An Update on the Hydrological Land Surface Data and Services at NASA GES DISC

"The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelve NASA Earth Observing System data centers that document, process, archive and distribute data from Earth science missions and related projects. The GES DISC hosts many hydrological land surface data products including North American Land Data Assimilation System (NLDAS), Global Land Data Assimilation System (GLDAS), the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), and assimilated-Gravity Recovery and Climate Experiment (GRACE). The Land Surface Model (LSM) products contain model output, including heat fluxes, rain, snow, soil temperature, soil moisture and runoff; and observational forcing data, including surface pressure, temperature, precipitation, downward shortwave and longwave radiation, humidity, and wind. The temporal resolution of the hydrology data at the GES DISC ranges from hourly to monthly and the spatial resolution 0.1 degree to 1.0 degree. The variability of Earth’s water and energy cycles can be characterized with these spatially and temporally consistent, and quality controlled hydrological land surface data. These data could support ongoing modeling activities, and help improve our understanding of land-surface-atmosphere interactions and their impact on climate.
Over the years, new data is added, and older data is reprocessed and updated. This presentation provides a summary table of the hydrological data and discusses recent updates to the data and data services at the GES DISC. New data products include (1) GRACE Global Version 3.0 drought indicators and (2) FLDAS monthly global and Central Asia daily data. Reprocessed and updated products include (1) NLDAS Version 2.0 and (2) GRACE United States Version 4.0 drought indicators. Lastly, this presentation will discuss the opportunities for accessing some of our hydrology data from the cloud. As the hydrology land surface data is moved to the cloud, users can expect faster service for longer time series, notably the hydrology data rods time series service."