The total land water storage anomalies are aggregated from the Global Land Data Assimilation System (GLDAS) NOAH model. GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on the GLDAS project and model outputs please visit https://ldas.gsfc.nasa.gov/gldas. The aggregated land water anomalies (sum of soil moisture, snow, canopy water) provided here can be used for comparison against and evaluations of the observations of Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO over land. The monthly anomalies are computed over the same days during each month as GRACE and GRACE-FO data, and are provided on monthly 1 degree lat/lon grids in NetCDF format. Currently, the days included in these monthly anomaly computation are same as GRACE-FO monthly Level-2 RL06.3 JPL solutions.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.GLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. This data product is an Early Product for GLDAS-2.1 Noah 0.25 degree monthly dataset. The monthly data product was generated through temporal averaging of GLDAS-2.1 Noah 3-hourly data simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present.The GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency’s AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.The GLDAS-2.1 products supersede their corresponding GLDAS-1 products.The GLDAS-2.1 data are archived and distributed in NetCDF format.

Global Land Data Assimilation System Version 2 (hereafter, GLDAS-2) has two components: one forced entirely with the Princeton meteorological forcing data (hereafter, GLDAS-2.0), and the other forced with a combination of model and observation based forcing data sets (hereafter, GLDAS-2.1).

This data set, GLDAS-2.1 Noah 1.0 degree 3-hourly, simulated with the Noah Model 3.3 in Land Information System (LIS) Version 7, contains 36 land surface fields from January 2000 to present. GLDAS-2.1 simulation is forced by a combination of National Oceanic and Atmospheric Administration/National Center for Environmental Prediction's Global Data Assimilation System (GDAS) atmospheric analysis fields, spatially and temporally disaggregated Global Precipitation Climatology Project (GPCP) precipitation fields, and observation based downward shortwave and longwave radiation fields derived using the method of the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET). This data set supersedes GLDAS-1 products, in which improvements are made in the use of GPCP and the disaggregation scheme, and quality control for the AGRMET dataset. The GPCP 1-degree Daily (1DD) dataset is used and disaggregated to 3-hourly intervals, whereas GLDAS-1 used the NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP) pentad dataset and disaggregated to 6-hourly. The gaps and irregularity in the AGRMET shortwave downward flux are alleviated by additional filtering and bias correction to the Surface Radiation Budget (SRB) dataset. Furthermore, the spatial aggregation scheme of GDAS dataset is revised in GLDAS-2.1.

The simulation started on 1 January 2000 using the conditions from the GLDAS-2.0 simulation and was forced with GDAS and the disaggregated GPCP. The AGRMET radiation forcing is added for 1 March 2001 onwards.

The simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30), as well as the Noah model default land cover (Modified IGBP MODIS 20-category classification) and soil texture (Hybrid STATSGO/FAO) datasets.

The GLDAS-2.1 data are archived and distributed in NetCDF format.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.GLDAS-2.0 is one of two components of the GLDAS Version 2 (GLDAS-2) dataset, the second being GLDAS-2.1. GLDAS-2.0 is reprocessed with the updated Princeton Global Meteorological Forcing Dataset (Sheffield et al., 2006) and upgraded Land Information System Version 7 (LIS-7). It covers the period 1948-2010, and will be extended to more recent years as corresponding forcing data become available. The model simulation was initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulation used the common GLDAS datasets for land cover (MCD12Q1: Friedl et al., 2010), land water mask (MOD44W: Carroll et al., 2009), soil texture (Reynolds, 1999), and elevation (GTOPO30). The MODIS based land surface parameters are used in the current GLDAS-2.x products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012). Documentation: Readme How-to GES DISC Hydrology Documentation Provider's Note: the names with extension _tavg are variables averaged over the past 3-hours, the names with extension '_acc' are variables accumulated over the past 3-hours, the names with extension '_inst' are instantaneous variables, and the names with '_f' are forcing variables.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products. GLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. This data product, reprocessed in January 2020, is for GLDAS-2.1 Noah monthly 1.0 degree data from the main production stream and it is a replacement to its previous version.The monthly data product was generated through temporal averaging of GLDAS-2.1 Noah 3-hourly data simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present. The GLDAS-2.1 data are archived and distributed in NetCDF format. The GLDAS-2.1 products supersede their corresponding GLDAS-1 products.The GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.

Global Land Data Assimilation System Version 2 (hereafter, GLDAS-2) has two components: one forced entirely with the Princeton meteorological forcing data (hereafter, GLDAS-2.0), and the other forced with a combination of model and observation based forcing data sets (hereafter, GLDAS-2.1).

This data set, GLDAS-2.0 0.25 degree 3-hourly, contains a series of land surface parameters simulated from the Noah Model 3.3, currently covers from 1948 to 2010 and will be extended to recent years as the data set becomes available.

The model simulation was initialized on simulation date January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulation was forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). The simulation used the common GLDAS data sets for land cover (MCD12Q1: Friedl et al., 2010), land water mask (MOD44W: Carroll et al., 2009), soil texture (Reynolds, 1999), and elevation (GTOPO30). The MODIS based land surface parameters are used in the current GLDAS-2.x products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012).

The main objective for Version 2 is to create more climatologically consistent data sets using the Princeton forcing data sets extending from 1948. In Version 1, forcing sources switched several times throughout the record from 1979 to present, which introduced unnatural trends and exhibited highly uncertain forcing fields in 1995-1997. Other enhancements made in Version 2 include model version upgrade, switching to MODIS based land surface parameter data sets, and initialization of soil moisture over desert. In NOAH model, the bottom layer temperature data set was also updated. More details regarding the land surface parameter data changes at http://ldas.gsfc.nasa.gov/gldas/.

WGRIB or other GRIB reader is required to read the files. The data set applies a user-defined parameter table to indicate the contents and parameter numbers. The GRIBTAB file (http://disc.sci.gsfc.nasa.gov/hydrology/grib_tabs/gribtab_GLDAS_V2.txt) shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.

There are four vertical levels for the Soil Moisture (PDS 086) and Soil Temperature (PDS 085) in the Noah GRIBT files. For more information, please see the README Document at http://hydro1.sci.gsfc.nasa.gov/data/s4pa/GLDAS/GLDAS_NOAH025_3H.020/doc/README.GLDAS2.pdf or the GrADS ctl file at ftp://hydro1.sci.gsfc.nasa.gov/data/gds/GLDAS/GLDAS_NOAH025_3H.020.ctl.

This data set contains a series of land surface parameters simulated from the Noah 2.7.1 model in the Global Land Data Assimilation System (GLDAS). The data are in 1.0 degree resolution and range from January 1979 to present. The temporal resolution is 3-hourly.

This simulation was forced by a combination of NOAA/GDAS atmospheric analysis fields, spatially and temporally disaggregated NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP) fields, and observation based downward shortwave and longwave radiation fields derived using the method of the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET).

The simulation was initialized on 1 January 1979 using soil moisture and other state fields from a GLDAS/Noah model climatology for that day of the year.

WGRIB or another GRIB reader is required to read the files. The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.

For more information, please see the README Document.

El Sistema global de asimilación de datos de la Tierra de la NASA, versión 2 (GLDAS-2), tiene tres componentes: GLDAS-2.0, GLDAS-2.1 y GLDAS-2.2. GLDAS-2.0 se genera por completo con los datos de entrada de los factores meteorológicos de Princeton y proporciona una serie coherente en el tiempo desde 1948 hasta 2014. GLDAS-2.1 se aplica de forma forzosa con una combinación de datos de modelos y observaciones de 2000 …

La versione 2 del Global Land Data Assimilation System (GLDAS-2) della NASA è composta da tre componenti: GLDAS-2.0, GLDAS-2.1 e GLDAS-2.2. GLDAS-2.0 viene forzato interamente con i dati di input delle forze meteorologiche di Princeton e fornisce una serie temporalmente coerente dal 1948 al 2014. GLDAS-2.1 viene forzato con una combinazione di dati del modello e di osservazione dal 2000 …

222Radon flux map based on GLDAS Noah soil moisture reanalysis; monthly fluxes for 2006-2012; Karstens et al., ACP, doi:10.5194/acp-15-12845-2015 Karstens, U., Levin, I., Schmidthüsen, D., Schwingshackl, C. (2015). 222Radon flux map for Europe based on GLDAS Noah soil moisture, 2006-01-01–2012-11-30, Miscellaneous, https://hdl.handle.net/11676/OPun_V09Pcat5jomRRF-5o0H

Monthly radon flux map for Europe 2006-2023 based on soil uranium content (EANR, 2019, https://data.europa.eu/doi/10.2760/520053), soil properties (ESDB, Hiederer, 2013, https://doi.org/10.2788/94128), and GLDAS-Noah v2.1 soil moisture reanalysis (Beaudoing and Rodell, 2020, https://doi.org/10.5067/SXAVCZFAQLNO). The radon flux model is described in Karstens et al., 2015, https://doi.org/10.5194/acp-15-12845-2015. Karstens, U., Levin, I. (2024). traceRadon monthly radon flux map for Europe 2006-2023 (based on GLDAS-Noah v2.1 soil moisture), 2006-01-01–2023-12-30, Miscellaneous, https://hdl.handle.net/11676/nOaxBJY97lbajKnqDWgKLdo5

The goal of NLDAS is to construct quality-controlled, and spatially and temporally consistent, land-surface model (LSM) datasets from the best available observations and model output to support modeling activities. Specifically, this system is intended to reduce the errors in the stores of soil moisture and energy which are often present in numerical weather prediction models, and which degrade the accuracy of forecasts. NLDAS is currently running in near real-time on a 1/8th-degree grid over central North America; retrospective NLDAS datasets and simulations also extend back to January 1979. NLDAS constructs a forcing dataset from gauge-based observed precipitation data (temporally disaggregated using Stage II radar data), bias-correcting shortwave radiation, and surface meteorology reanalyses to drive several different LSMs to produce model outputs of surface fluxes, soil moisture, and snow cover. NLDAS is a collaboration project among several groups: NOAA/NCEP's Environmental Modeling Center (EMC), NASA's Goddard Space Flight Center (GSFC), Princeton University, the University of Washington, the NOAA/NWS Office of Hydrological Development (OHD), and the NOAA/NCEP Climate Prediction Center (CPC). NLDAS is a core project with support from NOAA's Climate Prediction Program for the Americas (CPPA). Data from the project can be accessed from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) as well as from the NCEP/EMC NLDAS website. This service provides access to NASA's North American Land Data Assimilation System (NLDAS) hourly Mosaic land surface model data.

Monthly radon flux map for Europe 2006-2022 based on soil uranium content (EANR, 2019, https://data.europa.eu/doi/10.2760/520053), soil properties (ESDB, Hiederer, 2013, https://doi.org/10.2788/94128), and GLDAS-Noah v2.1 soil moisture reanalysis (Beaudoing and Rodell, 2020, https://doi.org/10.5067/SXAVCZFAQLNO). The radon flux model is described in Karstens et al., 2015, https://doi.org/10.5194/acp-15-12845-2015. Karstens, U., Levin, I. (2023). traceRadon monthly radon flux map for Europe 2006-2022 (based on GLDAS-Noah v2.1 soil moisture), 2006-01-01–2022-12-30, Miscellaneous, https://hdl.handle.net/11676/5-Z-zRaqFgddALv0ohLonzWD

Precipitation is water released from clouds in the form of rain, sleet, snow, or hail. It is the primary source of recharge to the planet's fresh water supplies. This map contains a historical record showing the volume of precipitation that fell during each month from March 2000 to the present. Snow and hail are reported in terms of snow water equivalent - the amount of water that will be produced when they melt. Dataset SummaryThe GLDAS Precipitation layer is a time-enabled image service that shows average monthly precipitation from 2000 to the present, measured in millimeters. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: PrecipitationUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean. Mean precipitation for a given month is calculated over the entire period of record - 2000 to present. Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. If you do this, make sure to also select a specific variable. The minimum time extent is one month, and the maximum is 8 years. Variables: This layer has three variables: total precipitation, rainfall and snowfall. By default total is shown, but you can select a different variable using the multidimensional filter, or by applying the relevant raster function. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tool.

Daily radon flux map for Europe 2023 based on soil uranium content (EANR, 2019, https://data.europa.eu/doi/10.2760/520053), soil properties (ESDB, Hiederer, 2013, https://doi.org/10.2788/94128), and GLDAS-Noah v2.1 soil moisture reanalysis (Beaudoing and Rodell, 2020, https://doi.org/10.5067/E7TYRXPJKWOQ). The radon flux model is described in Karstens et al., 2015, https://doi.org/10.5194/acp-15-12845-2015. Karstens, U., Levin, I. (2024). traceRadon daily radon flux map for Europe 2023 (based on GLDAS-Noah v2.1 soil moisture), 2023-01-01–2023-12-31, Miscellaneous, https://hdl.handle.net/11676/l3ILxEvl5we3w3uOQSt1WyV2

Soils and soil moisture greatly influence the water cycle and have impacts on runoff, flooding and agriculture. Soil type and soil particle composition (sand, clay, silt) affect soil moisture and the ability of the soil to retain water. Soil moisture is also affected by levels of evaporation and plant transpiration, potentially leading to near dryness and eventual drought.Measuring and monitoring soil moisture can ensure the fitness of your crops and help predict or prepare for flash floods and drought. The GLDAS soil moisture data is useful for modeling these scenarios and others, but only at global scales. Dataset SummaryThe GLDAS Soil Moisture layer is a time-enabled image service that shows average monthly soil moisture from 2000 to the present, measured as the millimeters of water contained within four different depth levels. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: Soil MoistureUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. The GLDAS soil moisture data is useful for modeling, but only at global scales. By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean. Mean soil moisture for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. If you do this, make sure to also select a specific variable. The minimum time extent is one month, and the maximum is 8 years. Variables: This layer has five variables, corresponding to different depth levels. By default total is shown, but you can view an individual depth level using the multidimensional filter, or by applying the relevant raster function. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.

يتضمّن الإصدار 2 من نظام دمج بيانات الأراضي العالمي (GLDAS-2) التابع لوكالة ناسا ثلاثة مكوّنات: GLDAS-2.0 وGLDAS-2.1 وGLDAS-2.2. يتمّ فرض GLDAS-2.0 بالكامل باستخدام بيانات الإدخال للعوامل المناخية في جامعة "برينستون"، كما يوفّر سلسلة متّسقة زمنيًا من 1948 إلى 2014. يتم فرض GLDAS-2.1 باستخدام تركيبة من بيانات النماذج والرصد من عام 2000 إلى الوقت الحالي. تستخدِم مجموعات منتجات GLDAS-2.2 دمج البيانات (DA)، في حين أنّ منتجات GLDAS-2.0 وGLDAS-2.1 "مفتوحة الحلقة" (أي لا يتم دمج البيانات). يختلف اختيار بيانات الإدخال، بالإضافة إلى مصدر ومتغيّر ومخطّط رصد البيانات الوصفية، وذلك حسب منتجات GLDAS-2.2 المختلفة.GLDAS-2.1 هو أحد مكوّنَي مجموعة بيانات الإصدار 2 من GLDAS (GLDAS-2)، والثاني هو GLDAS-2.0. يشبه GLDAS-2.1 بثّ منتجات GLDAS-1، مع النماذج المحسّنة التي تم فرضها من خلال مجموعة من GDAS ومجموعة GPCP المجزّأة ومجموعات بيانات الإشعاع AGRMET. بدأت محاكاة GLDAS-2.1 في 1 كانون الثاني (يناير) 2000 باستخدام الظروف من محاكاة GLDAS-2.0. تم فرض هذه المحاكاة باستخدام حقول التحليل الجوي الخاصة بالإدارة الوطنية للمحيطات والغلاف الجوي (NOAA)/نظام دمج البيانات العالمي (GDAS) (Derber et al., 1991)، وحقول هطول الأمطار في مشروع Global Precipitation Climatology Project (GPCP) المُفصَّلة (Adler et al., 2003)، وحقول الإشعاع في نظام النمذجة المناخية الزراعية (AGRMET) التابع لوكالة الأرصاد الجوية التابعة لسلاح الجو، والتي أصبحت متاحة اعتبارًا من 1 آذار (مارس) 2001 فصاعدًا. المستندات: Readme طريقة التنفيذ مستندات الهيدرولوجيا في GES DISC مستندات أعمدة البيانات في GES DISC ملاحظة مقدّم الخدمة: الأسماء التي تحتوي على اللاحقة _tavg هي متغيّرات يتم احتساب متوسطها على مدار آخر 3 ساعات، والأسماء التي تحتوي على اللاحقة '_acc' هي متغيّرات يتم تجميعها على مدار آخر 3 ساعات، والأسماء التي تحتوي على اللاحقة '_inst' هي متغيّرات فورية، والأسماء التي تحتوي على '_f' هي متغيّرات قسرية.

Gridded Model Output from Global Land Data Assimilation System (GLDAS) 0.25 degree simulation using Noah 2.7.1 land surface model, experiment 891. This simulation was forced by a combination of NOAA/GDAS atmospheric analysis fields, spatially and temporally disaggregated NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP), and observation based downward shortwaveand longwave radiation fields derived using the method of the Air Force Weather Agency's AGRicultural METeorological modeling system.

Most of us understand the hydrologic cycle in terms of the visible paths that water can take such as rainstorms, rivers, waterfalls and lakes. However, an even larger volume of water flows through the air all around us in two invisible paths: evaporation and transpiration. These two paths together are referred to as evapotranpsiration (ET), and claim 61% of all terrestrial precipitation. Solar radiation, air temperature, wind speed, soil moisture, and land cover all affect the rate of evapotranspiration, which is a major driver of the global water cycle, and key component of most catchments' water budget. This map contains a historical record showing the volume of water lost to evapotranspiration globally during each month from March 2000 to the present.Dataset SummaryThe GLDAS Evapotranspiration layer is a time-enabled image service that shows total actual evapotranspiration monthly from 2000 to the present, measured in millimeters of water loss. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: EvapotranspirationUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales. By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean. Mean evapotranspiration for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.

Since 2002, NASA’s GRACE Satellite mission has allowed scientists of various disciplines to analyze and map the changes in Earth’s total water storage on a global scale. Although the raw data is available to the public, the process of viewing, manipulating, and analyzing the GRACE data can be tedious and difficult for those without strong technological backgrounds in programming or other related fields. Furthermore, simply knowing the changes in total water storage in a particular region typically isn’t enough to plan remediation efforts as there is no indication of whether the changes in storage are occurring in the groundwater, surface water, or soil moisture (groundwater being particularly difficult to estimate). The GRACE web-based application helps bridge the technical gap for decision makers by providing a user interface to visualize, not only the data collected from the GRACE mission, but the individual water storage components as well. Using the GLDAS Noah Land Surface Model, the application allows the user to isolate and identify the changes in surface water, soil moisture, and groundwater storage that makeup the total water storage quantities measured in the raw GRACE data. Analysis of these changes can also be performed on a regional or continental scale allowing users to aggregate and analyze the change in groundwater, soil moisture, surface water, and total water storage within their own personal regions of interest. The GRACE application also allows the user to view and compare different signal processing solutions for the total water storage data. In this way, the GRACE application offers scientists, engineers and decision makers a common starting point in their environmental modeling efforts and exposes the potential applications for a large-scale groundwater model. The GRACE application can be accessed here: