An improved approach for estimating observation and model error parameters in soil moisture data assimilation
Article first published online: 7 DEC 2010
Copyright 2010 by the American Geophysical Union.
Water Resources Research
Volume 46, Issue 12, December 2010
How to Cite
2010), An improved approach for estimating observation and model error parameters in soil moisture data assimilation, Water Resour. Res., 46, W12519, doi:10.1029/2010WR009402., and (
- Issue published online: 7 DEC 2010
- Article first published online: 7 DEC 2010
- Manuscript Accepted: 2 JUL 2010
- Manuscript Revised: 23 JUN 2010
- Manuscript Received: 6 APR 2010
- data assimilation;
- remote sensing;
- soil moisture
 The accurate specification of observing and/or modeling error statistics presents a remaining challenge to the successful implementation of many land data assimilation systems. Recent work has developed adaptive filtering approaches that address this issue. However, such approaches possess a number of known weaknesses, including a required assumption of serially uncorrelated error in assimilated observations. Recent validation results for remotely sensed surface soil moisture retrievals call this assumption into question. Here we propose and test an alternative system for tuning a soil moisture data assimilation system, which is robust to the presence of autocorrelated observing error. The approach is based on the application of a triple collocation approach to estimate the error variance of remotely sensed surface soil moisture retrievals. Using this estimate, the variance of assumed modeling perturbations is tuned until normalized filtering innovations have a temporal variance of one. Real data results over three highly instrumented watershed sites in the United States demonstrate that this approach is superior to a classical tuning strategy based on removing the serial autocorrelation in Kalman filtering innovations and nearly as accurate as a calibrated Colored Kalman filter in which autocorrelated observing errors are treated optimally.