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Data Assimilation to Extract Soil Moisture Information From SMAP ObservationsStatistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, they can be used to reduce the need for localized bias correction techniques typically implemented in data assimilation (DA) systems that tend to remove some of the independent information provided by satellite observations. Here, we use a statistical neural network (NN) algorithm to retrieve SMAP (Soil Moisture Active Passive) surface soil moisture estimates in the climatology of the NASA Catchment land surface model. Assimilating these estimates without additional bias correction is found to significantly reduce the model error and increase the temporal correlation against SMAP CalVal in situ observations over the contiguous United States. A comparison with assimilation experiments using traditional bias correction techniques shows that the NN approach better retains the independent information provided by the SMAP observations and thus leads to larger model skill improvements during the assimilation. A comparison with the SMAP Level 4 product shows that the NN approach is able to provide comparable skill improvements and thus represents a viable assimilation approach.
Document ID
20170010213
Acquisition Source
Goddard Space Flight Center
Document Type
Presentation
Authors
Kolassa, J. (Universities Space Research Association Greenbelt, MD, United States)
Reichle, R. H. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Liu, Q. (Science Systems and Applications, Inc. Greenbelt, MD, United States)
Alemohammad, S. H. (Columbia Univ. New York, NY, United States)
Gentine, P. (Columbia Univ. New York, NY, United States)
Date Acquired
October 19, 2017
Publication Date
September 19, 2017
Subject Category
Meteorology And Climatology
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN47191
Meeting Information
Meeting: Satellite Soil Moisture Validation and Application Workshop
Location: Vienna
Country: Austria
Start Date: September 19, 2017
End Date: September 20, 2017
Sponsors: European Space Agency, Technische Univ.
Funding Number(s)
CONTRACT_GRANT: NNG17HP01C
CONTRACT_GRANT: NNG11HP16A
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Keywords
D
NN
SMAP

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