Terrestrial Water Storage Forecasts
Authors: | |
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Owners: | Abheera Hazra |
Type: | Resource |
Storage: | The size of this resource is 4.1 GB |
Created: | Nov 01, 2021 at 5 p.m. |
Last updated: | Nov 04, 2021 at 4:15 p.m. (Metadata update) |
Published date: | Nov 04, 2021 at 4:15 p.m. |
DOI: | 10.4211/hs.508fc30c230149d8b97977bea9c0f49b |
Citation: | See how to cite this resource |
Content types: | Multidimensional Content Multidimensional Content Multidimensional Content Multidimensional Content Multidimensional Content Multidimensional Content Multidimensional Content Multidimensional Content Multidimensional Content Multidimensional Content Multidimensional Content Multidimensional Content |
Sharing Status: | Published |
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Views: | 1260 |
Downloads: | 28 |
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Abstract
The terrestrial water storage (TWS) forecasts are produced using the Catchment Land Surface Model (CLSM) forced with NASA’s Goddard Earth Observing System (GEOS), and is a part of NASA’s Hydrological Forecast and Analysis System (NHyFAS). The initial conditions in this system do not use data assimilation. The TWS forecast data is over continental Africa and the Middle East at 25km or 0.25deg spatial resolution and monthly temporal resolution. It covers the period between 1982 to 2019, the forecasts are initialized on the first of every month from 1982 through 2018 and the forecast leads extend out to 6 months. The TWS forecasts are included as monthly files with the following coordinates: 37years(1982-2018) x 6leads x 4ensembles x 320latitudes x 320longitudes.
Subject Keywords
Coverage
Spatial
Temporal
Start Date: | 01/01/1982 |
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End Date: | 05/31/2019 |












Content
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Related Resources
This resource is referenced by | Cook, B. I., Slinski, K., Peters-Lidard, C., McNally, A., Arsenault, K., & Hazra, A. (2021). The efficacy of seasonal terrestrial water storage forecasts for predicting vegetation activity over Africa, Journal of Hydrometeorology. https://doi.org/10.1175/JHM-D-21-0046.1 |
The content of this resource is derived from | NASA’s Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2); Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1 . |
The content of this resource is derived from | Climate Hazards Group InfraRed Precipitation with Station data, version 2.0 (CHIRPS); Funk, C., and Coauthors, 2015: The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66 |
The content of this resource is derived from | NASA’s Goddard Earth Observing System version 2 (GEOS); Borovikov, A., R. Cullather, R. Kovach, J. Marshak, G. Vernieres, Y. Vikhliaev, B. Zhao, and Z. Li, 2019: GEOS-5 seasonal forecast system. Climate Dyn., 53, 7335–7361, https://doi.org/10.1007/S00382-017-3835-2 |
The content of this resource is derived from | NASA's Hydrological Forecast and Analysis System (NHyFAS); Arsenault, K.R., and Coauthors, 2020: The NASA hydrological forecast system for food and water security applications. Bull. Amer. Meteor. Soc., 101, E1007–E1025, https://doi.org/10.1175/BAMS-D-18-0264.1. |
Credits
Funding Agencies
This resource was created using funding from the following sources:
Agency Name | Award Title | Award Number |
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U.S. Agency for International Development, Bureau of Humanitarian Assistance | NA | PAPA AID-FFP-T-17-00001 |
NASA Earth Science Applications: Food Security and Agriculture | NASA Food Security and Agriculture Consortium (FSAC) | 80NSSC18M0039 |
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http://creativecommons.org/licenses/by/4.0/
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