Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 11/5/2006
Publication Date: 12/11/2006
Citation: Reichle, R.J., Koster, R.D., Crow, W.T., Sharif, H., Mahanama, S. 2006. Assessing the impact of errors in soil moisture retrievals on their utility in a data assimilation system [abstract]. EOS Transactions, American Geophysical Union, Fall Supplements. 87(52):H23E1553. Interpretive Summary:
Technical Abstract: Soil moisture retrievals from satellites often contain large time-variant and time-invariant errors because the physical processes that relate brightness temperature to soil moisture are difficult to parameterize, and because the necessary parameters are difficult to obtain on the global scale. For the design of new satellite sensors it is important to understand just how uncertain satellite retrievals can be and still add useful information to a land data assimilation system. In this paper, we address this question with a fraternal twin experiment that is based on high-resolution (1 km) "true" soil moisture fields and associated passive microwave brightness temperatures from a long-term integration of the TOPLATS land surface model over the Red-Arkansas river basin. From the true fields, we simulate many different retrieval data sets at a typical satellite footprint scale (36 km). The different retrieval data sets reflect various realistic sources of uncertainty with different error structure and magnitude. After scaling the satellite data to the model soil moisture climatology for bias removal, the simulated retrieval data sets are then assimilated into the NASA Catchment land surface model with an Ensemble Kalman filter (EnKF). Finally, the quality of the assimilation estimates (with respect to the synthetic truth) is compared with that of a baseline integration of the Catchment model without assimilation. This procedure permits us to quantify explicitly the maximum level of uncertainty in the satellite retrievals for which information is still added in the assimilation. Performance measures include the traditional absolute (RMS) error, which is important for water cycle studies, and the time series correlation coefficient. The latter measures the quality of (scaled) anomaly estimates that can be used, for example, for forecast initialization.