Soil Moisture Intialization for Climate Predictions: Assimilating SMMR into a Land Surface Model
Wenge Ni-Meister, Jeffrey P. Walker, Rolf H. Reichle, Paul R. Houser and Randal D. Koster
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 quantitave information on soil moisture in a very thin near-surface soil layer at large scale. This information can be assimilated into a land surface model to retrieve better estimates of the soil moisture states. A Kalman filter-based data assimilation strategy has been implemented in the catchement-based land surface model(CLSM) used by the NASA Seasonal-to-Interannual Prediction Project (NSIPP). We assimilated Scanning Multifrequency Microwave Radiometer (SMMR) data for the perid of 1979-1987 and compared the resulting soil moisture content estimates with in-situ measurements collected in Russia, Mongolia and China. Our comparison results showed that the data assimilation method used here significantly improved our soil moisture estimation, demonstrating that Kalman filter-based assimilation is a feasible approach which can be used to combine remote sensing observations and land surface models for improved soil moisture initialization. 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.