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The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system

(2011) JOURNAL OF HYDROMETEOROLOGY. 12(5). p.750-765
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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.
Keywords
PASSIVE MICROWAVE, SOUTHERN UNITED-STATES, SIMULATIONS, AMSR-E, ERS SCATTEROMETER, GAUGE OBSERVATIONS, TEMPERATURE OBSERVATIONS, SURFACE MODELS, GLOBAL PRECIPITATION, ENSEMBLE KALMAN FILTER

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Citation

Please use this url to cite or link to this publication:

Chicago
Liu, Qing, Rolf H Reichle, Rajat Bindlish, Michael H Cosh, Wade T Crow, Richard de Jeu, Gabriëlle De Lannoy, George J Huffman, and Thomas J Jackson. 2011. “The Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System.” Journal of Hydrometeorology 12 (5): 750–765.
APA
Liu, Qing, Reichle, R. H., Bindlish, R., Cosh, M. H., Crow, W. T., de Jeu, R., De Lannoy, G., et al. (2011). The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. JOURNAL OF HYDROMETEOROLOGY, 12(5), 750–765.
Vancouver
1.
Liu Q, Reichle RH, Bindlish R, Cosh MH, Crow WT, de Jeu R, et al. The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. JOURNAL OF HYDROMETEOROLOGY. 2011;12(5):750–65.
MLA
Liu, Qing, Rolf H Reichle, Rajat Bindlish, et al. “The Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System.” JOURNAL OF HYDROMETEOROLOGY 12.5 (2011): 750–765. Print.
@article{2075924,
  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 ({\textacutedbl}Cal Val{\textacutedbl}) 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.},
  author       = {Liu, Qing and Reichle, Rolf H and Bindlish, Rajat and Cosh, Michael H and Crow, Wade T and de Jeu, Richard and De Lannoy, Gabri{\"e}lle and Huffman, George J and Jackson, Thomas J},
  issn         = {1525-755X},
  journal      = {JOURNAL OF HYDROMETEOROLOGY},
  keyword      = {PASSIVE MICROWAVE,SOUTHERN UNITED-STATES,SIMULATIONS,AMSR-E,ERS SCATTEROMETER,GAUGE OBSERVATIONS,TEMPERATURE OBSERVATIONS,SURFACE MODELS,GLOBAL PRECIPITATION,ENSEMBLE KALMAN FILTER},
  language     = {eng},
  number       = {5},
  pages        = {750--765},
  title        = {The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system},
  url          = {http://dx.doi.org/10.1175/JHM-D-10-05000.1},
  volume       = {12},
  year         = {2011},
}

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