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A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data AssimilationData assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.
Document ID
20110023466
Acquisition Source
Goddard Space Flight Center
Document Type
Preprint (Draft being sent to journal)
Authors
Kumar, Sujay V. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Reichle, Rolf H. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Harrison, Kenneth W. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Peters-Lidard, Christa D. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Yatheendradas, Soni (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Santanello, Joseph A. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Date Acquired
August 25, 2013
Publication Date
September 30, 2011
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC.JA.5386.2011
Funding Number(s)
CONTRACT_GRANT: AIST-08-077
Distribution Limits
Public
Copyright
Public Use Permitted.

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