant zenith angle of 28 deg. The realization of such a scanning strategy was possible via the continuous scan mode option of the Doppler LIDAR system with a number of accumulated pulses per beam Npa = 3000. Two different types of Halo Photonics DL systems were used at the three sites during the campaign: (i) a Halo Photonics Streamline XR with a range gate length of 48 m and Halo Photonics Streamline with a range gate length of 30 m. For the non-XR systems the focus was set to 500 m, for XR systems it is set to infinity per default. For more details concerning the scan configuration see also Steinheuer et al. (2022). These level 1 data are provided by DWD using the dl_toolbox (https://github.com/mkay-atm/dl_toolbox) and include both the instantaneous Doppler LIDAR measurements and related values (e.g. radial velocity and signal-to-noise ratio as function of range gate, time, and azimuth direction) and relevant information concerning the system’s specific parameters which are either fixed by the manufacturer (e.g. wavelength, pulse repetition frequency, pulse length) or can be configured by the user (e.g. range gate length, number of pulse accumulation, focus). Due to the short sampling time per ray, regular time synchronization vs. a reference at prescribed intervals occasionally resulted in a jump back of the time stamp assigned to each vector of radial velocity data. We did not correct that since we wanted to keep the original level-1 data as they were provided from the instrument. Note that the physical range resolution depends on the pulse length (see, e.g., Frehlich, R., 1997, https://doi.org/10.1175/1520-0426(1997)014<0054:EOWTOC>2.0.CO;2) which is set at a fixed value by the manufacturer, this value is different for each system. To harmonize the output, we configured the Streamline and Streamline XR systems each in the same way. This may imply that the range and height bounds given in the level 1 and level 2 data, respectively, may show a positive or negative overlap between neighbouring range / height gates. dlidcsm_level2: Level-2 data represent 10-min averages of the derived mean wind vector and of wind gust speeds. Usually, gusts are defined as a 3s moving average (WMO). We try to match this by calculating first for each scan (sampling time 3.4 s) a wind estimate and then we search for the maximum value (= gust) within a pre-defined 10min interval. Reliability of both the derived mean wind and the gust wind speeds has been assessed for both methods (see below) by comparison with the sonic wind and gust product data at a reference level of 90 m for a several-months data set. RMSD values are in the order of 0.3 m/s for the mean wind speed, and 0.7 m/s for the maximum gust speed, respectively. The Level2dwd product of this release (Version 2024_v01) contains results based on the DWD processing V01. This processing is based on the data processing described in Päschke and Detring (2024, https://doi.org/10.5194/amt-17-3187-2024). Their publication deals with the analysis of Doppler Lidar TKE measurements. As the data processing has similar challenges (due to the small number of pulses per ray), the data handling for the DL gust mode was adapted to this. The new data set (version v01) is generated with this updated processing. A separate publication is in preparation and will be added to this description, for first information see Detring, C. et al., 2022). The quality control procedures implemented for the level2dwd product include an assessment of the signal-to-noise ratio of the backscattered lidar signal (snr), various statistical tests to remove outliers (acf, r2), and completeness tests concerning the availability of both single beam data per scan and single-scan wind values per 10-minute interval (n_good_data, n_good_circulation). Each lidar-based value is accompanied by a quality flag (qwind) where 0 = bad, and 1 = good. The Level2uzk product contains results based on the Uni Cologne processing (see Steinheuer et al., 2022) and https://github.com/JSteinheuer/DWL_retrieval). Here, the gust is provided if at least 50% of the individual scans within the 10 minutes have been processed. The quality control procedures implemented for the level2uzk product omits an assessment of the signal-to-noise ratio of the backscattered lidar signal (snr), but are instead based on statistical coherence. This involves fitting a wind vector to the radial observations and iteratively eliminating outliers that are inconsistent with the fit. The iteration stops when the fit has small uncertainties (wind is returned) or too many rejected observations (no wind is returned). The amount of included observations and the quality of the fit is combined to a covariance matrix for the wind vector describing the uncertainty of each estimate. Please check Steinheuer et al. (2022) for more details. sonic: The sonic measurements are recorded at 20Hz and are quality checked. After quality control, both the 10min mean wind and the maximum gust speed for each interval are calculated. Here, the maximum of the 3s moving average is chosen as the maximum gust speed. This goes with the definition of wind gusts according to the WMO standard. Quality control of the sonic raw data follows Vickers and Mahrt (1997). It includes tests for non-physical values, constant values, and spikes. Constant values are eliminated, and spikes are detected and replaced by linearly interpolated values. Note that this spike-detection does not affect the identification of wind gusts, since it only removes significant outliers of at maximum three consecutive data values (corresponding to a duration of less than 0.2 s which is more than one magnitude shorter than the duration of a gust). Each sonic value is accompanied by a quality flag (qc_wind) where 0 = bad, and 1 = good. Units: Units for all variables (see TableOfContents): dlidcsm_level1: degrees; 1/ (m sr), m/s; m/s; 1; m; degrees dlidcsm_level2: Level2dwd: m/s; degrees; m/s; m/s; m/s; m/s; degrees; m/s; m/s; m/s; 1; 1; 1; 1; 1; 1; 1; 1; 1; percent; percent Level2uzk: m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; degrees; degrees; degrees; m²/s²; m²/s²; m²/s²; m/s; m/s; m/s sonic: m/s; m/s; m/s; 1; 1; 1; 1; m/s; degrees; m/s; m/s; 1 geoLocations: BoundingBox: westBoundLongitude: 14.122 degrees East; eastBoundLongitude: 14.192 degrees East; southBoundLatidude: 52.167 degrees North; northBoundLatitude: 52.209 degrees North; geoLocationPlace: Germany, UTM zone 33U Locations: Birkholz: 52.200 degrees North, 14.192 degrees East, 70 meters above mean sea level Falkenberg: 52.167 degrees North, 14.123 degrees East, 73 meters above mean sea level Lindenberg: 52.209 degrees North, 14.122 degrees East, 115 meters above mean sea level Size: All data are organized in daily files. For the ease of downloading all sonic, all Doppler LIDAR level-2, and Doppler LIDAR level-1 data of the three supersites are packed into one tar archive each; the total number of tar archives is hence 1 + 1 + 3. Files sizes of these archives are: sonic: ~3.6 MByte, Doppler LIDAR level-2: ~0.5 GByte, Doppler LIDAR level-1 Birkholz: ~26.1 GByte, Doppler LIDAR level-1 Falkenberg: ~34.4 GByte, Doppler LIDAR level-1 Lindenberg: ~13.1 GByte; the total amount is about 74 GByte. Format: netCDF DataSources: dlidcsm: Single site ground-based remote sensing, see "Technical Info" for instruments sonic: Single site tower-based in situ observations, see "Technical Info" for instruments Contact: general: carola.detring (at) dwd.de sonic: carola.detring (at) dwd.de Doppler LIDAR DWD processing: frank.beyrich (at) dwd.de Doppler LIDAR UzK processing: julian.steinheuer (at) uni-koeln.de Web page: LIDAR: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/fval-dlidcsm-wind-and-gust.html and SONIC: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-turb-l2-wind-and-gust.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html https://www.fdr.uni-hamburg.de/record/16243 10.25592/uhhfdm.16243 oai:fdr.uni-hamburg.de:16243 eng doi:10.25592/uhhfdm.9824 doi:10.25592/uhhfdm.9902 doi:10.5194/amt-15-3243-2022 doi:10.25592/uhhfdm.11227 url:https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html doi:10.5194/ems2022-184 doi:10.5194/amt-17-3187-2024 doi:10.25592/uhhfdm.16242 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Doppler LIDAR Ultrasonic Anemometer Wind speed Wind direction Wind gust Measurements FESSTVAL SAMD Ultrasonic anemometer and doppler lidar wind and gust data products during FESSTVAL 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:16509 2024-12-12T13:06:53Z user-uhh user-icdc user-cliccs user-cen

Sadikni, Remon Kern, Stefan 2024-12-12 Abstract: The soil moisture time series data of the extension of the EUMETSAT H-SAF product H119: H120 based on MetOp-B, and -C ASCAT data, processing version v7 (https://doi.org/10.15770/EUM_SAF_H_0009), are converted into geographic maps (cartesian grid) of daily running 5-day average/composite soil moisture (SM) distribution separately for ascending and descending overpasses. Two different 5-day SM distributions are given: one is based solely on nominally computed SM, the other one includes also those SM values which were negative (down to -25%, correction flag = 1) or positive (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. All data are interpolated into a cartesian grid of x- and y-dimensions of original grid. For more information see the respective global attribute in the netCDF file. TableOfContents: soil moisture; soil moisture noise; soil moisture extended; soil moisture extended noise; soil moisture status flag; number of overpasses per grid cell; historic probability of snow cover; historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD Technical Info: dimensons: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2022-01-01; temporalExtent_endDate: 2024-07-31; temporalResolution: daily; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.1125; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-B, MetOp-C); instrumentProvider: EUMETSAT, ESA; License: The following applies to the original product: All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products. Methods: For a description of the methods used to obtain the 5-day average / composite data we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi:10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.2, 2022; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.1, 2021; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v1.1, 2022. Units: units for all variables (see TableOfContents): percent, percent, percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: -90.0 degrees North; northBoundLongitude: 90.0 degrees North; geoLocationPlace: global over land Size: 730 files per year [note: there are 2 files per day, one for the ascending, one for the descending overpasses]; ~61.569 MegaByte per file; ~43.892 GigaByte per year (provided as two zip-files per year) Format: netCDF DataSources: Original Data as time series on a 12.5 km DGG Grid: https://hsaf.meteoam.it/Products/Detail?prod=H120 (last access: 2024-11-29); this original product comes with the following notion: "All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products." See also: http://hsaf.meteoam.it; https://hsaf.meteoam.it/Products/Detail?prod=H120 Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/16509 10.25592/uhhfdm.16509 oai:fdr.uni-hamburg.de:16509 eng info:eu-repo/semantics/altIdentifier/doi/10.25592/uhhfdm.8680 doi:10.15770/EUM_SAF_H_0009 url:http://hsaf.meteoam.it url:https://www.cen.uni-hamburg.de/en/icdc/land/ascat-soilmoisture.html doi:10.25592/uhhfdm.10467 doi:10.25592/uhhfdm.16510 doi:10.25592/uhhfdm.13101 doi:10.25592/uhhfdm.13100 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Daily Satellite Remote Sensing ASCAT MetOp-B/C EUMETSAT HSAF University of Vienna ICDC ASCAT Global Maps of daily running 5-day mean surface soil moisture - extension 2024 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:16510 2024-12-12T13:07:25Z user-uhh user-icdc user-cliccs user-cen

Sadikni, Remon Kern, Stefan 2024-12-12 Abstract: The globally gridded daily 5-day running mean surface soil moisture product derived at ICDC (https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html , https://doi.org/10.25592/uhhfdm.16509) from soil moisture time series data of the extension of the EUMETSAT H-SAF product H119: H120 based on MetOp-B, and -C ASCAT data, processing version v7 (https://doi.org/10.15570/EUM_SAF_H_0009), are averaged to obtain monthly means of the surface soil moisture (SM) distribution separately for ascending and descending overpasses. The monthly mean SM values include the nominally computed SM values as well as those SM values which were negative (down to -25%, correction flag = 1) or larger than 100% (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. The threshold for the monthly average is (the number of days per Month) 10. If there are fewer values per month, the value is set to the missing_value. For more information see the respective global attribute in the netCDF file. TableOfContents: mean soil moisture extended; mean soil moisture extended noise; number of valid soil moisture extended values per month; mean number of overpasses per grid cell; mean historic probability of snow cover; mean historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD; soil moisture status flag Technical Info: dimensions: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2022-01-01; temporalExtent_endDate: 2024-07-31; temporalResolution: monthly; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.11225; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-B, MetOp-C); instrumentProvider: EUMETSAT, ESA Methods: For a description of the methods used to obtain the daily 5-day running mean / composite data on which these monthly data are based, we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi: 10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.2, 2022; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.1, 2021; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v1.1, 2022. Units: Units for all variables (see TableOfContents): percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3, 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: 24 files per year [12 for ascending, 12 for descending overpasses]; ~56.439 MegaByte per file; ~1.3228 GigaByte in total (data are packed into two zip-archives per year, one for the ascending, one for the descending data) Format: netCDF DataSources: Gridded daily 5-day running mean surface soil moisture maps: https://doi.org/10.25592/uhhfdm.16509; see also https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html Original time-series of the surface soil moisture: https://hsaf.meteoam.it/Products/Detail?prod=H120 Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/16510 10.25592/uhhfdm.16510 oai:fdr.uni-hamburg.de:16510 eng doi:10.25592/uhhfdm.16509 url:http://hsaf.meteoam.it url:https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html doi:10.25592/uhhfdm.10468 doi:10.25592/uhhfdm.13103 doi:10.25592/uhhfdm.13102 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Monthly Satellite Remote Sensing ASCAT MetOp-B/C EUMETSAT HSAF University of Vienna ICDC ASCAT Global Maps of monthly mean surface soil moisture - extension 2024 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:12921 2025-01-14T15:21:22Z user-cliccs user-icdc user-cen

Sadikni, Remon Kern, Stefan 2023-07-14 Abstract: Original forest cover fraction, vegetation cover fraction and fraction of non-vegetated area on 250m grid resolution sinusoidal grid were obtained in HDF file format from https://lpdaac.usgs.gov/mod44bv061/, read together with the bit-encoded quality information and converted into netCDF file format with latitude/longitude coordinates of every 250m x 250m pixel, and decoded quality flag information included (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html). TableOfContents: forest cover fraction; other vegetation cover fraction; non-vegetated land cover fraction; forest cover fraction standard deviation; quality flag Technical Info: dimension: 4800 columns x 4800 rows x unlimited; temporalExtent_startDate: 2022-03-06; temporalExtent_endDate: 2023-03-05; temporalResolution: yearly; spatialResolution: 250; spatialResolutionUnit: meters; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: meters; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: meters; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] https://lpdaac.usgs.gov/products/mod44bv061/; [2] Townshend, J., et al., User Guide for the MODIS Vegetation Continuous Fields product Collection 6.1, verison 1, https://lpdaac.usgs.gov/documents/1494/MOD44B_User_Guide_V61pdf; [3] Algorithm Theoretical Basis Document (ATBD), https://lpdaac.usgs.gov/documents/113/MOD44B_ATBD.pdf; [4] Carroll, M., et al., 2011. Vegetative Cover Conversion and Vegetation Continuous Fields. In: Ramachandran, B., C. O. Justice, and M. Abrams (eds.), Land Remote Sensing and Global Environment Change: NASA's Earth Observing System and the Science of ASTER and MODIS. Springer Verlag.; [5] Hansen, M., et al., 2005. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Rem. Sens., 26(19), 4359-4380. Units: Units for all variables (see TableOfContents): percent; percent; percent; percent; 1 geoLocations: westBoundLongitude:depends on tile; eastBoundLongitude: depends on tile; southBoundLatitude: depends on tile; northBoundLatitude: depends on tile; geoLocationPlace: global on land, see: https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Size: files are packed into one zip-archive per year with an average size of about 25.6 GByte. Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD44B.061 (last accessed 2023-06-23), see also https://lpdaac.usgs.gov/products/mod44bv061/ (last accessed: 2023-16-16) Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html https://www.fdr.uni-hamburg.de/record/12921 10.25592/uhhfdm.12921 oai:fdr.uni-hamburg.de:12921 eng doi:10.5067/MODIS/MOD44B.061 doi:10.25592/uhhfdm.11198 url:https://lpdaac.usgs.gov/products/mod44bv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html doi:10.25592/uhhfdm.12922 doi:10.25592/uhhfdm.11197 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Forest Cover Fraction Vegetation Cover Fraction Sinusoidal grid tiles Yearly Satellite Remote Sensing MODIS EOS-Terra University of Maryland ICDC MODIS Collection 6.1 sinusoidal tiles yearly Forest and Vegetation Cover Fraction Extension 01 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10867 2025-02-03T16:51:20Z user-cen user-icdc user-cliccs user-uhh

Sadikni, Remon Kern, Stefan 2022-11-01 Abstract: Original LAI and FAPAR data (see https://lpdaac.usgs.gov/products/mod15a2hv061/) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the LAI and FAPAR data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html). For each tile, latitude and longitude information of the center of each 500 m x 500 m pixel is provided in a separate netCDF file. TableOfContents: FAPAR; LAI; FAPAR retrieval standard deviation; LAI retrieval standard deviation; Detailed quality flag; Quality flag for land; Quality flag for cloud and aerosol; Quality flag for method Technical Info: dimension: 2400 columns x 2400 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2021-12-31; temporalResolution: 8-daily; spatialResolution: 500; spatialResolutionUnit: meter; horizontalResolutionXdirection: 500; horizontalResolutionXdirectionUnit: meter; horizontalResolutionYdirection: 500; horizontalResolutionYdirectionUnit: meter; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] MODIS collection 6.1 (C61) LAI/FPAR Product User Guide, https://lpdaac.usgs.gov/documents/926/MOD15_User_Guide_V61.pdf ; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document (ATBD), v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf; [3] Yang, et al., From validation to algorithm improvement. Trans. Geosci. Rem. Sens., 44, 1885-1898, 2006; [4] Morisette, et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, Trans. Geosci. Rem. Sens., 44, 1804-1817, 2006; [5] Garrigues, et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008; [6] https://icdc.cen.uni-hamburg.de/en/modis-lai-fpar.html Units: Units for all variables (see TableOfContents): percent, m2/m2, percent, m2/m2, 1, 1, 1, 1 geoLocations: westBoundLongitude:depends on tile; eastBoundLongitude: depends on tile; southBoundLatitude: depends on tile; northBoundLatitude: depends on tile; geoLocationPlace: global on land, see: https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Size: (files are packed into one zip-file per year) 2000: 270 x 40 files, 92.169 Mbyte / file 2001: 270 x 44 files (20010618 and 20010626 are missing) 2002: 270 x 35 files (20020101 until 20020322 are missing) 2003-2021: 270 x 46 files / year Latitude/Longitude: 291 files, 46.08 Mbyte / file Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD15A2H.061 [last access: 2022-01-17], see also: https://lpdaac.usgs.gov/products/mod15a2hv061/ [last access: 2022-01-16] Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2022-10-26] https://www.fdr.uni-hamburg.de/record/10867 10.25592/uhhfdm.10867 oai:fdr.uni-hamburg.de:10867 eng doi:10.5067/MODIS/MOD15A2H.061 url:https://lpdaac.usgs.gov/products/mod15a2hv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html doi:10.25592/uhhfdm.10863 doi:10.25592/uhhfdm.11776 doi:10.25592/uhhfdm.10866 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Leaf Area Index LAI FAPAR Sinusoidal Grid Tiles 8-daily Satellite Remote Sensing MODIS EOS-Terra Boston University ICDC MODIS Collection 6.1 Sinusoidal Tiles 8-daily LAI and FAPAR info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10863 2025-02-03T16:51:31Z user-cen user-icdc user-uhh user-cliccs

Sadikni, Remon Kern, Stefan 2022-10-26 Abstract: Original LAI and FAPAR data (see https://lpdaac.usgs.gov/products/mod15a2hv061/) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the LAI and FAPAR data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html and https://doi.org/10.25592/uhhfdm.10867). These are subsequently read and re-gridded onto an equi-rectangular climate modeling grid (CMG). Only those LAI and FAPAR values are used where i) the cloud flag indicates a maximum of two cloud influences, and where ii) cloud cover is clearly defined, i.e. "assumed clear sky" is not used. Flag layers are summarized such that there is gridded information about 1) cloud fraction, 2) fraction of average and high aerosol load, 3) primary and secondary land-cover type, and 4) primary and secondary quality flag. Primary and secondary refer to the highest and 2nd-highest pixel count of the respective type or flag within the grid cell. Note that the count of valid values differs for the grid cell mean LAI and FAPAR and their variance, and for the grid-cell mean retrieval standard deviation. TableOfContents: Grid cell mean FAPAR; Grid cell mean LAI; FAPAR variance across grid cell; LAI variance across grid cell; Grid cell mean FAPAR retrieval standard deviation; Grid cel mean LAI retrieval standard deviation; Count of useful FAPAR or LAI values per grid cell; Count of useful FAPAR or LAI retrieval standard deviation values in grid cell; Primary quality flag; Secondary quality flag; Primary land cover; Secondary land cover; Grid cell cloud fraction; Grid cell aerosol fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2021-12-31; temporalResolution: 8-daily; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] MODIS collection 6.1 (C61) LAI/FPAR Product User Guide, https://lpdaac.usgs.gov/documents/926/MOD15_User_Guide_V61.pdf ; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document (ATBD), v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf; [3] Yang, et al., From validation to algorithm improvement. Trans. Geosci. Rem. Sens., 44, 1885-1898, 2006; [4] Morisette, et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, Trans. Geosci. Rem. Sens., 44, 1804-1817, 2006; [5] Garrigues, et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008; [6] https://icdc.cen.uni-hamburg.de/en/modis-lai-fpar.html Units: Units for all variables (see TableOfContents): percent, m2/m2, percent, m4/m4, percent, m2/m2, 1, 1, 1, 1, 1, 1, percent, percent geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: (files are packed into one zip-file per year) 2000: 40 files, 9864980 byte / file 2001: 44 files (20010618 and 20010626 are missing) 2002: 35 files (20020101 until 20020322 are missing) 2003-2021: 46 files / year Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD15A2H.061 [last access: 2022-01-17], see also: https://lpdaac.usgs.gov/products/mod15a2hv061/ [last access: 2022-01-16] Data on sinusoidal grid tiles in netCDF-format: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2022-10-26] and https://doi.org/10.25592/uhhfdm.10867 [last access: 2022-10-28] Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2022-10-26] https://www.fdr.uni-hamburg.de/record/10863 10.25592/uhhfdm.10863 oai:fdr.uni-hamburg.de:10863 eng doi:10.25592/uhhfdm.10867 url:https://lpdaac.usgs.gov/products/mod15a2hv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html doi:10.25592/uhhfdm.8585 doi:10.25592/uhhfdm.11777 doi:10.25592/uhhfdm.8584 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Leaf Area Index LAI FAPAR Global Maps 8-daily Satellite Remote Sensing MODIS EOS-Terra Boston University ICDC MODIS Collection 6.1 global 8-daily LAI and FAPAR info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:17022 2025-03-23T19:22:25Z user-icdc user-cen user-uhh

Sadikni, Remon Kern, Stefan Sobottke , Vincent Rust, Henning Goeber, Martin Boettcher, Christopher Lehmke, Jonas 2025-03-20 Abstract: The data set contains meteorological data from a citizen science network that was part of the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) from May to September 2021. The network consists of 56 stations that were mostly set up in gardens of citizen participants of the project around the Meteorological Observatory Lindenberg - Richard-Aßmann-Observatory (MOL-RAO eastern Germany; 52.16°N, 14.12°E). A map of the positions of the stations can be found in this report. One of the goals was to investigate to what extent a data set gathered from a citizen science network can add value in addition to the network of the MOL-RAO and professional networks in general. The main subjects of investigation were sub-mesoscale structures such as cold pools. The 56 stations were equipped with devices called MESSI = "Mein Eigenes SubSkalen Instrument", which is a low-cost, autonomous device planned and built at Freie Universität Berlin for the use case of building meteorological Citizen Science networks. The data set comprises level-1 data, measured every 10 seconds, and level-2 data for which the level-1 data were aggregated to 1-minute time periods. TableOfContents: Air temperature inside device; Air temperature outside device; Air temperature derived from statistical model; Surface air pressure at station level; Surface downwelling illuminance Technical Info: dimension: level-1: 8640 x 56, level-2: 1440 x 56; temporalExtent_startDate: 2021-06-10 00:00:10; temporalExtent_endDate: 2021-10-01 00:00:00; temporalResolution: level-1: 10, level-2: 60; temporalResolutionUnit: seconds; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: PT1000 Heraeus M222 ADS114SOxB, BMP38x, Si1151/1145; instrumentType: Resistive temperature sensor, pressure sensor, illuminance sensor; instrumentLocation: Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Texas Instruments, Bosch Sensortec, Silicon Labs Methods: The MESSI measures air temperature, air pressure, surface downwelling illuminance and humidity (humidity data is not being published due to issues with the respective sensor during the campaign). The original time resolution is 10 seconds. As a part of the data processing a statistical model was used to establish a relation to a reference station and use this to adjust the measurements such that bias and conditional bias with respect to a DWD reference is reduced. For this processing step we used reference data of a 1-minute resolution from a ventilated station at MOL-RAO provided by Deutscher Wetterdienst (DWD, no final quality control applied) and similarly aggregated observation data of one particular MESSI position next to the DWD device. Subsequently, this model was applied to all MESSI data to get more accurate values for the air temperature than measured directly. Because the script for quality control and statistical modelling has been optimized for the 1-minute time resolution data it can be assumed that these are more accurate - especially the 3rd temperature quantity: "Air temperature derived from statistical model". As another step of preprocessing several quality controls were applied. All measurements that did not pass these quality controls have been excluded from the dataset (values set to missing values). The MESSI device and data processing including the statistical modelling and the applied quality controls is described in more detail in [http://dx.doi.org/10.17169/refubium-42772]. Quality: Absolute accuracy according to manufacturers of sensors is for the air temperature inside and outside device::+/- 0.15 K and for the surface air pressure at station level: +/- 50 Pa. The actual accuracy is probably lower due to assembly of the devices by the participants and other factors. In the modelling procces a root-mean-square-error of 0.64 K was estimated when comparing the value of the variable "air temperature inside device" of the MESSI with the reference sensor. The total data availability is 53%. This is because of the filters mentioned above, failures in data transfer (see below) and mainly the fact that measurements started and ended at different times for the different stations. Best data availability is given in July and August (72% and 73%). At some stations the MESSI had to be exchanged during the campaign due to technical issues. In some cases it can not be guaranteed that the device was measuring at the respective station position (possible untracked movement of device by citizen participant). The MESSIs did send their GPS-position to the MESSI database via "LoRaWan" and the "The Things Network" once per day. But in some cases this data transfer failed, leading to some uncertainty of the station position. These and other information can be found in fesstval_messi_station_list.csv Units: K; K; K; Pa; lux geoLocations: BoundingBox: westBoundLongitude: 13° 43' 28.56'' East; eastBoundLongitude: 14° 25' 59.519'' East; southBoundLatitude: 52° 5' 31.2'' North; northBoundLatitude: 52° 18' 4.68'' North; geoLocationPlace: Germany, UTM zone 33U Locations: 56 station locations within BoundingBox (see fesstval_messi_station_list.csv); lowerBoundAltitude: 38 meter above MSL; upperBoundAltitude: 98 meter above MSL Size: Daily files for each quantity provided as one packed tar-archive for either level-1 or level-2 data; total volume level-1 data: 1.23 Gb; total volume level-2 data: 0.37 Gb; total volume of the packed files is about 1/5 of that. Format: netCDF DataSources: Multiple site near-surface observations (see fesstval_messi_station_list.csv) Contact: henning.rust (at) fu-berlin.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/fval-fub-messi.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html https://www.fdr.uni-hamburg.de/record/17022 10.25592/uhhfdm.17022 oai:fdr.uni-hamburg.de:17022 eng doi:10.25592/uhhfdm.9824 doi:10.25592/uhhfdm.9902 doi:10.17169/refubium-42772 doi:10.25592/uhhfdm.17021 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Atmosphere Measurements near surface temperature surface downwelling Illuminance near surface air pressure Citizen Science FESSTVAL SAMD Meteorological network observations by MESSI weather stations during FESSTVaL 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:17942 2025-09-12T10:28:44Z user-icdc user-cen user-uhh

Aziz, Proshonni Sadikni, Remon Kern, Stefan 2025-09-10 Abstract: In the framework of the European Space Agency (ESA) Climate Change Initiative Phase 2 (CCI+) sea ice essential climate variable (ECV) project (SICCI) [Climate Change Initiative Sea_Ice_cci_Project] a suite of Landsat images of both hemispheres were used to evaluate sea-ice concentration (SIC) products based on satellite microwave radiometry. First, surface broadband albedo values were estimated based on channels 3,4,5 of the Landsat-8 OLI sensor. Secondly, a supervised classification was employed, classifying the broadband albedo maps into open water, thin/bare ice and thick/snow-covered ice. Thresholds used for the classification are less or equal than 0.07 and greater or than 0.4 for the open water / thin ice and thin ice / thick ice transition, respectively. Metadata files are given for both hemispheres including the sun zenith angle of the Landsat-8 OLI image and the two thresholds used. Resulting maps have been quality checked for artifacts due to cloudy pixels and double scenes. Note that there might a few maps with a slight overlap from two adjacent Landsat images. TableOfContents: surface type flag (0: open water, 1: thin or bare sea ice, 2: thick or snow-covered ice, 127: missing data or clouds) Technical Info: dimensions: nominal: 6166 columns x 6000 rows x unlimited; dimensions actual: variable, depends on how the Landsat scene fits into a rectangular bounding box determined by the minimum and maximum values of latitude and longitude of each scene; temporalExtent_startDate: 2019-01-01; temporalExtent_endDate: 2020-12-31; temporalResolution: ~28 s / image; spatialResolution: 30; spatialResolutionUnit: meters; horizontalResolutionXdirection: 30; horizontalResolutionXdirectionUnit: meters; horizontalResolutionYdirection: 30; horizontalResolutionYdirectionUnit: meters; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Landsat-8: Operational Land Imager (OLI); instrumentType: optical sensor; instrumentLocation: Landsat-8; instrumentProvider: NASA Methods: [1] http://esa-cci.nersc.no/?q=documents#/Public/Documents from phase 2/D4.1_SICCI_P2_PVIR-SIC_Issue_1.1.pdf; [2] Knap, W. H., Brock, B. W., Oerlemans, J., and Willis, I. C.: Comparison of Landsat TM-derived and ground-based albedos of Haut Glacier d Arolla, Switzerland. Int. J. Rem. Sens., 20(17), 3293-3310, 1999; [3] Koepke, P., Removal of Atmospheric Effects from AVHRR albedos, J. Appl. Meteorol., 28, 1341-1348, 1989; [4] Barsi, J. A., Kenton, L., Kvaran, G., Markham, B. L., and Pedelty, J. A.: The spectral response of the Landsat-8 operational land imager. Rem. Sens., 6(10), 10232-10251, https://doi.org/10.3390/rs61010232, 2014; [5] Zatko, M. C., and Warren, S. G.: East Antarctic sea ice in spring: spectral albedo of snow, nilas, frost flowers and slush, and light-absorbing impurities in snow. Ann. Glaciol., 56(69), 53-64, https://doi.org/10.3189/2015AoG69A574, 2015. Units: 1 geoLocations: Northern Hemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: 50.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: Northern Hemisphere over water Southern Hemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -80.0 degrees North; northBoundLatitude: -60.0 degrees North; geoLocationPlace: Southern Hemisphere over water Size: (files are packed into one zip-file per year for the Northern Hemisphere and into one zip-file per month for the Southern Hemisphere) Northern Hemisphere: 80 files in total; about 67 Gbyte (zipped: 27.6 Gbyte) in total Southern Hemisphere: 209 files in total; about 167 Gbyte (zipped: 75.1 Gbyte) in total Format: netCDF DataSources: https://earthexplorer.usgs.gov/ [last access: 2025-09-09] Contact: stefan.kern (at) uni-hamburg.de https://www.fdr.uni-hamburg.de/record/17942 10.25592/uhhfdm.17942 oai:fdr.uni-hamburg.de:17942 eng url:https://earthexplorer.usgs.gov/ doi:10.25592/uhhfdm.9181 doi:10.25592/uhhfdm.17941 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Polar Oceans Sea Ice Supervised Classification Satellite Remote Sensing Landsat-8 OLI USGS ICDC Landsat surface type over water from supervised classification of surface broadband albedo estimates - Part II info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:18070 2026-01-09T11:34:16Z user-cen user-icdc user-cliccs user-uhh

Sadikni, Remon Kern, Stefan 2025-12-09 Abstract: Reflectance measurements carried out at Terra MODIS bands 1, 3 and 4 are combined in a spectral un-mixing approach to estimate the melt-pond fraction on Arctic sea ice north of 60°N for months June through August for the years 2000 through 2024. The resulting data set has daily temporal sampling and is offered on a polar-stereographic grid (true latitude is 70°N) with 500 m x 500 m and 12.5 km x 12.5 km grid resolution. The data files contain, in addition to the melt-pond fraction, also the fraction of open water between the sea ice and the net sea-ice surface fraction, i.e. the fraction of sea ice without melt ponds. For information about the approach used and the validation of the data set offered here we refer to Rösel et al. (2012) and Sadikni and Kern (2025). TableOfContents: 500 m x 500 m product: grid-cell fraction of melt ponds (on sea ice); grid-cell fraction of sea ice without melt ponds; grid-cell fraction of open water 12.5 km x 12.5 km product: grid-cell fraction of melt ponds (on sea ice); grid-cell fraction of sea ice without melt ponds; grid-cell fraction of open water; grid-cell_fraction_of_melt_ponds standard_deviation; grid-cell_fraction_of_sea_ice_without_melt_ponds standard_deviation; grid-cell_fraction_of_open_water standard_deviation; number of clear-sky 500 m grid cells; clear-sky mask While the 12.5 km x 12.5 km comes with latitude and longitude values for each grid cell, those of the 500 m x 500 m product are provided in a separate netCDF file. Note that the grid resolution of 12.5 km (and 500m) is only valid at the latitude at which the tangential plane of the polar stereographic projection touches the Earth's surface - here 70°N. North of this latitude the actual grid cell area is smaller, south of it it is larger (see also https://nsidc.org/data/user-resources/help-center/guide-nsidcs-polar-stereographic-projection#anchor-grid-distortion). In order to assist in the computation of the actual area (in km2) covered by melt ponds (or the other two surface types) we provide a netCDF file of the true area of each grid cell of the 12.5 km x 12.5 km product. Technical Info: 500 m x 500 m product: dimensions: 13286 columns x 13293 rows; temporalExtent_startDate: 2000-06-01; temporalExtent_endDate: 2024-08-31; temporalResolution: daily; spatialResolution: 500; spatialResolutionUnit: meter; horizontalResolutionXdirection: 500; horizontalResolutionXdirectionUnit: meter; horizontalResolutionYdirection: 500; horizontalResolutionYdirectionUnit: meter; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Moderate Resolution Spectroradiometer (MODIS); instrumentType: 32-band imaging spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA 12.5 km x 12.5 km product: dimensions: 531 columns x 531 rows; temporalExtent_startDate: 2000-06-01; temporalExtent_endDate: 2024-08-31; temporalResolution: daily; spatialResolution: 12500; spatialResolutionUnit: meter; horizontalResolutionXdirection: 12500; horizontalResolutionXdirectionUnit: meter; horizontalResolutionYdirection: 12500; horizontalResolutionYdirectionUnit: meter; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Moderate Resolution Spectroradiometer (MODIS); instrumentType: 32-band imaging spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Missing data (either because the MOD09GA product has gaps or because the melt-pond fraction retrieval failed): 2000-08-06 to 2000-08-17; 2001-06-16 to 2001-07-02; 2002-06-15 to 2002-06-19; 2002-07-11; 2004-06-20 to 2004-06-22; 2005-07-21; 2008-08-06 to 2008-08-08; 2008-08-11; 2008-08-22; 2010-06-17; 2010-08-01; 2010-08-16; 2013-06-09; 2013-06-15; 2014-08-02; 2016-06-07; 2016-06-08; 2016-06-11; 2016-08-12; 2018-08-04; 2021-08-11; 2024-06-21; 2024-06-22; 2024-06-25. Methods: For a description of the methods used see Sadikni and Kern, 2025. Units: 500 m x 500 m product: 1, 1, 1 12500 m x 12500 m product: 1, 1, 1, 1, 1, 1, 1, 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: 60.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: northern hemisphere Size: 500 m x 500 m product: Single file: 2.1 GigaByte; zip-file or one month: 1.5 to 4.0 GigaByte (depends on gaps due to cloud cover); total volume (as unpacked netCDF): 4.87 TeraByte 12.5 km x 12.5 km product: Single file: ~10 MegaByte; zip-file of one month: 60 to 100 MegaByte (depends on gaps due to cloud cover); total volume (as unpacked netCDF): 22.8 GigaByte Format: netCDF DataSources: MODerate resolution Imaging Spectroradiometer MODIS aboard the Earth Observation Satellite (EOS) Terra collection 6.1 product MOD09A1 of the surface spectral reflectance: https://lpdaac.usgs.gov/products/mod09gav061/ (last access: Dec. 6, 2024). Contact: remon.sadikni (at) uni-hamburg.de; stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/arctic-meltponds.html https://www.fdr.uni-hamburg.de/record/18070 10.25592/uhhfdm.18070 oai:fdr.uni-hamburg.de:18070 eng doi:10.5194/tc‐6‐431‐2012 doi:10.1594/WDCC/MODIS__Arctic__MPF_V02 doi:10.25592/uhhfdm.18069 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode sea ice melt ponds Arctic surface type fraction summer melt satellite remote sensing mixed pixel approach artificial neural network MODIS ICDC Daily melt-pond fraction on Arctic sea ice from TERRA MODIS visible imagery info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:18381 2026-02-24T13:42:19Z user-icdc user-cen user-uhh user-cliccs

Sadikni, Remon Kern, Stefan 2026-02-24 Abstract: Original LAI and FAPAR data (see https://lpdaac.usgs.gov/products/mod15a2hv061/) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the LAI and FAPAR data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html). For each tile, latitude and longitude information of the center of each 500 m x 500 m pixel is provided in a separate netCDF file. TableOfContents: FAPAR; LAI; FAPAR retrieval standard deviation; LAI retrieval standard deviation; Detailed quality flag; Quality flag for land; Quality flag for cloud and aerosol; Quality flag for method Technical Info: dimension: 2400 columns x 2400 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2025-12-31; temporalResolution: 8-daily; spatialResolution: 500; spatialResolutionUnit: meter; horizontalResolutionXdirection: 500; horizontalResolutionXdirectionUnit: meter; horizontalResolutionYdirection: 500; horizontalResolutionYdirectionUnit: meter; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] MODIS collection 6.1 (C61) LAI/FPAR Product User Guide, https://lpdaac.usgs.gov/documents/926/MOD15_User_Guide_V61.pdf ; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document (ATBD), v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf; [3] Yang, et al., From validation to algorithm improvement. Trans. Geosci. Rem. Sens., 44, 1885-1898, 2006; [4] Morisette, et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, Trans. Geosci. Rem. Sens., 44, 1804-1817, 2006; [5] Garrigues, et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008; [6] https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html Units: Units for all variables (see TableOfContents): percent, m2/m2, percent, m2/m2, 1, 1, 1, 1 geoLocations: westBoundLongitude:depends on tile; eastBoundLongitude: depends on tile; southBoundLatitude: depends on tile; northBoundLatitude: depends on tile; geoLocationPlace: global on land, see: https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Size: (files are packed into one zip-file per year) 2000: 270 x 40 files, 92.169 Mbyte / file 2001: 270 x 44 files (20010618 and 20010626 are missing) 2002: 270 x 35 files (20020101 until 20020322 are missing) 2003-2025: 270 x 46 files / year Latitude/Longitude: 291 files, 46.08 Mbyte / file Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD15A2H.061 [last access: 2026-01-12], see also: https://lpdaac.usgs.gov/products/mod15a2hv061/ [last access: 2026-01-12] Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2026-02-24] https://www.fdr.uni-hamburg.de/record/18381 10.25592/uhhfdm.18381 oai:fdr.uni-hamburg.de:18381 eng doi:10.5067/MODIS/MOD15A2H.061 url:https://lpdaac.usgs.gov/products/mod15a2hv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html doi:10.25592/uhhfdm.16751 doi:10.25592/uhhfdm.18378 doi:10.25592/uhhfdm.10866 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Leaf Area Index LAI FAPAR Sinusoidal Grid Tiles 8-daily Satellite Remote Sensing MODIS EOS-Terra Boston University ICDC MODIS Collection 6.1 Sinusoidal Tiles 8-daily LAI and FAPAR info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10179 2023-01-25T09:34:30Z user-icdc

Kirsch, Bastian Hohenegger, Cathy Klocke, Daniel Ament, Felix 2022-05-02 This data set contains meteorological network observations collected during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) from May to August 2021. The observational set up consisted of a ground-based network of 99 autonomous measurement stations that covered an circular area of 30 km in diameter centered around the Meteorological Observatory Lindenberg (eastern Germany; 52.16°N, 14.12°E). The primary goal of the network measurements was to observe the spatial structure of convective cold pools at sub-mesoscale resolution (100 m - 10 km). During the experiment, 82 low-cost and custom-designed APOLLO (Autonomous cold POoL LOgger) stations sampled air temperature and pressure at 1-s resolution, while 21 WXT weather stations based on commercial sensors provided additional information on relative humidity, wind speed and precipitation at 10-s resolution. The data of all network stations is stored in daily files separated after station type and measurement variable. Quality: Absolute accuracy of temperature (pressure) generally better than +/- 0.5 K (1 hPa), however, the instrument design focused on the relative accuracy of measurements. The overall data availability of temperature measurements for APOLLO (WXT) stations is 92.0 % (98.1 %). Maintenance logbook and site picutures are attached for further interpretation of measurements. Instruments: 80x APOLLO (Autonomous cold POoL LOgger) station 19x WXT weather station Location: Circular area of about 30 km in diameter centered around the Meteorological Observatory Lindenberg (eastern Germany; latitude 52.16°N, longitude 14.12°E) https://www.fdr.uni-hamburg.de/record/10179 10.25592/uhhfdm.10179 oai:fdr.uni-hamburg.de:10179 eng doi:10.25592/uhhfdm.9765 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode observation network temperature pressure cold pool convection mesoscale FESSTVal measurement SAMD Meteorological network observations by APOLLO and WXT weather stations during FESSTVaL 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10196 2022-11-07T14:05:53Z user-icdc user-cen user-cliccs

Sadikni, Remon Jahnke-Bornemann, Annka Kern, Stefan 2022-05-12 Abstract: The globally gridded daily 5-day running mean surface soil moisture product derived at ICDC (https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html , https://doi.org/10.25592/uhhfdm.10195) from soil moisture time series data of the EUMETSAT H-SAF product H115 and its extension H116 based on MetOp-A and -B ASCAT data, processing version v5, are averaged to obtain monthly means of the surface soil moisture (SM) distribution separately for ascending and descending overpasses. The monthly mean SM values include the nominally computed SM values as well as those SM values which were negative (down to -25%, correction flag = 1) or larger than 100% (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. The threshold for the monthly average is (the number of days per Month) 10. If there are fewer values per month, the value is set to the missing_value. For more information see the respective global attribute in the netCDF file. TableOfContents: mean soil moisture extended; mean soil moisture extended noise; number of valid soil moisture extended values per month; mean number of overpasses per grid cell; mean historic probability of snow cover; mean historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD; soil moisture status flag Technical Info: dimensons: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2007-01-01; temporalExtent_endDate: 2021-12-31; temporalResolution: monthly; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.11225; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-A, MetOp-B); instrumentProvider: EUMETSAT, ESA Methods: For a description of the methods used to obtain the daily 5-day running mean / composite data on which these monthly data are based, we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi: 10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.1, 2019; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling ( H115) and Extension (H116), v0.1, 2019; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.3, 2019. Units: Units for all variables (see TableOfContents): percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3, 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: 24 files per year [12 for ascending, 12 for descending overpasses]; ~56.439 MegaByte per file; ~19.873 GigaByte in total (data are packed into two zip-archives per year, one for the ascending, one for the descending data) Format: netCDF DataSources: Gridded daily 5-day running mean surface soil moisture maps: https://doi.org/10.25592/uhhfdm.10195; see also https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html Original time-series of the surface soil moisture: https://doi.org/10.15770/EUM_SAF_H_0006 Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/10196 10.25592/uhhfdm.10196 oai:fdr.uni-hamburg.de:10196 eng doi:10.25592/uhhfdm.10195 doi:10.15770/EUM_SAF_H_0006 url:http://hsaf.meteoam.it url:https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html doi:10.25592/uhhfdm.8989 doi:10.25592/uhhfdm.8682 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Monthly Satellite Remote Sensing ASCAT MetOp-A/B EUMETSAT HSAF ICDC ASCAT Global Maps of monthly mean surface soil moisture info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:9903 2023-01-25T09:15:50Z user-cen user-icdc

Jahnke-Bornemann, Annika 2022-02-08 The SAMD light data-product description-document includes the conventions for file names, variables and NetCDF-files. The standardized XML-file convention is included as well as all necessary abbreviations for institutes, instruments, variables, etc. https://www.fdr.uni-hamburg.de/record/9903 10.25592/uhhfdm.9903 oai:fdr.uni-hamburg.de:9903 eng doi:10.25592/uhhfdm.9902 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode SAMD FESSTVaL data The SAMD Product Standard (Standardized Atmospheric Measurement Data) info:eu-repo/semantics/technicalDocumentation publication-technicalnote

oai:fdr.uni-hamburg.de:10148 2023-01-25T09:34:30Z user-icdc user-uhh

Wildmann, Norman 2022-03-31 This data set contains aggregated measurements from a fleet of multicopter UAS. The data was measured during the period 21 June 2021 through 02 July 2021 at the GM Falkenberg with the SWUF-3D fleet. The data was collected in association to the FESSTVaL field campaign. The data is structured as one file per UAS (level 1) as well as aggregated data from all UAS operating simultaneously in specific flights (level 2). For level-2 data, time synchronization between individual UAS was done through interpolation. The different flight pattern are named after this convention: horizontal pattern: 'swuf3dhori' vertival (tower) pattern: 'swuf3dvt' calibration pattern: 'swuf3dcalib' VAD validation pattern: 'swuf3dvad' vertical profiles: 'swuf3dvpro' Quality: Uncertainty of wind measurement was validated to below 0.3 m/s. Temperature measurement radiation error was corrected with data from the radiation sensor sups_rao_rad02_l1. Variables: zonal_wind, m/s, 2021-06-21, 2021-07-02 meridional_wind, m/s, 2021-06-21, 2021-07-02 upward_air_velocity, m/s, 2021-06-21, 2021-07-02 wind_speed, m/s, 2021-06-21, 2021-07-02 wind_from_direction, degree, 2021-06-21, 2021-07-02 air_pressure, Pa, 2021-06-21, 2021-07-02 air_temperature, K, 2021-06-21, 2021-07-02 relative_humidity, %, 2021-06-21, 2021-07-02 potential_temperature, K, 2021-06-21, 2021-07-02 humidity_mixing_ratio, g kg-1, 2021-06-21, 2021-07-02 https://www.fdr.uni-hamburg.de/record/10148 10.25592/uhhfdm.10148 oai:fdr.uni-hamburg.de:10148 eng doi:10.25592/uhhfdm.10147 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode SAMD FESSTVaL wind measurement Multicopter UAS measurements at GM Falkenberg during FESSTVaL 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8973 2023-01-25T09:34:29Z user-cen user-icdc

Kirsch, Bastian Hohenegger, Cathy Klocke, Daniel Senke, Rainer Offermann, Michael Ament, Felix 2021-03-18 This data set contains meteorological measurement data collected during the Field Experiment on Sub-mesoscale Spatio-Temporal variability at Hamburg (FESST@HH) between June and August 2020. The observational set up of FESST@HH consisted of a ground-based network of 103 autonomous measurement stations (see photos), that covered the greater area (50 km x 35 km) of Hamburg (Germany; 53.5 °N 10.0 °E) with the primary goal to observe the spatial dimension of convective cold pools. During the experiment 82 low-cost and self-designed APOLLO (Autonomous cold POoL LOgger) stations sampled air temperature and pressure at 1-s resolution, while 21 WXT weather stations with commercial sensors provided additional information on relative humidity, wind speed and precipitation at 10-s resolution. All variables are sampled at a height of 3 m above ground, if not indicated otherwise. https://www.fdr.uni-hamburg.de/record/8973 10.25592/uhhfdm.8973 oai:fdr.uni-hamburg.de:8973 eng doi:10.25592/uhhfdm.8966 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode atmosphere meteorological measurements urban measurements network cold pool temperature pressure FESST@HH meteorological network measurements info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8989 2022-11-07T14:05:52Z user-cliccs user-cen user-icdc

Sadikni, Remon Jahnke-Bornemann, Annka Kern, Stefan 2021-04-09 Abstract: The globally gridded daily 5-day running mean surface soil moisture product derived at ICDC (https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html , https://doi.org/10.25592/uhhfdm.8988) from soil moisture time series data of the EUMETSAT H-SAF product H115 and its extension H116 based on MetOp-A and -B ASCAT data, processing version v5, are averaged to obtain monthly means of the surface soil moisture (SM) distribution separately for ascending and descending overpasses. The monthly mean SM values include the nominally computed SM values as well as those SM values which were negative (down to -25%, correction flag = 1) or larger than 100% (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. The threshold for the monthly average is (the number of days per Month) 10. If there are fewer values per month, the value is set to the missing_value. For more information see the respective global attribute in the netCDF file. TableOfContents: mean soil moisture extended; mean soil moisture extended noise; number of valid soil moisture extended values per month; mean number of overpasses per grid cell; mean historic probability of snow cover; mean historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD; soil moisture status flag Technical Info: dimensons: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2007-01-01; temporalExtent_endDate: 2020-12-31; temporalResolution: monthly; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.11225; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-A, MetOp-B); instrumentProvider: EUMETSAT, ESA Methods: For a description of the methods used to obtain the daily 5-day running mean / composite data on which these monthly data are based, we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi: 10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.1, 2019; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling ( H115) and Extension (H116), v0.1, 2019; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.3, 2019. Units: Units for all variables (see TableOfContents): percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3, 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: 24 files per year [12 for ascending, 12 for descending overpasses]; ~56.439 MegaByte per file; ~18.519 GigaByte in total (data are packed into two zip-archives per year, one for the ascending, one for the descending data) Format: netCDF DataSources: Gridded daily 5-day running mean surface soil moisture maps: https://doi.org/10.25592/uhhfdm.8988; see also https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html Original time-series of the surface soil moisture: https://doi.org/10.15770/EUM_SAF_H_0006 Contact: stefan.kern (at) uni-hamburg.de Web page: https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/8989 10.25592/uhhfdm.8989 oai:fdr.uni-hamburg.de:8989 eng doi:10.25592/uhhfdm.8988 doi:10.15770/EUM_SAF_H_0006 url:http://hsaf.meteoam.it url:https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html doi:10.25592/uhhfdm.8683 doi:10.25592/uhhfdm.8682 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Monthly Satellite Remote Sensing ASCAT MetOp-A/B EUMETSAT HSAF ICDC ASCAT Global Maps of monthly mean surface soil moisture info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8988 2022-11-07T13:42:24Z user-cliccs user-cen user-icdc

Sadikni, Remon Jahnke-Bornemann, Annika Kern, Stefan 2021-04-09 Abstract: The soil moisture time series data of the EUMETSAT H-SAF product H115 and its extension H116 based on MetOp-A and -B ASCAT data, processing version v5, are converted into geographic maps (cartesian grid) of daily running 5-day average/composite soil moisture (SM) distribution separately for ascending and descending overpasses. Two different 5-day SM distributions are given: one is based solely on nominally computed SM, the other one includes also those SM values which were negative (down to -25%, correction flag = 1) or positive (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. All data are interpolated into a cartesian grid of x- and y-dimensions of original grid. For more information see the respective global attribute in the netCDF file. TableOfContents: soil moisture; soil moisture noise; soil moisture extended; soil moisture extended noise; soil moisture status flag; number of overpasses per grid cell; historic probability of snow cover; historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD Technical Info: dimensons: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2007-01-01; temporalExtent_endDate: 2020-12-31; temporalResolution: daily; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.1125; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-A, MetOp-B); instrumentProvider: EUMETSAT, ESA; License: The following applies to the original product: All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products. Methods: For a description of the methods used to obtain the 5-day average / composite data we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi:10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.1, 2019; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling ( H115) and Extension (H116), v0.1, 2019; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling (H115) and Extension (H116), v0.3, 2019. Units: units for all variables (see TableOfContents): percent, percent, percent, percent, 1, 1, percent, percent, percent, percent, m3/m3, m3/m3 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: -90.0 degrees North; northBoundLongitude: 90.0 degrees North; geoLocationPlace: global over land Size: 730 (leap year: 732) files per year [note: there are 2 files per day, one for the ascending, one for the descending overpasses]; ~61.569 MegaByte per file; ~43.892 GigaByte per year; ~614.970 GigaByte in total (provided as two zip-files per year) Format: netCDF DataSources: Original Data as time series on a 12.5 km DGG Grid: https://doi.org/10.15770/EUM_SAF_H_0006 (last access: 2021-04-01); this original product comes with the following notion: "All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products." See also: http://hsaf.meteoam.it; https://navigator.eumetsat.int/product/EO:EUM:DAT:METOP:H115; https://navigator.eumetsat.int/product/EO:EUM:DAT:METOP:H116 Contact: stefan.kern (at) uni-hamburg.de Web page: https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html https://www.fdr.uni-hamburg.de/record/8988 10.25592/uhhfdm.8988 oai:fdr.uni-hamburg.de:8988 eng doi:10.15770/EUM_SAF_H_0006 url:http://hsaf.meteoam.it url:https://icdc.cen.uni-hamburg.de/en/ascat-soilmoisture.html doi:10.25592/uhhfdm.8681 doi:10.25592/uhhfdm.8680 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Surface soil moisture Global maps Daily Satellite Remote Sensing ASCAT MetOp-A/B EUMETSAT HSAF University of Vienna ICDC ASCAT Global Maps of daily running 5-day mean surface soil moisture info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10413 2024-02-26T10:16:36Z user-icdc user-cliccs user-cen user-uhh

Kern, Stefan 2022-08-19 Abstract: Spatial correlation length scales derived from ESA-CCI SICCI-2 project sea-ice concentration data products at 50.0km grid resolution based on AMSR-E brightness temperature measurements for the period June 2002 through September 2011. TableOfContents: sea_ice_area_fraction_error_correlation_length; total_standard_error_correlation_length; minimum_rmsd_for_sea_ice_area_fraction_correlation_length; minimum_rmsd_for_total_standard_error_correlation_length; surface_type_flag Technical Info: dimensions: 216 columns x 216 rows x unlimited; temporalExtent_startDate: 2002-06-16; temporalExtent_endDate: 2011-09-19; temporalResolution: daily; spatialResolution: 50.0; spatialResolutionUnit: km; horizontalResolutionXdirection: 50.0; horizontalResolutionXdirectionUnit: km; horizontalResolutionYdirection: 50.0; horizontalResolutionYdirectionUnit: km; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E); instrumentType: multi-channel microwave radiometer; instrumentLocation: Earth Observation Satellite (EOS) - Aqua; instrumentProvider: JAXA; License: CC-BY-SA-NC-4.0 Methods: See Kern, S., Spatial correlation length scales of sea-ice concentration errors of high-concentration pack ice, Remote Sensing, 13(21), 4421, 2021, https://doi.org/10.3390/rs13214421 Units: units for all variables (see TableOfContents): km,km,1,1,1 geoLocations: NorthernHemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: 16.62393 degrees North; northBoundLongitude: 90.0 degrees North; geoLocationPlace: northernHemisphere SouthernHemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: -90.0 degrees North; northBoundLongitude: -16.62393 degrees North; geoLocationPlace: southernHemisphere Size: 730 (leap year 732) files per year, one for each hemisphere, ~1.17 MegaBype per file, ~0.8348 GigaByte per year, ~7.728 GigaByte in total (provided as annual zip archives for each hemisphere = 20 files) Format: netCDF DataSources: Pedersen, L. T., Dybkjaer, G., Eastwood, S., Heygster, G., Ivanova, N., Kern, S., Lavergne, T., Saldo, R., Sandven, S., Soerensen, A., and Tonboe, R. T.: ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR2 instruments at 50km grid spacing, version 2.1, Centre for Environmental Data Analysis [data set], 5 October 2017, https://doi.org/10.5285/5f75fcb0c58740d99b07953797bc041e Contact: stefan.kern (at) uni-hamburg.de https://www.fdr.uni-hamburg.de/record/10413 10.25592/uhhfdm.10413 oai:fdr.uni-hamburg.de:10413 eng doi:10.3390/rs13214421 doi:10.5285/5f75fcb0c58740d99b07953797bc041e doi:10.5285/5f75fcb0c58740d99b07953797bc041e doi:10.25592/uhhfdm.10412 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Spatial correlation length scales Sea ice concentration Hemispheric maps Satellite remote sensing AMSR-E Daily ESA-CCI ICDC Spatial correlation length scales of sea-ice concentration errors of high-concentration pack ice for ESA-CCI-SICCI2-50km info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10279 2024-02-26T10:16:25Z user-icdc user-cen user-uhh

Kirsch, Bastian Stiehle, Bernhard Löhnert, Ulrich Ament, Felix 2022-09-08 This data set contains atmospheric profile measurements retrieved from 452 radiosonde ascents during FESSTVaL 2021. The data set consits of observations from three data sources: (1) operational 6-hourly radiosonde launches from the Meteorological Observatory Lindenberg - Richard Aßmann Observatory (MOL-RAO) performed by the German Weather Service (sups_rao; 421 sondes); (2) special radiosonde launches from the MOL-RAO performed by the University of Hamburg (fval_uhh; 19 sondes); and (3) special radiosonde launches from the Falkenberg boundary layer measurement site (located about 5 km south of MOL-RAO) performed by the University of Cologne (fval_uzk; 12 sondes). All profiles are stored in seperate files according to the SAMD convention for FESSTVaL. A data file contains the 3-dimensional position information and basic meteorological parameters measured by the radiosonde dependent on time (at 1-s resolution) and typically covers both ascent and descent of the radiosonde flight. The vertical resolution of the measurement data depends on the ascent/descent velocity of the sonde. Date and time in the file names refer to the actual launch time (in UTC) of the respective radiosonde. Quality: The measurements are performed by standard radiosonde types. Apart from basic physical consistency checks, the data set contains the measurements as recorded by the instruments. For technical specifications please refer to the manufacturer's information (Vaisala RS41-SGP: https://www.vaisala.com/sites/default/files/documents/RS41-SGP-Datasheet-B211444EN.pdf; GRAW DMF-09: https://www.graw.de/fileadmin/cms_upload/de/Resources/GRS-KD-0038-DE_V01.13_DFM-09_Datenblatt.pdf). The data processing for (2) includes a correction of erroneous measurement time in the original source files, where the time stamp is updated only every 10 s. Therefore, the 1-s time stamps for (2) are determined with an accuracy of +/-5 s or better. Additionally, these profiles occasionally contain isolated time steps produced by the instruments that should be treated with caution. Parameters: Standard Name: air_temperature; Unit: K;Start-time: 2021-05-07;End-time: 2021-08-29 Standard Name: air_pressure;Unit: Pa;Start-time: 2021-05-07;End-time: 2021-08-29 relative_humidity, 1, 2021-05-07 - 2021-08-29 wind_speed, m s-1, 2021-05-07 - 2021-08-29 wind_from_direction, degree, 2021-05-07 - 2021-08-29 latitude, degrees_north, 2021-05-07 - 2021-08-29 longitude, degrees_east, 2021-05-07 - 2021-08-29 altitude, m, 2021-05-07 - 2021-08-29 Measurement Details Instruments: Vaisala RS41-SGP (sups_rao, fval_uhh) GRAW DMF-09 (fval_uzk) Location: 52.2094°N, 14.1203°E, 112 m, MOL-RAO, Germany Instruments altitude: 52.2094°N, 14.1203°E, 112 m, MOL-RAO, Germany Horizontal resolution: None Vertical resolution: approx. 10 m (depending on vertical velocity of radiosonde) Time resolution: 1 second Funding: This research was carried out in the Hans Ertel Center for Weather Research (HErZ). This German research network of universities, research institutions, and the German Weather Service (DWD) is funded by the BMVI (Federal Ministry of Transport and Digital Infrastructure). Provenance and History: The measurement data for (1) are retrieved from the opendata server of the German Weather Service. All data are processed by the University of Hamburg. https://www.fdr.uni-hamburg.de/record/10279 10.25592/uhhfdm.10279 oai:fdr.uni-hamburg.de:10279 eng doi:10.25592/uhhfdm.10278 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode atmosphere measurement profile radiosonde temperature pressure relative humidity wind speed wind direction Radiosonde profile measurements during FESSTVaL 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10385 2024-02-26T14:23:34Z user-icdc user-uhh

Dewani, Noviana Leinweber, Ronny 2022-07-29 The dataset contains the level 1 and level 2 data of vertical stare mode Doppler lidar during the FESSTVaL campaign 2020/2021. The dataset is retrieved from Halo Photonics Streamline XR Doppler lidar 161 during FESSTVaL 2020 and Doppler lidar 146 for FESSTVaL 2021. The data was measured during the period 1 June - 10 August 2020 and 25 May - 30 August 2021 at Falkenberg. The data is structured in folders as level1 and level2, one file per day. L1 data contains the configuration variable of Doppler lidar and the primary variable such as vertical velocity data, attenuated backscatter and intensity data. The vertical velocity variance and the estimated mixing layer height with 30-minute resolution and vertical velocity in 1-minute resolution are stored in L2 data. Quality: The dataset period is only available during the vertical stare mode period. The unavailable period is due to the change in the scanning configuration. https://www.fdr.uni-hamburg.de/record/10385 10.25592/uhhfdm.10385 oai:fdr.uni-hamburg.de:10385 eng doi:10.25592/uhhfdm.10384 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode measurement lidar FESSTVAL Vertical velocity data from vertical stare Doppler lidar, Falkenberg, FESSTVaL campaign 2020/2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:10255 2024-02-27T08:20:55Z user-cen user-icdc user-uhh

Jahnke-Bornemann, Annika 2022-06-16 The "Standardized Precipitation Index" (SPI) is used to describe extremely dry or wet climate situations. The advantages of SPI usage are: Only precipitation data are needed for the calculation of the index. The index is a standardized measure for precipitation in different climatic regions and for seasonal differences. Calculated for different time scales: meteorological, agricultural-economic and hydrological. SPI Classes: SPI ≤ -2: Extremely dry, -2 < SPI ≤ -1.5: Severely dry, -1.5 < SPI ≤ -1: Moderately dry, -1 < SPI ≤ 1: Near normal, 1 < SPI ≤ 1.5: Moderately wet, 1.5 < SPI ≤ 2: Severely wet, SPI ≥ 2: Extremely wet. Calculation: The SPI, presented here, is different from the original SPI definition of McKee et al. 1993. An enhanced SPI is used, that significantly reduces errors resulting from the determination of the precipitation's distribution (Sienz et al. 2011). MC Kee et al. 1993 shifted the time series of the SPI one time step into the future, but this is not done for the calculation of the SPI presented here. The reference period used for calculation of all distributions is 1901-2020. The SPIs (1, 3, 6, 9, 12, 24, 48) were calculated from the Climate Research Unit (CRU) precipitation data set, Version: CRU TS 4.05 for the period 1901 - 2020 for Europe and USA. It is an update and replaces the SPI from CRU by Frank Sienz. As various changes were made to the scripts, comparisons with examples of the results were made to ensure the quality of the data. The date specified in the files always indicates the end of the period under consideration. https://www.fdr.uni-hamburg.de/record/10255 10.25592/uhhfdm.10255 oai:fdr.uni-hamburg.de:10255 eng doi:10.25592/uhhfdm.10239 doi:10.25592/uhhfdm.10254 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode precipitation ICDC SPI drought SPI - Standardized Precipitation Index from CRU for EU and USA info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:9758 2024-02-28T10:50:54Z user-icdc user-uhh

Steinheuer, Julian Detring, Carola Kayser, Markus Leinweber, Ronny 2021-12-16 This data set contains level 1 and level 2 Doppler wind lidar measurements during 2019-08-15 and 2020-08-31 from different configurations and three different instruments (Halo Photonics Streamline). The purpose of the project part was to evaluate the ability of lidars to retrieve wind gusts. The instruments were replaced next to the Falkenberg meteorological tower in order to have a reference measurement of a sonic anemometer at 90.3m. The level 2 data are reproducible with this (https://doi.org/10.5281/ZENODO.5780949) code. The data are structured in folders for years (fesstval_2019 and fesstval_2020), for different devices (wl_***, sonic), and foldersof the corresponding different configurations. For each configuration there are the Level 1 files in level_1_** (where ** denoted the creator) and Level 2 files in level_2_js. The folder /sonic contains Level 2 files for the sonic anemometer in 90.3m of the Falkenberg meteorological tower. In the folders are daily netcdfs-files in SAMD convention with radial Doppler velocities (level1) and 10 minutes mean wind and wind gust peaks (level 2). The lidar wl_44 (fesstval_2020) was located in Lindberg (~5km north of Falkenberg). Quality: The data set does not contain a full time period but the result of a test campaign where configuration changes and breaks where made. The retrieval contains uncertainty estimates for each retrieved wind value. The data are based on a new approach and further testings are appropriate. Instruments: HALO Photonics Dopller lidar (system_ids: 177, 78, 143, 44, 172) Metek ultrasonic wind anemometer (factory version USA-1, 20Hz) Location: Latitude 52,16715, Longitude 14,12275. Falkenberg, Germany Instruments altitude: Latitude 52,16715, Longitude 14,12275. Falkenberg, Germany Provenance and History: Level 1 files have been created by Deutscher Wetterdienst, Meteorologisches Observartorium Lindenberg. https://www.fdr.uni-hamburg.de/record/9758 10.25592/uhhfdm.9758 oai:fdr.uni-hamburg.de:9758 eng doi:10.25592/uhhfdm.9757 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode LIDAR wind wind gust Falkenberg SAMD measurement FESSTVaL Doppler wind lidar wind and gust data from FESSTVaL 2019/2020, Falkenberg, data version 01 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8880 2024-06-27T09:46:37Z user-cen user-icdc user-cliccs user-uhh

Sadikni, Remon Jahnke-Bornemann, Annika Kern, Stefan 2021-02-25 Abstract: MODIS Collection 6 8-day Gross Primary Production (GPP) and Net Photosynthesis data on the MODIS sinusoidal grid are taken from the netCDF files produced at ICDC, for which the bit-encoded quality information given in the HDF-files was already decoded, and re-gridded to build a global map of grid-cell mean GPP and net photosynthesis and their variances on a global equirectangular climate modeling grid (CMG). Only those GPP or net photosynthesis values are used where i) the cloud flag indicates either clear sky or assumed clear sky, where the MODLAND quality is good and where the confidence flag suggests best quality or good quality data. The confidence flag is provided as a grid-cell mean rounded value with fractions of the five original flags being provided for convenience. Cloud conditions are included in form of the primary cloud flag and the fraction this primary cloud flag occupies among the valid 500 m sinusoidal grid grid cells. Two separate layers of the number of valid grid cells of the 500 m sinusoidal grid are given, one is for the geophysical data and one is for the flags. TableOfContents: grid cell mean Gross_Primary_Production (GPP); grid cell mean Net Photosynthesis; GPP standard deviation over grid cell; Net Photosynthesis standard deviation over grid cell; number of used GPP or net photosynthesis values per grid cell; number of used confidence and quality flag values per grid cell; grid cell mean confidence flag; fraction of confidence flag 0 in grid cell; fraction of confidence flag 1 in grid cell; fraction of confidence flag 2 in grid cell; fraction of confidence flag 3 in grid cell; fraction of confidence flag 4 in grid cell; primary cloud flag; primary cloud flag fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2020-12-31; temporalResolution: 8-daily; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] Running, S. W., and M. Zhao, Users Guide Daily GPP and Annual NPP (MOD17A2H/A3H) and Year-end Gap-Filled (MOD17A2HGF/A3HGF) Products NASA Earth Observing System MODIS Land Algorithm, (For Collection 6), Version 4.0, January 2, 2019; [2] Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto, A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547-560, 2004; [3] Running, S. W., A measurable planetary boundary layer for the biosphere. Science, 337(6101), 1458-1459, 2012; [4] Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running, Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95(2), 164-176, 2005 Units: Units for all variables (see TableOfContents): kg C / m2; kg C / m2; kg C / m2; kg C / m2; 1; 1; 1; percent; percent; percent; percent; percent; 1; percent geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: (files are packed into one zip-archive per year) 2001-2020: 46 files per year, each approximately 12975000 bytes 2000: 40 files of same size Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD17A2H.006 (last accessed: 2021-02-01), see also https://lpdaac.usgs.gov/products/mod17a2hv006/ (last accessed: 2021-02-01) Data on sinusoidal grid tiles in netCDF format: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2023-11-24) Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2023-11-24) https://www.fdr.uni-hamburg.de/record/8880 10.25592/uhhfdm.8880 oai:fdr.uni-hamburg.de:8880 eng doi:10.5067/MODIS/MOD17A2H.006 url:https://icdc.cen.uni-hamburg.de/en/modis-primaryproduction.html url:https://lpdaac.usgs.gov/products/mod17a2hv006/ doi:10.25592/uhhfdm.8556 doi:10.25592/uhhfdm.8555 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Gross Primary Production Net Photosynthesis Global maps 8-daily Satellite Remote Sensing MODIS EOS-Terra NTSG UMT ICDC MODIS Collection 6 global 8-daily Gross Primary Production info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8556 2024-06-27T09:46:37Z user-cen user-cliccs user-icdc user-uhh

Sadikni, Remon Jahnke-Bornemann, Annika Kern, Stefan 2021-01-20 Abstract: MODIS Collection 6 8-day Gross Primary Production (GPP) and Net Photosynthesis data on the MODIS sinusoidal grid are taken from the netCDF files produced at ICDC, for which the bit-encoded quality information given in the HDF-files was already decoded, and re-gridded to build a global map of grid-cell mean GPP and net photosynthesis and their variances on a global equirectangular climate modeling grid (CMG). Only those GPP or net photosynthesis values are used where i) the cloud flag indicates either clear sky or assumed clear sky, where the MODLAND quality is good and where the confidence flag suggests best quality or good quality data. The confidence flag is provided as a grid-cell mean rounded value with fractions of the five original flags being provided for convenience. Cloud conditions are included in form of the primary cloud flag and the fraction this primary cloud flag occupies among the valid 500 m sinusoidal grid grid cells. Two separate layers of the number of valid grid cells of the 500 m sinusoidal grid are given, one is for the geophysical data and one is for the flags. TableOfContents: grid cell mean Gross_Primary_Production (GPP); grid cell mean Net Photosynthesis; GPP standard deviation over grid cell; Net Photosynthesis standard deviation over grid cell; number of used GPP or net photosynthesis values per grid cell; number of used confidence and quality flag values per grid cell; grid cell mean confidence flag; fraction of confidence flag 0 in grid cell; fraction of confidence flag 1 in grid cell; fraction of confidence flag 2 in grid cell; fraction of confidence flag 3 in grid cell; fraction of confidence flag 4 in grid cell; primary cloud flag; primary cloud flag fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2019-08-13; temporalResolution: 8-daily; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] Running, S. W., and M. Zhao, Users Guide Daily GPP and Annual NPP (MOD17A2H/A3H) and Year-end Gap-Filled (MOD17A2HGF/A3HGF) Products NASA Earth Observing System MODIS Land Algorithm, (For Collection 6), Version 4.0, January 2, 2019; [2] Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto, A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547-560, 2004; [3] Running, S. W., A measurable planetary boundary layer for the biosphere. Science, 337(6101), 1458-1459, 2012; [4] Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running, Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95(2), 164-176, 2005 Units: Units for all variables (see TableOfContents): kg C / km2; kg C / km2; kg C / km2; kg C / km2; 1; 1; 1; percent; percent; percent; percent; percent; 1; percent geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: (files are packed into one zip-archive per year) 2001-2018: 46 files per year, each 12974688 bytes 2000: 40 files of same size 2019: 29 files of same size Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD17A2H.006 (last accessed: 2019-08-27), see also https://lpdaac.usgs.gov/products/mod17a2hv006/ (last accessed: 2019-08-27) Data on sinusoidal grid tiles in netCDF format: https://icdc.cen.uni-hamburg.de/en/modis-primaryproduction.html (last accessed: 2021-01-20) Contact: stefan.kern (at) uni-hamburg.de Web page: https://icdc.cen.uni-hamburg.de/en/modis-primaryproduction.html (last accessed: 2021-01-20) https://www.fdr.uni-hamburg.de/record/8556 10.25592/uhhfdm.8556 oai:fdr.uni-hamburg.de:8556 eng doi:10.5067/MODIS/MOD17A2H.006 url:https://icdc.cen.uni-hamburg.de/en/modis-primaryproduction.html url:https://lpdaac.usgs.gov/products/mod17a2hv006/ doi:10.25592/uhhfdm.8880 doi:10.25592/uhhfdm.8555 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Gross Primary Production Net Photosynthesis Global maps 8-daily Satellite Remote Sensing MODIS EOS-Terra NTSG UMT ICDC MODIS Collection 6 global 8-daily Gross Primary Production info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:14635 2024-07-10T21:12:17Z user-icdc user-cen user-uhh

Sadikni, Remon Kern, Stefan 2024-07-10 Abstract: MODIS Collection 6.1 yearly gap-filled Gross Primary Production (GPP) and Net Primary Production (NPP) data on the MODIS sinusoidal grid are taken from the netCDF files produced at ICDC, for which the bit-encoded quality information given in the HDF-files was already decoded, and re-gridded to build a global map of grid-cell mean GPP and NPP and their variances on a global equirectangular climate modeling grid (CMG). Only those GPP or NPP values are used where i) the cloud flag indicates either clear sky or assumed clear sky, where the MODLAND quality is good and where the confidence flag suggests best quality or good quality data. The confidence flag is provided as a grid-cell mean rounded value with fractions of the five original flags being provided for convenience. Cloud conditions are included in form of the primary cloud flag and the fraction this primary cloud flag occupies among the valid 500 m sinusoidal grid grid cells. Two separate layers of the number of valid grid cells of the 500 m sinusoidal grid are given, one is for the geophysical data and one is for the flags. TableOfContents: grid cell mean Gross_Primary_Production (GPP); grid cell mean Net_Primary_Production (NPP); GPP standard deviation over grid cell; NPP standard deviation over grid cell; number of valid used GPP or NPP values per grid cell; number of valid used confidence and quality flag values per grid cell; grid cell mean confidence flag; fraction of confidence flag 0 in grid cell; fraction of confidence flag 1 in grid cell; fraction of confidence flag 2 in grid cell; fraction of confidence flag 3 in grid cell; fraction of confidence flag 4 in grid cell; primary cloud flag; primary cloud flag fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2001-01-01; temporalExtent_endDate: 2023-12-31; temporalResolution: Yearly; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] Running, S. W., and M. Zhao, Users Guide Daily GPP and Annual NPP (MOD17A2H/A3H) and Year-end Gap-Filled (MOD17A2HGF/A3HGF) Products NASA Earth Observing System MODIS Land Algorithm, (For Collection 6), Version 4.0, January 2, 2019; [2] Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto, A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547-560, 2004; [3] Running, S. W., A measurable planetary boundary layer for the biosphere. Science, 337(6101), 1458-1459, 2012; [4] Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running, Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95(2), 164-176, 2005 Units: Units for all variables (see TableOfContents): kg C m-2; kg C m-2; kg C m-2; kg C m-2; 1; 1; 1; percent; percent; percent; percent; percent; 1; percent geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: (files are packed into one zip-archive) 2001-2023: 1 file per year, each approximately 12975000 bytes Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD17A3HGF.061 (last accessed: 2024-06-03), see also https://lpdaac.usgs.gov/products/mod17a3hgfv061/ (last accessed: 2024-06-03) Data on sinusoidal grid tiles in netCDF format: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-07-09) or https://doi.org/10.25592/uhhfdm.14633 (last accessed: 2024-07-09). Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-07-09) https://www.fdr.uni-hamburg.de/record/14635 10.25592/uhhfdm.14635 oai:fdr.uni-hamburg.de:14635 eng doi:10.25592/uhhfdm.14633 url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html url:https://lpdaac.usgs.gov/products/mod17a3hgfv061/ doi:10.25592/uhhfdm.14463 doi:10.25592/uhhfdm.14634 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Gross Primary Production Net Primary Production Global Yearly Satellite Remote Sensing MODIS EOS-Terra NTSG UMT ICDC MODIS Collection 6.1 global yearly gap-filled Gross and Net Primary Production info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:11394 2023-02-02T09:10:47Z user-icdc user-cen user-uhh

Sadikni, Remon Kern, Stefan Leinweber, Ronny Meier, Fred Beyrich, Frank 2023-01-31 Abstract: This data set contains vertical profiles of the mean wind vector derived from Doppler lidar measurements between June 12 and July 19 2021 at the Grenzschichtmessfeld (GM) Falkenberg (sups_rao_dlidvad00) and between May 17 and July 15 2021 at the Meteorological Observatory Lindenberg – Richard-Aßmann-Observatory (fval_tub_dlidvad00) during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL). The GM Falkenberg as part of the Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory are operated by the German national meteorological service (Deutscher Wetterdienst, DWD). The Doppler lidar system operated at the GM site was kindly provided by Technische Universität Berlin, Institute for Ecology, Chair of Climatology. Both, level 1 and level 2 data are provided. TableOfContents: level1: sups_rao_dlidvad00: sensor azimuth angle; attenuated backscatter coefficient; spectral width of detected signal; radial velocity of scatterers away from instrument (doppler velocity); error of doppler velocity; backscatter intensity; range bands; zenith angle fval_tub_dlidvad00: sensor azimuth angle; attenuated backscatter coefficient; radial velocity of scatterers away from instrument (doppler velocity); error of doppler velocity; backscatter intensity; range bands; zenith angle level2: wind speed; wind direction; eastward wind component u; northward wind component v; upward air velocity w; wind speed uncertainty; wind direction uncertainty; eastward wind component uncertainty; northward wind component uncertainty; upward air velocity uncertainty; wind quality flag; eastward wind component quality flag; northward wind component quality flag; upward air velocity quality flag; coefficient of determination; condition number; number of radial velocities; horizontal width of Doppler LIDAR data; height bounds; time bounds Technical Info: level1: dimension: 17280 x 250; temporalExtent_startDate: 2021-05-17 00:00:00; temporalExtent_endDate: 2021-07-20 00:00:00; temporalResolution: 5; temporalResolutionUnit: seconds; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; rangeResolution: 48; rangeResolutionUnit: meters; verticalResolution: 36; verticalResolutionUnit: meters; verticalStart: 0; verticalStartUnit: meters; verticalEnd: 12000; verticalEndUnit: meters; instrumentNames: Stream Line XR S/N 143 (sups_rao_dlidvad00), Stream Line XR S/N 44 (fval_tub_dlidvad00); instrumentType: Doppler LIDAR; instrumentLocation: Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd. level2: dimension01: 144 timesteps x 250 (10-minute mean); dimension02: 48 timesteps x 250 (30-minute mean); temporalExtent_startDate: 2021-05-17 00:00:00; temporalExtent_endDate: 2021-07-19 15:30:00; temporalResolution01: 10; temporalResolutionUnit01: minutes; temporalResolution02: 30; temporalResolutionUnit02: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: 36; verticalResolutionUnit: meters; verticalStart: 0; verticalStartUnit: meters; verticalEnd: 11600; verticalEndUnit: meters; instrumentNames: Stream Line XR S/N 143 (sups_rao_dlidvad00), Stream Line XR S/N 44 (fval_tub_dlidvad00); instrumentType: Doppler LIDAR; instrumentLocation: Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd.. Methods: fval_tub_dlidvad00: Level-1 data represent the instantaneous backscatter intensity and radial velocity profile measurements along each single ray. Level-2 data represent 10- and 30-minutes averages of the mean wind vector, respectively. All data are organized in daily files. They are based on a step-stare Velocity Azimuth Display (VAD) lidar scan pattern with 24 rays per scan circle and 30000 lidar pulses per ray at a 15 degrees zenith angle. The profiles typically cover a height range between about 90 m a.g.l. and (at least) the top of the boundary layer. The actual maximum height is highly variable, it depends on the maximum nominal range of the instrument, on the scan geometry and on the presence of scatterers in the atmosphere (e.g., aerosols, cloud particles). Besides the meteorological variables, an error estimate and a quality flag is given for each variable. Furthermore, quality test information and instrumental parameters are given. Raw data processing followed the sensitive consensus approach identifying the most reliable cluster of radial velocity retrievals along each scan direction from a series of scans during the averaging interval. Quality tests include an assessment of the coefficient of determination of the VAD fit and of the condition number indicating the sectoral coverage of the fit. The retrieval algorithm has been validated through a long-term inter-comparison of lidar-based winds versus radiosonde and radar wind profiler wind retrievals resulting in a typical uncertainty (rmsd) of 0.8 m/s and 8.0 degrees for wind speed and wind direction, respectively. Each measured value is accompanied by a quality flag where 0 = bad, and 1 = good. Note: Data from June 12 to June 23 had to be reprocessed to correct for an azimuth error, these data are labelled as version 01; all other data are labelled as version 00. sups_rao_dlidvad00: Level-1 data represent the instantaneous backscatter intensity and radial velocity profile measurements along each single ray. Level-2 data represent 10- and 30-minutes averages of the mean wind vector, respectively. They are processed by DWD using the consensus approach (see, e.g., Päschke et al. 2015 - https://doi.org/10.5194/amt-8-2251-2015) identifying the most reliable cluster of radial velocity retrievals along each scan direction from a series of scans during the averaging interval. All data are organized in daily files. They are based on a step-stare Velocity Azimuth Display (VAD) lidar scan pattern with 24 rays per scan circle and 30000 lidar pulses per ray at a 15 degrees zenith angle. The profiles typically cover a height range between about 90 m a.g.l. and (at least) the top of the boundary layer. The actual maximum height is highly variable, it depends on the maximum nominal range of the instrument, on the scan geometry and on the presence of scatterers in the atmosphere (e.g., aerosols, cloud particles). Besides the meteorological variables, an error estimate and a quality flag are given for each variable. Furthermore, quality test information and instrumental parameters are given. Quality tests of the DWD standard processing include an assessment of the coefficient of determination of the VAD fit and of the condition number indicating the sectoral coverage of the fit. The retrieval algorithm has been validated through a long-term inter-comparison of lidar-based winds versus radiosonde and radar wind profiler wind retrievals resulting in a typical uncertainty (rmsd) of 0.8 m/s and 8.0 degrees for wind speed and wind direction, respectively. Each measured value is accompanied by a quality flag where 0 = bad, and 1 = good. Units: Units for all variables (see TableOfContents): level1: sups_rao_dlidvad00: degrees; 1/ (m sr); m/s; m/s; m/s; 1; m; degrees fval_tub_dlidvad00: degrees; 1/ (m sr); m/s; m/s; 1; m; degrees level2: m/s; degrees; m/s; m/s; m/s; m/s; degrees; m/s; m/s; m/s; 1, 1, 1, 1, 1, 1, 1, m; m; s geoLocations: BoundingBox: westBoundLongitude: 14.1222 degrees East; eastBoundLongitude: 14.1287 degrees East; southBoundLatidude: 52.1665 degrees North; northBoundLatitude: 52.2098 degrees North; geoLocationPlace: Germany, UTM zone 33U Locations: Falkenberg: 52.1665 degrees North, 14.1222 degrees East, 73 meters above mean sea level Lindenberg: 52.2098 degrees North, 14.1287 degrees East, 104 meters above mean sea level Size: All data are organized in daily files. Level-1 and level-2 data of the two instruments are each packed separately into one tar archive; the total number of tar archives is hence 2 + 2 = 4. Files sizes of these archives are: Falkenberg level-1: ~1.94 GByte, level-2: 15 MByte, Lindenberg level-1: 3.75 GByte, level-2: 25 MByte; the total amount is ~ 6 GByte. Format: netCDF DataSources: Single site ground-based remote sensing, see "Technical Info" for instruments Contact: ronny.leinweber (at) dwd.de; frank.beyrich (at) dwd.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/fval-dlidvad-wind.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html https://www.fdr.uni-hamburg.de/record/11394 10.25592/uhhfdm.11394 oai:fdr.uni-hamburg.de:11394 eng doi:10.25592/uhhfdm.9824 doi:10.25592/uhhfdm.9902 doi:10.5194/amt-8-2251-2015 url:https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html doi:10.25592/uhhfdm.11226 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Doppler LIDAR Vertical Azimuth Display Wind speed Wind direction Measurements FESSTVAL SAMD Doppler lidar mean wind profiles from VAD scans during FESSTVAL 2021 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:16656 2025-01-14T15:21:28Z user-cen user-icdc user-uhh user-cliccs

Sadikni, Remon Kern, Stefan 2025-01-15 Abstract: NetCDF files of the forest cover fraction, vegetation cover fraction and fraction of non-vegetated area on 250m grid resolution sinusoidal grid generated at ICDC from the original HDF files (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html and https://doi.org/10.25592/uhhfdm.16655) obtained from https://lpdaac.usgs.gov/mod44bv061/ are used to compute globally gridded maps of these parameters at 0.5 degree grid resolution on an equi-rectangular climate modeling grid (CMG). The global maps contain the grid-cell mean fractions of the three mentioned parameters, their variance within the grid cells, and - for the forest cover fraction - the grid-cell mean standard deviation. In addition, the data set includes maps of the number of valid forest cover fraction values at 250 m resolution per 0.5 degree grid cell, a grid cell mean quality flag and fractions of the two most abundant quality flags (primary and secondary). Generally all valid data are used; the user is advised to check the quality flags to eventually discard data of low quality. TableOfContents: grid cell mean forest cover fraction; grid cell mean forest cover fraction standard deviation; forest cover fraction variance within grid cell; grid cell mean vegetation cover fraction; vegetation cover fraction variance within grid cell; non-vegetated area cover fraction; non-vegetated area cover fraction variance within grid cell; number of useful vegetation cover fraction values per grid cell; grid cell mean quality flag; primary quality flag fraction; secondary quality flag fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2023-03-06; temporalExtent_endDate: 2024-03-04; temporalResolution: yearly; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] https://lpdaac.usgs.gov/products/mod44bv061/; [2] Townshend, J., et al., User Guide for the MODIS Vegetation Continuous Fields product Collection 6.1, verison 1, https://lpdaac.usgs.gov/documents/1494/MOD44B_User_Guide_V61pdf; [3] Algorithm Theoretical Basis Document (ATBD), https://lpdaac.usgs.gov/documents/113/MOD44B_ATBD.pdf; [4] Carroll, M., et al., 2011. Vegetative Cover Conversion and Vegetation Continuous Fields. In: Ramachandran, B., C. O. Justice, and M. Abrams (eds.), Land Remote Sensing and Global Environment Change: NASA's Earth Observing System and the Science of ASTER and MODIS. Springer Verlag.; [5] Hansen, M., et al., 2005. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Rem. Sens., 26(19), 4359-4380. Units: Units for all variables (see TableOfContents): percent; percent; 1; percent; 1; percent; 1; 1; 1; 1; 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: 1 file, ~5.2 Mb Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD44B.061 (last accessed 2024-12-27), see also https://lpdaac.usgs.gov/products/mod44bv061/ (last accessed: 2024-12-27) Reprocessed data on sinusoidal grid tiles in netCDF format: https://doi.org/10.25592/uhhfdm.16655 (last access 2025-01-15), see also https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html (last accessed 2025-01-15) Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html https://www.fdr.uni-hamburg.de/record/16656 10.25592/uhhfdm.16656 oai:fdr.uni-hamburg.de:16656 eng doi:10.5067/MODIS/MOD44B.061 url:https://lpdaac.usgs.gov/products/mod44bv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html doi:10.25592/uhhfdm.16655 doi:10.25592/uhhfdm.11196 doi:10.25592/uhhfdm.12922 doi:10.25592/uhhfdm.8560 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Forest Cover Fraction Vegetation Cover Fraction Global maps Yearly Satellite Remote Sensing MODIS EOS-Terra University of Maryland ICDC MODIS Collection 6 .1 global yearly Forest and Vegetation Cover Fraction Extension 02 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:12922 2025-01-14T15:21:28Z user-cliccs user-icdc user-cen user-uhh

Sadikni, Remon Kern, Stefan 2023-07-14 Abstract: NetCDF files of the forest cover fraction, vegetation cover fraction and fraction of non-vegetated area on 250m grid resolution sinusoidal grid generated at ICDC from the original HDF files (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html and https://doi.org/10.25592/uhhfdm.12921) obtained from https://lpdaac.usgs.gov/mod44bv061/ are used to compute globally gridded maps of these parameters at 0.5 degree grid resolution on an equi-rectangular climate modeling grid (CMG). The global maps contain the grid-cell mean fractions of the three mentioned parameters, their variance within the grid cells, and - for the forest cover fraction - the grid-cell mean standard deviation. In addition, the data set includes maps of the number of valid forest cover fraction values at 250 m resolution per 0.5 degree grid cell, a grid cell mean quality flag and fractions of the two most abundant quality flags (primary and secondary). Generally all valid data are used; the user is advised to check the quality flags to eventually discard data of low quality. TableOfContents: grid cell mean forest cover fraction; grid cell mean forest cover fraction standard deviation; forest cover fraction variance within grid cell; grid cell mean vegetation cover fraction; vegetation cover fraction variance within grid cell; non-vegetated area cover fraction; non-vegetated area cover fraction variance within grid cell; number of useful vegetation cover fraction values per grid cell; grid cell mean quality flag; primary quality flag fraction; secondary quality flag fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2022-03-06; temporalExtent_endDate: 2023-03-05; temporalResolution: yearly; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] https://lpdaac.usgs.gov/products/mod44bv061/; [2] Townshend, J., et al., User Guide for the MODIS Vegetation Continuous Fields product Collection 6.1, verison 1, https://lpdaac.usgs.gov/documents/1494/MOD44B_User_Guide_V61pdf; [3] Algorithm Theoretical Basis Document (ATBD), https://lpdaac.usgs.gov/documents/113/MOD44B_ATBD.pdf; [4] Carroll, M., et al., 2011. Vegetative Cover Conversion and Vegetation Continuous Fields. In: Ramachandran, B., C. O. Justice, and M. Abrams (eds.), Land Remote Sensing and Global Environment Change: NASA's Earth Observing System and the Science of ASTER and MODIS. Springer Verlag.; [5] Hansen, M., et al., 2005. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Rem. Sens., 26(19), 4359-4380. Units: Units for all variables (see TableOfContents): percent; percent; 1; percent; 1; percent; 1; 1; 1; 1; 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: 1 file, ~5.2 Mb Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD44B.061 (last accessed 2023-06-23), see also https://lpdaac.usgs.gov/products/mod44bv061/ (last accessed: 2023-06-23) Reprocessed data on sinusoidal grid tiles in netCDF format: https://doi.org/10.25592/uhhfdm.12921 (last access 2023-07-14), see also https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html (last accessed 2023-07-14) Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html https://www.fdr.uni-hamburg.de/record/12922 10.25592/uhhfdm.12922 oai:fdr.uni-hamburg.de:12922 eng doi:10.5067/MODIS/MOD44B.061 url:https://lpdaac.usgs.gov/products/mod44bv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html doi:10.25592/uhhfdm.12921 doi:10.25592/uhhfdm.11196 doi:10.25592/uhhfdm.8560 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Forest Cover Fraction Vegetation Cover Fraction Global maps Yearly Satellite Remote Sensing MODIS EOS-Terra University of Maryland ICDC MODIS Collection 6 .1 global yearly Forest and Vegetation Cover Fraction Extension 01 info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:11196 2025-01-14T15:21:28Z user-icdc user-cen user-uhh user-cliccs

Sadikni, Remon Kern, Stefan 2022-12-19 Abstract: NetCDF files of the forest cover fraction, vegetation cover fraction and fraction of non-vegetated area on 250m grid resolution sinusoidal grid generated at ICDC from the original HDF files (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html and https://doi.org/10.25592/uhhfdm.11198) obtained from https://lpdaac.usgs.gov/mod44bv061/ are used to compute globally gridded maps of these parameters at 0.5 degree grid resolution on an equi-rectangular climate modeling grid (CMG). The global maps contain the grid-cell mean fractions of the three mentioned parameters, their variance within the grid cells, and - for the forest cover fraction - the grid-cell mean standard deviation. In addition, the data set includes maps of the number of valid forest cover fraction values at 250 m resolution per 0.5 degree grid cell, a grid cell mean quality flag and fractions of the two most abundant quality flags (primary and secondary). Generally all valid data are used; the user is advised to check the quality flags to eventually discard data of low quality. TableOfContents: grid cell mean forest cover fraction; grid cell mean forest cover fraction standard deviation; forest cover fraction variance within grid cell; grid cell mean vegetation cover fraction; vegetation cover fraction variance within grid cell; non-vegetated area cover fraction; non-vegetated area cover fraction variance within grid cell; number of useful vegetation cover fraction values per grid cell; grid cell mean quality flag; primary quality flag fraction; secondary quality flag fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-03-05; temporalExtent_endDate: 2022-03-05; temporalResolution: yearly; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] https://lpdaac.usgs.gov/products/mod44bv061/; [2] Townshend, J., et al., User Guide for the MODIS Vegetation Continuous Fields product Collection 6.1, verison 1, https://lpdaac.usgs.gov/documents/1494/MOD44B_User_Guide_V61pdf; [3] Algorithm Theoretical Basis Document (ATBD), https://lpdaac.usgs.gov/documents/113/MOD44B_ATBD.pdf; [4] Carroll, M., et al., 2011. Vegetative Cover Conversion and Vegetation Continuous Fields. In: Ramachandran, B., C. O. Justice, and M. Abrams (eds.), Land Remote Sensing and Global Environment Change: NASA's Earth Observing System and the Science of ASTER and MODIS. Springer Verlag.; [5] Hansen, M., et al., 2005. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Rem. Sens., 26(19), 4359-4380. Units: Units for all variables (see TableOfContents): percent; percent; 1; percent; 1; percent; 1; 1; 1; 1; 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: 1 file per year, 2000-2021: 5197612 bytes; all packed into one zip-archive Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD44B.061 (last accessed 2022-11-01), see also https://lpdaac.usgs.gov/products/mod44bv061/ (last accessed: 2022-11-01) Reprocessed data on sinusoidal grid tiles in netCDF format: https://doi.org/10.25592/uhhfdm.11198 (last access 2022-12-19), see also https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html (last accessed 2022-12-19) Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html https://www.fdr.uni-hamburg.de/record/11196 10.25592/uhhfdm.11196 oai:fdr.uni-hamburg.de:11196 eng doi:10.5067/MODIS/MOD44B.061 url:https://lpdaac.usgs.gov/products/mod44bv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-vcf-forest.html doi:10.25592/uhhfdm.11198 doi:10.25592/uhhfdm.8560 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Forest Cover Fraction Vegetation Cover Fraction Global maps Yearly Satellite Remote Sensing MODIS EOS-Terra University of Maryland ICDC MODIS Collection 6 .1 global yearly Forest and Vegetation Cover Fraction info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:14185 2025-02-03T16:51:31Z user-icdc user-uhh user-cliccs user-cen

Sadikni, Remon Kern, Stefan 2024-04-04 Abstract: Original LAI and FAPAR data (see https://lpdaac.usgs.gov/products/mod15a2hv061/) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the LAI and FAPAR data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html and https://doi.org/10.25592/uhhfdm.14184). These are subsequently read and re-gridded onto an equi-rectangular climate modeling grid (CMG). Only those LAI and FAPAR values are used where i) the cloud flag indicates a maximum of two cloud influences, and where ii) cloud cover is clearly defined, i.e. "assumed clear sky" is not used. Flag layers are summarized such that there is gridded information about 1) cloud fraction, 2) fraction of average and high aerosol load, 3) primary and secondary land-cover type, and 4) primary and secondary quality flag. Primary and secondary refer to the highest and 2nd-highest pixel count of the respective type or flag within the grid cell. Note that the count of valid values differs for the grid cell mean LAI and FAPAR and their variance, and for the grid-cell mean retrieval standard deviation. TableOfContents: Grid cell mean FAPAR; Grid cell mean LAI; FAPAR variance across grid cell; LAI variance across grid cell; Grid cell mean FAPAR retrieval standard deviation; Grid cel mean LAI retrieval standard deviation; Count of useful FAPAR or LAI values per grid cell; Count of useful FAPAR or LAI retrieval standard deviation values in grid cell; Primary quality flag; Secondary quality flag; Primary land cover; Secondary land cover; Grid cell cloud fraction; Grid cell aerosol fraction Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2023-12-31; temporalResolution: 8-daily; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] MODIS collection 6.1 (C61) LAI/FPAR Product User Guide, https://lpdaac.usgs.gov/documents/926/MOD15_User_Guide_V61.pdf ; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document (ATBD), v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf; [3] Yang, et al., From validation to algorithm improvement. Trans. Geosci. Rem. Sens., 44, 1885-1898, 2006; [4] Morisette, et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, Trans. Geosci. Rem. Sens., 44, 1804-1817, 2006; [5] Garrigues, et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008; [6] https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html Units: Units for all variables (see TableOfContents): percent, m2/m2, percent, m4/m4, percent, m2/m2, 1, 1, 1, 1, 1, 1, percent, percent geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land Size: (files are packed into one zip-file per year) 2000: 40 files, 9864980 byte / file 2001: 44 files (20010618 and 20010626 are missing) 2002: 35 files (20020101 until 20020322 are missing) 2003-2023: 46 files / year Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD15A2H.061 [last access: 2024-03-13], see also: https://lpdaac.usgs.gov/products/mod15a2hv061/ [last access: 2024-03-13] Data on sinusoidal grid tiles in netCDF-format: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2024-04-04] and https://doi.org/10.25592/uhhfdm.14184 [last access: 2024-04-04] Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2024-04-04] https://www.fdr.uni-hamburg.de/record/14185 10.25592/uhhfdm.14185 oai:fdr.uni-hamburg.de:14185 eng doi:10.25592/uhhfdm.14184 url:https://lpdaac.usgs.gov/products/mod15a2hv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html doi:10.25592/uhhfdm.11777 doi:10.25592/uhhfdm.8584 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Leaf Area Index LAI FAPAR Global Maps 8-daily Satellite Remote Sensing MODIS EOS-Terra Boston University ICDC MODIS Collection 6.1 global 8-daily LAI and FAPAR info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:14184 2025-02-03T16:51:21Z user-icdc user-uhh user-cliccs user-cen

Sadikni, Remon Kern, Stefan 2024-04-04 Abstract: Original LAI and FAPAR data (see https://lpdaac.usgs.gov/products/mod15a2hv061/) are read together with their bit-encoded quality information from the HDF-files. The quality information is decoded and provided in form of separate flag layers in addition to the LAI and FAPAR data for each tile of the MODIS sinusoidal grid in netCDF file format (see https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html). For each tile, latitude and longitude information of the center of each 500 m x 500 m pixel is provided in a separate netCDF file. TableOfContents: FAPAR; LAI; FAPAR retrieval standard deviation; LAI retrieval standard deviation; Detailed quality flag; Quality flag for land; Quality flag for cloud and aerosol; Quality flag for method Technical Info: dimension: 2400 columns x 2400 rows x unlimited; temporalExtent_startDate: 2000-02-18; temporalExtent_endDate: 2023-12-31; temporalResolution: 8-daily; spatialResolution: 500; spatialResolutionUnit: meter; horizontalResolutionXdirection: 500; horizontalResolutionXdirectionUnit: meter; horizontalResolutionYdirection: 500; horizontalResolutionYdirectionUnit: meter; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Methods: [1] MODIS collection 6.1 (C61) LAI/FPAR Product User Guide, https://lpdaac.usgs.gov/documents/926/MOD15_User_Guide_V61.pdf ; [2] Myneni, R. B., et al., Algorithm Theoretical Basis Document (ATBD), v4.0, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf; [3] Yang, et al., From validation to algorithm improvement. Trans. Geosci. Rem. Sens., 44, 1885-1898, 2006; [4] Morisette, et al., Validation of global moderate resolution LAI products: A framework proposed within the CEOS Land Product Validation subgroup, Trans. Geosci. Rem. Sens., 44, 1804-1817, 2006; [5] Garrigues, et al., Validation and intercomparison of global Leaf Area Index products derived from remote sensing data, J. Geophys. Res., 113, G02028, https://doi.org/10.1029/2007JG000635, 2008; [6] https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html Units: Units for all variables (see TableOfContents): percent, m2/m2, percent, m2/m2, 1, 1, 1, 1 geoLocations: westBoundLongitude:depends on tile; eastBoundLongitude: depends on tile; southBoundLatitude: depends on tile; northBoundLatitude: depends on tile; geoLocationPlace: global on land, see: https://modis-land.gsfc.nasa.gov/MODLAND_grid.html Size: (files are packed into one zip-file per year) 2000: 270 x 40 files, 92.169 Mbyte / file 2001: 270 x 44 files (20010618 and 20010626 are missing) 2002: 270 x 35 files (20020101 until 20020322 are missing) 2003-2023: 270 x 46 files / year Latitude/Longitude: 291 files, 46.08 Mbyte / file Format: netCDF DataSources: Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD15A2H.061 [last access: 2024-03-13], see also: https://lpdaac.usgs.gov/products/mod15a2hv061/ [last access: 2024-03-13] Contact: stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html [last access: 2024-04-04] https://www.fdr.uni-hamburg.de/record/14184 10.25592/uhhfdm.14184 oai:fdr.uni-hamburg.de:14184 eng doi:10.5067/MODIS/MOD15A2H.061 url:https://lpdaac.usgs.gov/products/mod15a2hv061/ url:https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-lai-fpar.html doi:10.25592/uhhfdm.11776 doi:10.25592/uhhfdm.14185 doi:10.25592/uhhfdm.10866 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Vegetation Leaf Area Index LAI FAPAR Sinusoidal Grid Tiles 8-daily Satellite Remote Sensing MODIS EOS-Terra Boston University ICDC MODIS Collection 6.1 Sinusoidal Tiles 8-daily LAI and FAPAR info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:17560 2025-11-07T16:23:48Z user-cen user-icdc user-uhh user-cliccs

Sadikni, Remon Kern, Stefan Yakubu, Fuseini Böhner, Jürgen Schickhoff, Udo Scholten, Thomas Hasson, Shabeh Ul 2025-07-24 Abstract: This dataset provides globally consistent, bias-corrected climate data at 0.5° spatial resolution, consisting of a set of seven climate variables derived from three General Circulation Models (GCMs) participating in CMIP5 downscaled by 10 CORDEX Regional Climate Model (RCM) simulations and bias-corrected globally for the period 1950/1960–2099. It includes data from three climate change scenarios, namely RCP2.6, RCP4.5 and RCP8.5. The three GCMs are: ICHEC-EC-EARTH, MPI-M-MPI-ESM-LR, NOAA-GFDL-GFDL-ESM2M. Data are originally available as one netCDF file per GCM (3) per variable (7, NOAA-GFDL-GFDL-ESM2M: 5) per run (4, NOAA-GFDL-GFDL-ESM2M: 3). Available here are zip-archives of all netCDF files of one run, i.e. only rcp26 or only rcp45, per GCM (see Size for the overall sum per GCM). TableOfContents: daily mean 2m-air temperature (tas); daily minimum 2m-air temperature (tasmin), daily maximum 2m-air temperature (tasmax); daily sum of precipitation (pr); daily mean surface downwelling longwave radiation (rlds)*; daily mean 10m wind speed (sfcWind)*; daily mean relative humidity (hurs) *: These variables are NOT included in the NOAA-GFDL-GFDL-ESM2M driven data. TechnicalInfo: dimension: 720 columns x 360 rows; temporalExtent_startDate_Historlcal: 1950-01-01 00:00:00; temporalExtent_endDate_Historical: 2019-12-31 23:59:59; temporalDuration_Historical: 70; temporalDurationUnit_Historical: a; temporalExtent_startDate_RCPs: 2020-01-01 00:00:00; temporalExtent_endDate_RCPs: 2099-12-31 23:59:59; temporalDuration_RCPs: 80; temporalDurationUnit_RCPs: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none *) For MPI-M-MPI-ESM-LR: temporalExtent_startDate_Historlcal: 1960-01-01 00:00:00; temporalExtent_endDate_Historical: 2019-12-31 23:59:59; temporalDuration_Historical: 60; Methods: The ISIMIP3BASD v2.5 bias correction method (see Lange [2019; 2021]) was applied to adjust systematic biases using the GSWP3-W5E53 observational dataset. The regional climate models (RCMs) used are: (listed are Institution/working group, RCM Model, Driving GCM):" The observation is 'GSWP3-W5E5'; it's appearing as 'GSWP3-W5E53': Climate Service Center Germany (GERICS), REMO2009, MPI-ESM-LR Swedish Meteorological and Hydrological Institute (SMHI), RCA4, MPI-ESM-LR Climate Limited-area Modelling Community (CLMcom), CCLM4-8-17-CLM3-5, MPI-ESM-LR Climate Limited-area Modelling Community (CLMcom), CCLM5-0-2, MPI-ESM-LR Universite du Quebec a Montreal, CRCM5, MPI-ESM-LR Swedish Meteorological and Hydrological Institute (SMHI), RCA4, ICHEC-EC-EARTH Climate Limited-area Modelling Community (CLMcom), CCLM4-8-17-CLM3-5, ICHEC-EC-EARTH Climate Limited-area Modelling Community (CLMcom), CCLM5-0-2, ICHEC-EC-EARTH Swedish Meteorological and Hydrological Institute (SMHI), RCA4, NOAA-GFDL-GFDL-ESM2M National Center for Atmospheric Research, WRF, NOAA-GFDL-GFDL-ESM2M The historical runs begin 1950-01-01 (ICHEC-EC-EARTH and NOAA-GFDL-GFDL-ESM2M) or 1960-01-01 (MPI-M-MPI-ESM-LR) and end 2005-12-31. Historical runs are appended by rcp85 runs for years 2006-01-01 to 2019-12-31. All projection runs begin 2020-01-01 and end 2099-12-31. The routines (python) used to create and work with the data sets are available from this web page as well: BIAS-SIGNAL_plot.py; Discontinuity_Analysis.py; IBICUS-BIAS-CORRECTION.py; Monthly_Climatology_plot.py; Statistical_metric_plot.py; Time_Series_plot.py Quality: Not all of the domains have been downscaled by CORDEX RCMs. Therefore, data files for scenario rcp26 only contain 7 CORDEX domains; all other files contain 8 domains (see also https://cordex.org/domains/cordex-domain-description/) Units: K; K; K; kg m-2 s-1; W m-2; m s-1; percent GeoLocation: westBoundCoordinate: -180.0; westBoundCoordinateUnit: degrees East; eastBoundCoordinate: 180.0; eastBoundCoordinateUnit: degrees East; southBoundCoordinate: -90.0; southBoundCoordinateUnit: degrees North; northBoundCoordinate: 90.0; northBoundCoordinateUnit: degrees North Size: ICHEC-EC-EARTH: 137.7 GByte, MPI-M-MPI-ESM-LR: 130.6 GByte, NOAA-GFDL-GFDL-ESM2M: 55.5 GByte Format: netCDF DataSources: See the file "GloBCORD-HD_ESMs-RCMs.pdf" Contact: fuseini.yakubu (at) uni-hamburg.de; shabeh.hasson (at) uni-hamburg.de Webpage: https://www.geo.uni-hamburg.de/geographie/abteilungen/physische-geographie/arbeitsgruppen/ag-hareme.html https://www.fdr.uni-hamburg.de/record/17560 10.25592/uhhfdm.17560 oai:fdr.uni-hamburg.de:17560 eng doi:10.5281/zenodo.4686991 doi:10.5194/gmd-12-3055-2019 doi:10.25592/uhhfdm.17396 doi:10.25592/uhhfdm.17799 doi:10.25592/uhhfdm.17395 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Model Downscaling CORDEX Air Temperature Relative Humidity Surface Wind Speed Precipitation Amount Surface Downwelling Longwave Radiation CMIP5 ICDC Global Bias-Corrected CORDEX Datasets at Half Degree Resolution info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:16126 2024-12-12T12:02:18Z user-icdc user-cen user-uhh

Sadikni, Remon Rauschenbach, Quentin Kern, Stefan Notz, Dirk 2024-12-12 Abstract: This data set comprises time series of the monthly sea-ice area (SIA) in the Northern and Southern Hemisphere based on numerical models participating in CMIP6 (Climate Model Intercomparison Project #6). See description on https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/cmip6-sea-ice-area.html about how SIA is derived. See https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/cmip6-sea-ice-area.html to learn about the models included into this CMIP6 archive. SIA time series are provided for historical, piControl,and ssp runs (119, 126, 245, 370, 460, 585). The number of time series provided per run depends on the CMIP6 model; it might be zero. Table of contents: monthly sea-ice area Technical Info: Northern Hemisphere Historical: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_northern_hemisphere_historical; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1980, 466; temporalExtent_startDate: 1850-01-01; temporalExtent_endDate: 2014-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none piControl: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_northern_hemisphere_picontrol; dimensionName: NumberOfMonths, NumberofEnsembles; dimensionValue: 6012, 54; temporalExtent_startDate: 1850-01-01; temporalExtent_endDate: 2350-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp119: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_northern_hemisphere_ssp119; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 57; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp126: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_northern_hemisphere_ssp126; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 117; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp245: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_northern_hemisphere_ssp245; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 245; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp370: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_northern_hemisphere_ssp370; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 160; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp460: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_northern_hemisphere_ssp460; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 15; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp585: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_northern_hemisphere_ssp585; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 189; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none Southern Hemisphere: Historical: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_southern_hemisphere_historical; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1980, 462; temporalExtent_startDate: 1850-01-01; temporalExtent_endDate: 2014-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none piControl: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_southern_hemisphere_picontrol; dimensionName: NumberOfMonths, NumberofEnsembles; dimensionValue: 6012, 54; temporalExtent_startDate: 1850-01-01; temporalExtent_endDate: 2350-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp119: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_southern_hemisphere_ssp119; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 57; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp126: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_southern_hemisphere_ssp126; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 115; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp245: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_southern_hemisphere_ssp245; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 241; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp370: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_southern_hemisphere_ssp370; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 156; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp460: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_southern_hemisphere_ssp460; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 15; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none ssp585: standard_name: sea_ice_area; long_name: total_sea_ice_area_in_the_southern_hemisphere_ssp585; dimensionName: NumberOfMonths, TotalNumberofEnsembles; dimensionValue: 1032, 187; temporalExtent_startDate: 2015-01-01; temporalExtent_endDate: 2100-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none Methods: See description on https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/cmip6-sea-ice-area.html Units (all variables): 1e6 km2 geoLocations: Northern Hemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: 35.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: Northern Hemisphere Southern Hemisphere: westBoundLongitude: -180.0 degrees east; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: -35.0 degrees North; geoLocationPlace: Southern Hemisphere Size: Separately per hemisphere: 1 file including all runs (~19 MB), 1 file with historical runs (~9 MB), 1 file with piControl runs (~2.7 MB), 1 file for each ssp run (= 6 files, in total ~7.3 MB) - aka 9 netCDF and 9 ascii-txt files per hemisphere, aka in total 18 netCDF and 18 ascii-txt files with a total volume overall < 200 MB Format: netCDF; ascii-txt DataSources: https://www.wdc-climate.de/ui/q?query=CMIP6&page=0&rows=15 [last access: 2024-11-22] Contact: uhhsia.ifm (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/cmip6-sea-ice-area.html https://www.fdr.uni-hamburg.de/record/16126 10.25592/uhhfdm.16126 oai:fdr.uni-hamburg.de:16126 eng doi:10.25592/uhhfdm.11346 url:https://www.wdc-climate.de/ui/q?query=CMIP6&page=0&rows=15 url:https://pcmdi.llnl.gov/CMIP6/TermsOfUse doi:10.25592/uhhfdm.16125 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Marine Cryosphere Sea Ice Area Arctic Antarctic Numerical Modeling CMIP6 time series monthly University of Hamburg ICDC UHH CMIP6 Sea Ice Area Collection info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:11346 2025-12-05T09:29:13Z user-icdc user-cliccs user-cen

Rauschenbach, Quentin Doerr, Jakob Notz, Dirk Kern, Stefan 2024-03-05 Abstract: This data set comprises time series of the monthly sea-ice area (SIA) in the Northern Hemisphere (1 file) and in the Southern Hemisphere (1 file). SIA is derived from sea-ice concentration (SIC, also sea-ice area fraction) data of the following products: Walsh (version 2, only Northern Hemisphere), OSI SAF OSI-450a SIC climate data record / OSI-430a SIC interim climate data record, ESA-CCI+ phase 1 Higher Resolution SIC climate data record (Lavergne et al., 2023), HadISST (version 2), NASA-Team and Comiso-Bootstrap - both from the NOAA/NSIDC SIC climate data record (version 4.0). The monthly SIA is either directly computed from monthly SIC data or is derived as the mean of all daily SIA values. If required, the observational gap at the pole is interpolated. If required, temporal interpolation is applied - both to fill temporal (up to a maximum of 7 consecutive days) and spatial (if less than 1000 non-contiguous missing grid cells per day) gaps. Table of contents: Northern Hemisphere: HadISST_nsidc monthly sea-ice area; HadISST_orig monthly sea-ice area; ESA-CCI+ monthly sea-ice area, Comiso-Bootstrap monthly sea-ice area; NASA-Team monthly sea-ice area; OSI SAF monthly sea-ice area; Walsh monthly sea-ice area; Southern Hemisphere: HadISST_nsidc monthly sea-ice area; HadISST_orig monthly sea-ice area; ESA-CCI+ monthly sea-ice area, Comiso-Bootstrap monthly sea-ice area; NASA-Team monthly sea-ice area; OSI SAF monthly sea-ice area Technical Info: standard_name: sea-ice area; long_name: algorithm_specific hemispheric sea-ice area time-series; dimension: 2076; temporalExtent_startDate: 1850-01-01; temporalExtent_endDate: 2023-09-30; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: various SMMR SSM/I SSMIS; instrumentType: various multifrequency_microwave_radiometer; instrumentLocation: various Nimbus-7 DMSP-f8 DMSP-f11 DMSP-f13 DMSP-17; instrumentProvider: various Methods: See description on https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/uhh-sea-ice-area-product.html Units (all variables): 1e6 km2 geoLocations: Northern Hemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: 35.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: Northern Hemisphere Southern Hemisphere: westBoundLongitude: -180.0 degrees east; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: -35.0 degrees North; geoLocationPlace: Southern Hemisphere Size: Northern Hemisphere: 1 file, 7 variables, 2088 elements each (latest month with valid data is element 2085) Southern Hemisphere: 1 file, 6 variables, 2088 elements each (latest month with valid data is element 2085) Format: netCDF DataSources: https://nsidc.org/data/g10010 [last accessed: 2022-10-01] https://doi.org/10.15770/EUM_SAF_OSI_0013 [last accessed: 2023-03-10] https://doi.org/10.15770/EUM_SAF_OSI_0014 [last accessed: 2023-11-07] https://nsidc.org/data/g02202 [last accessed: 2024-01-24] https://www.metoffice.gov.uk/hadobs/hadisst2/data/HadISST.2.2.0.0_sea_ice_concentration.nc.gz [last accessed: 2023-01-09] https://dx.doi.org/10.5285/eade27004395466aaa006135e1b2ad1a [Last accessed: 2023-03-10] Contact: uhhsia.ifm (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/uhh-sea-ice-area-product.html https://www.fdr.uni-hamburg.de/record/11346 10.25592/uhhfdm.11346 oai:fdr.uni-hamburg.de:11346 eng url:https://nsidc.org/data/g10010 url:https://navigator.eumetsat.int/product/EO:EUM:DAT:0645 url:https://nsidc.org/data/g02202 url:https://www.metoffice.gov.uk/hadobs/hadisst2/data/HadISST.2.2.0.0_sea_ice_concentration.nc.gz url:https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/uhh-sea-ice-area-product.html doi:10.25592/uhhfdm.8559 doi:10.15770/EUM_SAF_OSI_0013 doi:10.5285/eade27004395466aaa006135e1b2ad1a url:https://zenodo.org/records/10014535 doi:10.25592/uhhfdm.16126 doi:10.25592/uhhfdm.8525 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Marine Cryosphere Sea ice area Arctic Antarctic Observational data Time series monthly University of Hamburg University of Bergen ICDC UHH Sea Ice Area Product info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:8559 2025-12-05T09:29:13Z user-cen user-icdc user-cliccs

Doerr, Jakob Notz, Dirk Kern, Stefan 2021-01-20 Abstract: This data set comprises time series of the monthly sea-ice area (SIA) in the Northern Hemisphere (1 file) and in the Southern Hemisphere (1 file). SIA is derived from sea-ice concentration (SIC, also sea-ice area fraction) data of the following products: Walsh (version 2, only Northern Hemisphere), OSI-450 / OSI-430-b, HadISST (version 2), NASA-Team and Comiso-Bootstrap - both from the NSIDC/NOAA SIC climate data record (version 3.1). The monthly SIA is either directly computed from monthly SIC data or is derived as the mean of all daily SIA values. If required, the observational gap at the pole is interpolated. If required, temporal interpolation is applied - both to fill temporal (up to a maximum of 7 consecutive days) and spatial (if less than 1000 non-contiguous missing grid cells per day) gaps. Table of contents: Northern Hemisphere: HadISST_nsidc monthly sea-ice area; HadISST_orig monthly sea-ice area; Comiso-Bootstrap monthly sea-ice area; NASA-Team monthly sea-ice area; OSI-SAF monthly sea-ice area; Walsh monthly sea-ice area; Southern Hemisphere: HadISST_nsidc monthly sea-ice area; HadISST_orig monthly sea-ice area; Comiso-Bootstrap monthly sea-ice area; NASA-Team monthly sea-ice area; OSI-SAF monthly sea-ice area Technical Info: standard_name: sea-ice area; long_name: algorithm_specific hemispheric sea-ice area time-series; dimension: 2040; temporalExtent_startDate: 1850-01-01; temporalExtent_endDate: 2019-12-31; temporalResolution: monthly; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionYdirection: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: various SMMR SSM/I SSMIS; instrumentType: various multifrequency_microwave_radiometer; instrumentLocation: various Nimbus-7 DMSP-f8 DMSP-f11 DMSP-f13 DMSP-17; instrumentProvider: various Methods: See description on https://icdc.cen.uni-hamburg.de/en/cryosphere/uhh-sea-ice-area-product.html Units (all variables): 1e6 km2 geoLocations: Northern Hemisphere: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: 35.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: Northern Hemisphere Southern Hemisphere: westBoundLongitude: -180.0 degrees east; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: -35.0 degrees North; geoLocationPlace: Southern Hemisphere Size: Northern Hemisphere: 1 file, 6 variables, 2040 elements each Southern Hemisphere: 1 file, 5 variables, 2040 elements each Format: netCDF DataSources: https://nsidc.org/data/g10010 [last accessed: 2019-09-13] https://doi.org/10.15770/EUM_SAF_OSI_0008 [last accessed: 2021-03-16] http://www.osi-saf.org/?q=content/global-sea-ice-concentration-interim-climate-data-record-release-2 [last accessed: 2021-03-16] https://nsidc.org/data/g02202 [last accessed: 2020-08-14] https://www.metoffice.gov.uk/hadobs/hadisst2/data/HadISST.2.2.0.0_sea_ice_concentration.nc.gz [last accessed: 2020-08-14] Contact: uhhsia.ifm (at) uni-hamburg.de Web page: https://icdc.cen.uni-hamburg.de/en/cryosphere/uhh-sea-ice-area-product.html https://www.fdr.uni-hamburg.de/record/8559 10.25592/uhhfdm.8559 oai:fdr.uni-hamburg.de:8559 eng url:https://nsidc.org/data/g10010 url:http://www.osi-saf.org/?q=content/global-sea-ice-concentration-interim-climate-data-record-release-2 url:https://nsidc.org/data/g02202 url:https://www.metoffice.gov.uk/hadobs/hadisst2/data/HadISST.2.2.0.0_sea_ice_concentration.nc.gz url:https://icdc.cen.uni-hamburg.de/en/cryosphere/uhh-sea-ice-area-product.html doi:10.25592/uhhfdm.8526 doi:10.15770/EUM_SAF_OSI_0008 doi:10.25592/uhhfdm.8525 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Climate Research Marine Cryosphere Sea ice area Arctic Antarctic Time series monthly University of Hamburg University of Bergen ICDC UHH Sea Ice Area Product info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:18219 2026-01-09T11:34:16Z user-cen user-icdc user-uhh user-cliccs

Sadikni, Remon Kern, Stefan 2026-01-09 Abstract: Reflectance measurements carried out at Terra MODIS bands 1, 3 and 4 are combined in a spectral un-mixing approach to estimate the melt-pond fraction on Arctic sea ice north of 60°N for months June through August for the years 2000 through 2024. The resulting data set has daily temporal sampling and is offered on a polar-stereographic grid (true latitude is 70°N) with 500 m x 500 m and 12.5 km x 12.5 km grid resolution. The data files contain, in addition to the melt-pond fraction, also the fraction of open water between the sea ice and the net sea-ice surface fraction, i.e. the fraction of sea ice without melt ponds. For information about the approach used and the validation of the data set offered here we refer to Rösel et al. (2012) and Sadikni and Kern (2025). TableOfContents: 500 m x 500 m product: grid-cell fraction of melt ponds (on sea ice); grid-cell fraction of sea ice without melt ponds; grid-cell fraction of open water 12.5 km x 12.5 km product: grid-cell fraction of melt ponds (on sea ice); grid-cell fraction of sea ice without melt ponds; grid-cell fraction of open water; grid-cell_fraction_of_melt_ponds standard_deviation; grid-cell_fraction_of_sea_ice_without_melt_ponds standard_deviation; grid-cell_fraction_of_open_water standard_deviation; number of clear-sky 500 m grid cells; clear-sky mask While the 12.5 km x 12.5 km comes with latitude and longitude values for each grid cell, those of the 500 m x 500 m product are provided in a separate netCDF file. Note that the grid resolution of 12.5 km (and 500m) is only valid at the latitude at which the tangential plane of the polar stereographic projection touches the Earth's surface - here 70°N. North of this latitude the actual grid cell area is smaller, south of it it is larger (see also https://nsidc.org/data/user-resources/help-center/guide-nsidcs-polar-stereographic-projection#anchor-grid-distortion). In order to assist in the computation of the actual area (in km2) covered by melt ponds (or the other two surface types) we provide a netCDF file of the true area of each grid cell of the 12.5 km x 12.5 km product. Technical Info: 500 m x 500 m product: dimensions: 13286 columns x 13293 rows; temporalExtent_startDate: 2000-06-01; temporalExtent_endDate: 2024-08-31; temporalResolution: daily; spatialResolution: 500; spatialResolutionUnit: meter; horizontalResolutionXdirection: 500; horizontalResolutionXdirectionUnit: meter; horizontalResolutionYdirection: 500; horizontalResolutionYdirectionUnit: meter; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Moderate Resolution Spectroradiometer (MODIS); instrumentType: 32-band imaging spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA 12.5 km x 12.5 km product: dimensions: 531 columns x 531 rows; temporalExtent_startDate: 2000-06-01; temporalExtent_endDate: 2024-08-31; temporalResolution: daily; spatialResolution: 12500; spatialResolutionUnit: meter; horizontalResolutionXdirection: 12500; horizontalResolutionXdirectionUnit: meter; horizontalResolutionYdirection: 12500; horizontalResolutionYdirectionUnit: meter; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Moderate Resolution Spectroradiometer (MODIS); instrumentType: 32-band imaging spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA Missing data (either because the MOD09GA product has gaps or because the melt-pond fraction retrieval failed): 2000-08-06 to 2000-08-17; 2001-06-16 to 2001-07-02; 2002-06-15 to 2002-06-19; 2002-07-11; 2004-06-20 to 2004-06-22; 2005-07-21; 2008-08-06 to 2008-08-08; 2008-08-11; 2008-08-22; 2010-06-17; 2010-08-01; 2010-08-16; 2013-06-09; 2013-06-15; 2014-08-02; 2016-06-07; 2016-06-08; 2016-06-11; 2016-08-12; 2018-08-04; 2021-08-11; 2024-06-21; 2024-06-22; 2024-06-25. Methods: For a description of the methods used see Sadikni and Kern, 2025. Units: 500 m x 500 m product: 1, 1, 1 12500 m x 12500 m product: 1, 1, 1, 1, 1, 1, 1, 1 geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: 60.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: northern hemisphere Size: 500 m x 500 m product: Single file: 2.1 GigaByte; zip-file or one month: 1.5 to 4.0 GigaByte (depends on gaps due to cloud cover); total volume (as unpacked netCDF): 4.87 TeraByte 12.5 km x 12.5 km product: Single daily file: ~10 MegaByte; annually aggregated file: ~670 MegaByte; zip-file of one month: 60 to 100 MegaByte (depends on gaps due to cloud cover); total volume (as unpacked netCDF): 22.8 GigaByte Format: netCDF DataSources: MODerate resolution Imaging Spectroradiometer MODIS aboard the Earth Observation Satellite (EOS) Terra collection 6.1 product MOD09A1 of the surface spectral reflectance: https://lpdaac.usgs.gov/products/mod09gav061/ (last access: Dec. 6, 2024). Contact: remon.sadikni (at) uni-hamburg.de; stefan.kern (at) uni-hamburg.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/cryosphere/arctic-meltponds.html https://www.fdr.uni-hamburg.de/record/18219 10.25592/uhhfdm.18219 oai:fdr.uni-hamburg.de:18219 eng doi:10.5194/tc‐6‐431‐2012 doi:10.1594/WDCC/MODIS__Arctic__MPF_V02 doi:10.5194/essd-2025-757 doi:10.25592/uhhfdm.18070 doi:10.25592/uhhfdm.18069 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode sea ice melt ponds Arctic surface type fraction summer melt satellite remote sensing mixed pixel approach artificial neural network MODIS ICDC Daily melt-pond fraction on Arctic sea ice from TERRA MODIS visible imagery info:eu-repo/semantics/other dataset