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- aggregation classification "A1".
- aggregation creator B293845.
- aggregation creator B293846.
- aggregation creator B293847.
- aggregation creator B293848.
- aggregation creator B293849.
- aggregation creator B293850.
- aggregation creator B293851.
- aggregation creator B293852.
- aggregation creator person.
- aggregation date "2011".
- aggregation format "application/pdf".
- aggregation hasFormat 2075924.bibtex.
- aggregation hasFormat 2075924.csv.
- aggregation hasFormat 2075924.dc.
- aggregation hasFormat 2075924.didl.
- aggregation hasFormat 2075924.doc.
- aggregation hasFormat 2075924.json.
- aggregation hasFormat 2075924.mets.
- aggregation hasFormat 2075924.mods.
- aggregation hasFormat 2075924.rdf.
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- aggregation hasFormat 2075924.txt.
- aggregation hasFormat 2075924.xls.
- aggregation hasFormat 2075924.yaml.
- aggregation isPartOf urn:issn:1525-755X.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Earth and Environmental Sciences".
- aggregation title "The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system".
- aggregation abstract "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.".
- aggregation authorList BK579870.
- aggregation endPage "765".
- aggregation issue "5".
- aggregation startPage "750".
- aggregation volume "12".
- aggregation aggregates 2075937.
- aggregation isDescribedBy 2075924.
- aggregation similarTo JHM-D-10-05000.1.
- aggregation similarTo LU-2075924.