Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: March 15, 2011
Publication Date: November 1, 2013
Citation: Crow, W.T. 2013. Estimating model and observation error covariance information for land data assimilation systems. In: S. Liang, X. Li and X. Xie, editors. Land Surface Observation, Modeling and Data Assimilation. Washington, DC: World Scientific Publishing Company. p. 171-205. Interpretive Summary: Accurate information regarding the availability of soil water is valuable for a wide range of agricultural applications (including irrigation scheduling, fertilizer application optimization, crop yield forecasting and water quality modeling). The current state-of-the-art for estimating large-scale surface soil moisture variations over agricultural landscapes are data assimilation systems which optimally merge soil moisture predictions from water balance models with remotely-sensed surface soil moisture retrievals obtained from satellite observations. This paper addresses a key technical challenge for such systems. Namely, how do you appropriately weight information in the satellite observations relative to (potentially conflicting) information provided by the land surface model? Currently, we have no reliable methodology for obtaining such weighting over most agricultural areas. This paper describes presents the first feasible technique for obtaining relative vital weighting information over poorly-instrumented portions of the globe. When applied within a land data assimilation system, our new technique is demonstrated to provide more accurate surface soil moisture predictions within three separate USDA ARS experimental watersheds.
Technical Abstract: In order to operate efficiently, data assimilation systems require accurate assumptions concerning the statistical magnitude and cross-correlation structure of error in model forecasts and assimilated observations. Such information is seldom available for the operational implementation of land data assimilation systems designed to ingest remotely-sensed observations. As a consequence, it is important to understand the impact of poor error assumptions on the performance of land data assimilation systems and strive to develop data assimilation tools to obtain the required statistical information. After presenting a simplified theoretical background for the problem, this chapter will describe recent research results which document the impact of incorrect model and observation error assumptions on the assimilation of surface soil moisture fields into a land surface models and review the recent development of online adaptive filtering systems in which both modeling and observation error covariance information is estimated during a land data assimilation analysis.