Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/9/2007
Publication Date: 4/15/2008
Citation: Reichle, R.H., Crow, W.T., Keppenne, C.L. 2008. An adaptive ensemble Kalman filter for soil moisture data assimilation. Water Resources Research. 44, W03423. http://dx.doi.org/10.1021/2007WR006357. Interpretive Summary: Satellites are often used to measure properties of the Earth’s atmosphere, oceans, or land surface from space. Data assimilation systems are then used to combine such satellite observations with computer models of Earth system processes and to estimate land surface conditions that cannot be observed from space but are needed for applications. For example, moisture in the top few centimeters of the soil can be observed from satellite, but not soil moisture in the top meter of the soil. The latter is needed for drought assessment and forecasting. Good data assimilation results rely on accurately understanding the errors in the satellite observations and in the Earth system model. These errors, however, are very difficult to characterize because accurate ground-based observations are typically lacking. In this paper we present an adaptive data assimilation system that estimates not only the land surface properties (such as soil moisture) but also the errors in the satellite observations and in the Earth system model. We demonstrate the adaptive data assimilation system for a test case with computer-generated “synthetic” satellite observations over the Red-Arkansas river basin. We find that the adaptive technique performs well and is able to provide improved estimates of land surface conditions when compared to the non-adaptive data assimilation system.
Technical Abstract: In a 19-year twin experiment for the Red-Arkansas river basin we assimilate synthetic surface soil moisture retrievals into the NASA Catchment land surface model. We demonstrate how poorly specified model and observation error parameters affect the quality of the assimilation products. In particular, soil moisture estimates from data assimilation are sensitive to observation and model error variances and, for very poor input error parameters, may even be worse than model estimates without data assimilation. Estimates of surface heat fluxes and runoff are at best marginally improved through the assimilation of surface soil moisture and tend to have large errors when the assimilation system operates with poor input error parameters. We present a computationally affordable, adaptive assimilation system that continually adjusts model and observation error parameters in response to internal diagnostics. The adaptive filter can identify model and observation error variances and provide generally improved assimilation estimates when compared to the non-adaptive system.