Evaluation and intercomparison of the effects of snow and soil moisture data assimilation in three LSMs from the Air Force's operational Global Hydro-Intelligence System

This study presents an evaluation and intercomparison of the effects of joint snow and soil moisture data assimilation in three separate LSMs included in the Air Force’s operational Global Hydro-Intelligence System. The NASA Hydrological Sciences Laboratory integrated these three separate LSMs (Noah-3.9, Noah-MP-4.0.1, and JULES-5.0) into the Land Information System (LIS) software framework, including multi-variate/multi-sensor data assimilation of snow and soil moisture products. A custom release of the LIS system with these research capabilities was transitioned to the Air Force for operations as part of their Global Hydro-Intelligence (GHI) near real-time (NRT) land data analysis system. Global offline LSM simulations at ~10-km grid spacing of all three LSMs from Nov 2007 to present were run using a NASA-developed precipitation and surface meteorological analysis. Products assimilated during the simulations include an Air Force-based snow analysis, a new NASA-developed snow analysis, and soil moisture retrievals from SMOPS and from SMAP. The data assimilation (DA) simulations and the corresponding Open Loop (no DA) simulations for all three LSMs were evaluated against numerous global, regional, and in situ reference products, including soil moisture, snow, evapotranspiration, and streamflow. The effects of data assimilation on the results, as well as a model intercomparison, will be presented.