Correcting Land Surface Model Predictions for the Impact of Temporally Sparse Rainfall Rate Measurements Using an Ensemble Kalman Filter and Surface Brightness Temperature Observations

Current attempts to measure short-term (<1 month) rainfall accumulations using spaceborne radiometers are characterized by large sampling errors associated with low observation frequencies for any single point on the globe (from two to eight measurements per day). This degrades the value of spaceborne rainfall retrievals for the monitoring of surface water and energy balance processes. Here a data assimilation system, based on the assimilation of surface L-band brightness temperature (TB) observations via the ensemble Kalman filter (EnKF), is introduced to correct for the impact of poorly sampled rainfall on land surface model predictions of root-zone soil moisture and surface energy fluxes. The system is evaluated during the period from 1 April 1997 to 31 March 1998 over two sites within the U.S. Southern Great Plains. This evaluation includes both a data assimilation experiment, based on synthetically generated TB measurements, and the assimilation of real TB observations acquired during the 1997 Southern Great Plains Hydrology Experiment (SGP97). Results suggest that the EnKF-based assimilation system is capable of correcting a substantial fraction (>50%) of model error in root-zone (40 cm) soil moisture and latent heat flux predictions associated with the use of temporally sparse rainfall measurements as forcing data. Comparable gains in accuracy are demonstrated when actual TB measurements made during the SGP97 experiment are assimilated.