Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 10, 2005
Publication Date: June 1, 2006
Citation: Crow, W.T. 2006. Impact of incorrect hydrologic model error assumptions on the sequential assimilation of remotely sensed surface soil moisture. Journal of Hydrometeorology. 8(3):421-431. Interpretive Summary: Data assimilation algorithms are mathematical tools for optimally updating uncertain model predictions with imperfect observations. They are increasingly being applied to the case of assimilating remotely-sensed surface soil moisture observations into land surface models. However, these approaches typically require detailed statistical information concerning the source and statistical structure of modeling errors. Such information is not commonly available. This paper explores the impact of making poor approximations about model error on prospects for successfully assimilating remotely-sensed surface soil moisture into a land surface model. Recommendations are made on ways to successfully constrain unknown model errors.
Technical Abstract: Sequential data assimilation approaches require some type of dynamic model error/covariance information in order to optimally merge model predictions with observations. The Ensemble Kalman filter (EnKF) derives such information through a Monte Carlo approach and the introduction of random noise in model states, fluxes, and/or forcing data. However, in land data assimilation, relatively little guidance exists concerning strategies for selecting the appropriate magnitude and/or location of introduced model noise. In addition, little is known about the sensitivity of filter prediction accuracy to (potentially) inappropriate assumptions concerning the source and magnitude of model error. Using a series of synthetic identical twin experiments, this analysis explores the consequences of making incorrect assumptions concerning the source and magnitude of model error on the efficiency of assimilating surface soil moisture observations to constrain deeper root-zone soil moisture predictions made by a land surface model. Results suggest that inappropriate model error assumptions can lead to circumstances in which the assimilation of surface soil moisture observations actually degrade the performance of a land surface model (relative to open loop assimilations that lack a data assimilation component). Prospects for diagnosing such circumstances, and adaptively correcting the culpable model errors assumptions, using filter innovations are discussed. The dual assimilation of both runoff (from streamflow) and surface soil moisture appears to offer a more robust assimilation framework within which the impacts of incorrect model error assumptions are more readily diagnosed via filter innovations.