2026-05-14T13:20:53Z https://www.fdr.uni-hamburg.de/oai2d

oai:fdr.uni-hamburg.de:9104 2023-02-01T14:20:42Z user-cliccs user-uhh

Gresse, Eduardo Hedemann, Christopher Petzold, Jan Held, Hermann Aykut, Stefan Li, Chao Schneider, Uwe Ratter, Beate Oßenbrügge, Jürgen Fröhle, Peter Köhl, Michael Wiener, Antje Bassen, Alexander Beyer, Jürgen Brüggemann, Michael Busch, Timo d'Amico, Emilie Frisch, Thomas Guenther, Lars Jarke-Neuert, Johannes Johnson, Matthew Lange, Andres Pavenstädt, Christopher Perino, Grischa Sander, Junis Scheffran, Jürgen Schenuit, Felix Wickel, Martin Wilkens, Jan Zengerling, Cathrin Neuburger, Martina Datchoua-Tirvaudey, Alvine Schnegg, Michael Notz, Dirk Lüdemann, Jana Schmitt, Tobias Singer, Katrin Milinski, Sebastian Suarez-Gutierrez, Laura Stammer, Detlef Engels, Anita Marotzke, Jochem Gresse, Eduardo Hedemann, Christopher Petzold, Jan 2021-06-09 Series In the annual Hamburg Climate Futures Outlook, CLICCS researchers make the first systematic attempt to assess which climate futures are plausible, by combining multidisciplinary assessments of plausibility. Current Issue The inaugural 2021 Hamburg Climate Futures Outlook addresses the question: Is it plausible that the world will reach deep decarbonization by 2050? Websites www.cliccs.uni-hamburg.de/results/hamburg-climate-futures-outlook.html www.cliccs.uni-hamburg.de https://www.fdr.uni-hamburg.de/record/9104 10.25592/uhhfdm.9104 oai:fdr.uni-hamburg.de:9104 eng url:https://www.cliccs.uni-hamburg.de/results/hamburg-climate-futures-outlook.html url:https://www.cliccs.uni-hamburg.de doi:10.25592/uhhfdm.9103 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode CLICCS, Climate Research, Plausibility, Climate Futures, HCFO, Hamburg Climate Futures Outlook, DFG Project Number 390683824 Hamburg Climate Futures Outlook: Assessing the plausibility of deep decarbonization by 2050 info:eu-repo/semantics/report publication-report

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: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: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: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: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:11230 2023-02-01T14:20:42Z user-cliccs user-uhh

Aykut, Stefan Bähring, Jill Bassen, Alexander Beer, Christian Brovkin, Victor Brüggemann, Michael Busch, Timo Commelin, Solange d'Amico, Emilie de Vrese, Philipp Engels, Anita Frisch, Thomas Fröhlich, Christiane Fünfgeld, Anna Gresse, Eduardo Guenther, Lars Guillén Bolaños, Tania Hanf, Franziska S. Hawxwell, Tom Held, Hermann Hoffmann, Peter Huang-Lachmann, Jo-Ting Huch, Charlotte Jantke, Kerstin Jarke-Neuert, Johannes Johnson, Matthew Kleinen, Thomas Kleinen-von Königslöw, Katharina Knoblauch, Christian Köhl, Michael Kutzbach, Lars Langendijk, Gaby S. Li, Chao López-Rivera, Andrés Marotzke, Jochem Mosuela, Cleovi Müller, Franziska Neuburger, Martina Neumann, Manuel Notz, Dirk Pagnone, Anna Pavenstädt, Christopher Pein, Johannes Perino, Grischa Reveco Umaña, Cristóbal Rödder, Simone Rothe, Delf Rötzel, Theresa Scheffran, Jürgen Schenuit, Felix Schneider, Uwe Schröder, Ursula Schrum, Corinna Seiffert, Rita Sillmann, Jana Soans, Erika Struve, Svenja Vogler, Anselm Wickel, Martin Wiener, Antje Wilkens, Jan Zengerling, Cathrin Engels, Anita Marotzke, Jochem Gresse, Eduardo López-Rivera, Andrés Pagnone, Anna Wilkens, Jan 2023-02-01 Series In the annual Hamburg Climate Futures Outlook, CLICCS researchers make the first systematic attempt to assess which climate futures are plausible, by combining multidisciplinary assessments of plausibility. Current Issue The purpose of this second Hamburg Climate Futures Outlook is to systematically analyze and assess the plausibility of certain well-defined climate futures based on present knowledge of social drivers and physical processes. In particular, we assess the plausibility of those climate futures that are envisioned by the 2015 Paris Agreement, namely holding global warming to well below 2°C and, if possible, to 1.5°C, relative to pre-industrial levels (UNFCCC 2015, Article 2 paragraph 1a). The world will have to reach a state of deep decarbonization by 2050 to be compliant with the 1.5°C goal. We therefore work with a climate future scenario that combines emissions and temperature goals. Websites www.cliccs.uni-hamburg.de/results/hamburg-climate-futures-outlook.html www.cliccs.uni-hamburg.de https://www.fdr.uni-hamburg.de/record/11230 10.25592/uhhfdm.11230 oai:fdr.uni-hamburg.de:11230 eng url:https://www.cliccs.uni-hamburg.de/results/hamburg-climate-futures-outlook.html url:https://www.cliccs.uni-hamburg.de doi:10.25592/uhhfdm.9103 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode CLICCS, Climate Research, Climate Plausibility, Climate Futures, Social driver of decarbonization, Climate tipping points, 1.5°C warming, Decarbonization, Paris Agreement temperature goals, societal transformation for climate change, Sustainable climate adaptation, HCFO, Hamburg Climate Futures Outlook, DFG Project number 390683824, Hamburg Climate Futures Outlook: The plausibility of a 1.5°C limit to global warming - social drivers and physical processes info:eu-repo/semantics/report publication-report

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: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: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:18182 2025-12-27T21:23:50Z user-cliccs user-uhh

Anne Gerstenberg Kai-Uwe Schnapp Grischa Perino Johannes Jarke Neuert Ella Karnik Hinks Sarah Fenske Anne Gerstenberg Grischa Perino Kai-Uwe Schnapp 2025-12-09 Data set title: “Policymakers' perceptions of climate policy instruments” Project title: CLICCS – B2, WP3 Project duration: 10.2021-12.2025 Project participants: Prof. Grischa Perino, Prof. Kai-Uwe Schnapp, Dr. Johannes Jarke-Neuert, Dr. Anne Gerstenberg, Sarah Fenske, Ella Karnik Hinks This research project was set up in order to further a better understanding of the goals, perceptions and preferences about specific climate policy instruments from those who help shape and implement them. For an extensive description of the data please refer to: 10.25592/uhhfdm.16313 Type of data: qualitative semi-structured interviews Cases: European Union, Germany, France, Poland Interview partners: policymakers Timeframe: 2022-2023 Contents uploaded here: Extensive data documentation document Anonymized interview transcripts Questionnaire Codebook Maxqda with all completed data analysis https://www.fdr.uni-hamburg.de/record/18182 10.25592/uhhfdm.18182 oai:fdr.uni-hamburg.de:18182 eng doi:10.25592/uhhfdm.18181 info:eu-repo/semantics/restrictedAccess qualitative methods, semi-structred interviews, interviews Policymakers' perceptions of climate policy instruments 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: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: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: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: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:16314 2026-02-04T09:47:08Z user-uhh user-cen user-cliccs user-esrah

Gerstenberg, Anne Schnapp, Kai-Uwe Jarke-Neuert, Johannes Perino, Grischa Karnik Hinks, Ella Fenske, Sarah 2024-11-28 On the CSS Working Paper Series The CSS Working Paper Series welcomes papers on all aspects connected with the research focus of the center and gives authors a chance to increase the circulation, visibility, and impact of their research. The Working Paper Series presents results from ongoing and cutting-edge research at the CSS and partner institutions. Working paper topics reflect the various disciplines involved in the CSS. The Series serves to publish (preliminary) results quickly, and Working Papers may prepare a publication in an academic journal at a later date. Replication studies are also welcome. Abstract of Current Paper This paper documents the data collection and coding process of the qualitative interview data from the “Policymakers’ Perceptions of Climate Policy Instruments” project. This data documentation follows the standard for qualitative research and data policy of the American Journal of Political Science (AJPS, 2023) and its guidelines for the provision of replication files. Given the impossibility of making the transcripts of our interviews public, we aim at least to present the entire process of data collection and analysis as transparently as possible, in order to enable a critical reception of our work to the interested reader. We begin by briefly outlining the research interest of the project and presenting the research question and its relevance. We then justify the choice of cases and provide a brief description of the selected cases. Next, we describe the chosen method and the design of the qualitative semi-structured questionnaire. This is followed by an account of the data collection process and a description of the final sample. Finally, we explain our method of analysis. Website www.wiso.uni-hamburg.de/en/forschung/forschungszentren/css-html https://www.fdr.uni-hamburg.de/record/16314 10.25592/uhhfdm.16314 oai:fdr.uni-hamburg.de:16314 eng url:https://portal.issn.org/resource/ISSN/2699-8327 doi:10.25592/uhhfdm.16313 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode climate policy policy instruments qualitative methods semi-structured expert interviews qualitative content analysis Documentation for the dataset of the research project "Policymakers' perceptions of Climate Policy Instruments" info:eu-repo/semantics/workingPaper publication-workingpaper

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: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:14272 2024-05-13T06:24:58Z user-cliccs user-uhh

Aykut, Stefan C. Hüppauff, Lukas Frerichs, Lea Fünfgeld, Anna Walter, Yannick Aguirre, Flinn Mollyk, Aitana Ritterbach, Lennart Hildebrandt, Franziska 2024-05-07 Deutschland hat sich das Ziel gesetzt, bis 2045 die Klimaneutralität zu erreichen. Die Umsetzung hinkt den Erfordernissen jedoch hinterher, denn der hochkomplexe Transformationsprozess fordert bestehende Interessen, Gewohnheiten und Geschäftsmodelle heraus. Die gesellschaftlichen Umsetzungshürden für ambitionierten Klimaschutz erscheinen hoch. Andererseits wird Klimapolitik auch immer wieder durch gesellschaftliches Engagement angetrieben. Vor diesem Hintergrund entwickelt die Mercator-Stiftungsprofessur für Soziologie Methoden, um zu bewerten, inwieweit eine umfassende Transformation auch in politischer und sozialer Hinsicht plausibel ist. In einem jährlichen Assessment werden ausgewählte gesellschaftliche Treiber und ihre jeweiligen Dynamiken, Kontextbedingungen und Wirkungslogiken untersucht. Im Mai 2024 wurde der erste Klimawende Ausblick veröffentlicht. Er legt den Grundstein für die folgende Studienreihe, indem er den Analyserahmen zur Plausibilität von Transformationsprozessen vorstellt und einen methodischen Baukasten für die Analyse der deutschen Klimawende entwickelt. Darüber hinaus werden vier von insgesamt zwölf sozialen Treibern der Klimawende in einer erste Teilanalyse untersucht: deutsche Klimapolitik im europäischen Rahmen, globale Klimagovernance, Klimabewegung und -proteste, und Klimaklagen. https://www.fdr.uni-hamburg.de/record/14272 10.25592/uhhfdm.14272 oai:fdr.uni-hamburg.de:14272 handle:uhh.de/wiso-klimawende doi:10.25592/uhhfdm.14271 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Dekarbonisierung Ökologische Transformation Energiewende Plausibilität Klimawende Ausblick 2024 info:eu-repo/semantics/report publication-report

oai:fdr.uni-hamburg.de:14338 2024-11-15T19:03:26Z user-uhh user-cliccs

Sultana, Ferdous 2024-06-02 This dataset includes transcripts of 12 expert interviews conducted in Bangladesh and online on the topic: "Climate Change as a Threat Multiplier: Expert Perspectives on Human Security in Bangladesh". The interviews were conducted in English and lasted 45-90 minutes. The transcripts were edited for minimal grammatical correctness and to remove redundancies and interjections common in spoken language but do not add value to the transcript. The content analysis coding scheme of the interview data can be found in this repository under the DOI number: doi.org/10.25592/uhhfdm.14393. https://www.fdr.uni-hamburg.de/record/14338 10.25592/uhhfdm.14338 oai:fdr.uni-hamburg.de:14338 eng doi:10.25592/uhhfdm.14337 info:eu-repo/semantics/restrictedAccess South Asia, Climate Change, Human Security, Conflict, Migration, Expert Interviews Transcripts of expert interviews on "Climate Change as a Threat Multiplier: Expert Perspectives on Human Security in Bangladesh" info:eu-repo/semantics/other dataset

oai:fdr.uni-hamburg.de:14393 2024-11-15T19:04:29Z user-cliccs user-uhh

Sultana, Ferdous Scheffran, Jürgen 2024-06-14 This dataset includes the content analysis coding scheme conducted based on the transcripts of expert interviews on "Climate Change as a Threat Multiplier: Expert Perspectives on Human Security in Bangladesh". The transcripts can be found in this data repository under the DOI number: 10.25592/uhhfdm.14338. https://www.fdr.uni-hamburg.de/record/14393 10.25592/uhhfdm.14393 oai:fdr.uni-hamburg.de:14393 eng doi:10.25592/uhhfdm.14392 info:eu-repo/semantics/restrictedAccess South Asia, Climate Change, Human Security, Conflict, Migration, Expert Interviews Coding scheme of expert interviews on "Climate Change as a Threat Multiplier: Expert Perspectives on Human Security in Bangladesh" 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: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: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: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: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: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