GLOBAL DATA ASSIMILATION AND FORECAST EXPERIMENTS USING SSM I WIND-SPEED DATA DERIVED FROM A NEURAL-NETWORK ALGORITHM/

A neural network algorithm used in this study to derive Special Sensor Microwave/Imager (SSM/I) wind speeds from the Defense Meteorological Satellite Program satellite-observed brightness temperatures is briefl y reviewed. The SSM/I winds derived from the neural network algorithm are not only of better quality, but also cover a larger area when comp ared to those generated from the currently operational Goodberlet algo rithm. The areas of increased coverage occur mainly over the regions o f active weather developments where the operational Goodberlet algorit hm fails to produce good quality wind data due to high moisture conten ts of the atmosphere. These two main characteristics associated with t he SSM/I winds derived from the neural network algorithm are discussed . SSM/I wind speed data derived from both the neural network algorithm and the operational Goodberlet algorithm are tested in parallel globa l data assimilation and forecast experiments for a period of about thr ee weeks. The results show that the use of neural-network-derived SSM/ I wind speed data leads to a greater improvement in the first-guess wi nd fields than use of wind data generated by the operational algorithm . Similarly, comparison of the forecast results shows that use of the neural-network-derived SSM/I wind speed data in the data assimilation and forecast experiment gives better forecasts when compared to those from the operational run that uses the SSM/I winds from the Goodberlet algorithm. These results of comparison between the two parallel analy ses and forecasts from the global data assimilation experiments are di scussed.