Wednesday, 25 January 2017
4E (Washington State Convention Center )
The land analysis in Climate Forecast System Reanalysis (CFSR) was conducted with the Global Land Data Assimilation System (GLDAS) running under NASA Land Information System (LIS) using the Noah LSM (Land Surface Model) to evolve land states and to compute surface fluxes. The land states are updated using a “semi-coupled” approach, where these states are generated from a parallel GLDAS driven by observed precipitation (a blend of satellite retrievals, gauge-based, and model-based at higher latitudes) and with near-surface forcing from the parent atmospheric data analysis system. However, assimilation of remotely-sensed estimates of land-surface states such as soil moisture and snowpack are not supported in the old version of LIS currently used in GLDAS. Therefore, we need to bring in the new version of LIS into NCEP operational systems. The current NASA LIS (version 7) integrates NOAA operational land surface and hydrological models (NCEP’s Noah, versions from 2.7.1 to 3.3 and the future Noah-MP), high-resolution satellite and observational data, and land DA tools. The existing land DA capabilities in LIS have been transitioned to support NCEP’s land surface assimilation of satellite-based soil moisture and snow observations. A set of offline numerical experiments driven by the GFS forecast forcing have been conducted to evaluate the impact of assimilating snow with daily Global Historical Climatology Network (GHCN). The statistics from LIS EnKF DA results with 20 members are better than all the other methods including AFWA SNODEP, operational GFS/GDAS product, LIS control run, and LIS DA with direct replacement.
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