Wednesday, 25 January 2017
4E (Washington State Convention Center )
Accurate estimation of land surface states, namely, soil moisture, surface temperature, snowpack, vegetation and soil type, and surface water and energy fluxes, is critical for weather and climate prediction. Operational prediction systems are found to have non-trivial bias in land surface states and land surface forcing of precipitation, among other atmospheric forcing variables, leading to inaccurate surface water and energy partitioning and errors in the consequent land and atmospheric prediction. This has motivated the development of the Global Land Data Assimilation System (GLDAS). The concept of GLDAS is to utilize the best available in-situ and remotely-sensed observations of land surface properties within the framework of land modeling and data assimilation to produce enhanced fields of land surface states and fluxes. The current NCEP operational GLDAS was implemented in January 2011 as the land analysis component in the NCEP Climate Forecast System Reanalysis (CFSR) and the NCEP operational CFS/CDAS version 2. Yet, the land data assimilation module in the current NCEP operational GLDAS is a simple approach for snow cover and snow depth analysis. Scientists and software engineers at the NASA Goddard Space Flight Center has developed a Land Information System that provides a land surface modeling and data assimilation infrastructure on highly parallel computing platforms with portable capability to facilitate inter-operation with other Earth system modeling and assimilation frameworks. The objective of this project is to upgrade the NCEP GLDAS by porting the updated LIS infrastructure to the NCEP Central Computing System.
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