New Satellite Passive Microwave Retrieval Algorithms and Their Application to Study Atmospheric Phenomena and Oceanic Parameters

The new high accuracy algorithms for atmospheric and oceanic parameter retrievals from satellite passive microwave measurement data, based on Neural Network (NN) technique, employed for the radiative transfer equation inversion, are considered in this study. Extensive calibration of Special Sensor Microwave Imager/Sounder (SSMIS) measurement data onboard Defense Meteorological Satellite Program (DMSP) satellites (F16, F17 and F18) is fulfilled against the geophysical model used for brightness temperature calculations. The application of these algorithms, along with previously developed, applicable to Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Sounding Radiometer - Earth Observing System (AMSR-E) instruments, to satellite data allows conducting various case studies, including synoptic-scale and mesoscale weather system (e.g. polar lows, extratropical and tropical cyclones) investigation. Several case studies of weather systems in tropical and polar regions are comprehensively considered, involving all available satellite and in-situ data, with an accent on satellite passive microwave data usage.