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Effective Assimilation of SMAP Observations Using Statistical TechniquesStatistical 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, the need for bias correction prior to an assimilation of these estimates is reduced, which could result in a more effective use of the independent information provided by the satellite observations. In this study, a statistical neural network (NN) retrieval algorithm is calibrated using SMAP brightness temperature observations and modeled soil moisture estimates (similar to those used to calibrate the SMAP Level 4 DA system). Daily values of surface soil moisture are estimated using the NN and then assimilated into the NASA Catchment model. The skill of the assimilation estimates is assessed based on a comprehensive comparison to in situ measurements from the SMAP core and sparse network sites as well as the International Soil Moisture Network. The NN retrieval assimilation is found to significantly improve the model skill, particularly in areas where the model does not represent processes related to agricultural practices. Additionally, the NN method is compared to assimilation experiments using traditional bias correction techniques. The NN retrieval assimilation is found to more effectively use the independent information provided by SMAP resulting in larger model skill improvements than assimilation experiments using traditional bias correction techniques.
Document ID
20180000668
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. Lanham, MD, United States)
Alemohammad, S. H. (Columbia Univ. New York, NY, United States)
Gentine, P. (Columbia Univ. New York, NY, United States)
Date Acquired
January 18, 2018
Publication Date
December 11, 2017
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN50552
Meeting Information
Meeting: Annual American Geophysical Union (AGU) Fall Meeting 2017
Location: New Orleans, LA
Country: United States
Start Date: December 11, 2017
End Date: December 15, 2017
Sponsors: American Geophysical Union
Funding Number(s)
CONTRACT_GRANT: NNG11HP16A
CONTRACT_GRANT: NNG17HP01C
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.

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