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.