Automating the estimation of various meteorological parameters using satellite data and machine learning techniques

Satellite data from various sensors and platforms are being used to develop automated algorithms to assist in U.S. Navy operational weather assessment and forecasting. Supervised machine learning techniques are used to discover patterns in the data and develop associated classification and parameter estimation algorithms. These methods are applied to cloud classification in GOES imagery, tropical cyclone intensity estimation using SSM/I data, and cloud ceiling height estimation at remote locations using appropriate geostationary and polar orbiting satellite data in conjunction with numerical weather prediction output and climatology. All developed algorithms rely on training data sets that consist of records of attributes (computed from the appropriate data source) and the associated ground truth.