The Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

摘要

The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from theModern Era Retrospective-analysis for Research and Applications(MERRA), the study investigates soilmoisture skill derived from(i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficientR) is assessed using in situ observations in the continental United States at 37 single-profile sites within the SoilClimate AnalysisNetwork (SCAN) forwhich skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (‘‘CalVal’’) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill of AMSR-E retrievals is R 5 0.42 versus SCAN and R 5 0.55 versus CalVal measurements. The skill ofMERRA surface and root-zone soilmoisture is R5 0.43 and R5 0.47, respectively, versus SCAN measurements. MERRA surface moisture skill is R 5 0.56 versus CalVal measurements. Adding information from precipitation observations increases (surface and root zone) soil moisture skills by DR ; 0.06. AssimilatingAMSR-E retrievals increases soil moisture skills by △R～0.08.Adding information from both sources increases soil moisture skills by △R～0.13, which demonstrates that precipitation corrections and assimilation of satellite soil moisture retrievals contribute important and largely independent amounts of information.