期刊
JOURNAL OF HYDROMETEOROLOGY
卷 12, 期 5, 页码 750-765出版社
AMER METEOROLOGICAL SOC
DOI: 10.1175/JHM-D-10-05000.1
关键词
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资金
- NASA
- Terra
- Aqua
- ACRIMSAT
- Research Foundation Flanders (FWO), Belgium
- USDA/ARS
- NASA GSFC
- NOAA/CPC
- Deutscher Wetterdienst
- NSIDC
- VU Amsterdam
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 the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture 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 coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (Cal Val) 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 = 0.42 versus SCAN and R = 0.55 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R = 0.43 and R = 0.47, respectively, versus SCAN measurements. MERRA surface moisture skill is R = 0.56 versus CalVal measurements. Adding information from precipitation observations increases (surface and root zone) soil moisture skills by Delta R similar to 0.06. Assimilating AMSR-E retrievals increases soil moisture skills by Delta R similar to 0.08. Adding information from both sources increases soil moisture skills by Delta R similar to 0.13, which demonstrates that precipitation corrections and assimilation of satellite soil moisture retrievals contribute important and largely independent amounts of information.
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