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Improving numerical weather forecast using multi-frequency passive microwave satellite observations and data assimilation methods

A multi-frequency passive microwave data assimilation system (CALDAS) was developed to physically introduce the satellite observed land and atmospheric moisture information into a mesoscale model to enhance the capability of numerical prediction. CALDAS merged information from AMSRE's lower-frequency observations with that from higher frequencies, and therefore facilitated passive microwave remote sensing to obtain atmospheric information over land surfaces. They system was applied over a mesoscale domain of Niger, Africa. The results showed that CALDAS improved the cloud representation and land-atmosphere feed back mechanism, significantly. Detailed validations will be carried out in the near future to asses the full potential of the system.