Abstract:
Current climate models for seasonal prediction or water resource management are limited due to poor initialization of land surface soil moisture states. Passive microwave remote sensing provides quantitative information of water content at a very thin near-surface soil layer at large scale. This information can be assimilated into a land surface model to retrieve better soil moisture states. A Kalman filter-based data assimilation strategy is currently installed in a catchement-based land surface model(CLSM) in the NASA Seasonal-to-Interannual Prediction Project(NSIPP). We use this algorithm to assimilate Scanning Multifrequency Microwave Radiometer (SMMR) data for the perid of 1979-1987 and compare the asimilated soil moisture with in-situ measurements collected in Russia, Mongolia and China. Our comparison results show the data assimilation method used here significantly improves soil moisture estimation. Our study demonstrates 1).The Kalman filter-based assimilation is a feasible approach which can be used to link remote sensing and land surface models for improved climate prediction; 2). SMMR data can provide better intialization of soil moisture states for the period of 1979-1987 for retrospective study in NSIPP; 3) Our algorithm can also be used to assimilate data collected from the Advanced Microwave Scanning Radiometer for the Earth (AMSR-E) observing system instrument on the current EOS Aqua satellite to provide better soil moisture states for real time forecasting.