A Neural Network Multiparameter Algorithm for SSM/I Ocean Retrievals: Comparisons and Validations

A new empirical multiparameter Special Sensor Microwave/Imager (SSM/I) retrieval algorithm based on the neural network approach, which retrieves wind speed, columnar water vapor, and columnar liquid water simultaneously using only SSM/I brightness temperatures, is compared with existing global SSM/I retrieval algorithms. In terms of wind speed retrieval accuracy, the new algorithm systematically outperforms all algorithms considered under all weather conditions where retrievals are possible with an algorithm rms error of 1.0 m/s under clear and 1.3 m/s under clear plus cloudy conditions. It also generates high wind speeds with acceptable accuracy. This improvement in accuracy is coupled with increased areal coverage with obvious benefits for operational applications. With respect to columnar water vapor and columnar liquid water, the new algorithm reproduces the results of existing algorithms closely. The new algorithm has been tested and accepted for operational use at the National Centers for Environmental Prediction (NCEP) producing a positive impact on forecast winds through assimilation into NCEPʹs numerical weather prediction models.