Abstract
This study aims to retrieve cloud top height (CTH)—excluding cirrus—using passive microwave radiometer observations combined with humidity and temperature profiles. A machine-learning-based approach, combining neural network and gradient boosting methods, is used with Cloud Profiling Radar observations as input. The subsequently derived microwave CTH predictions show a mean average error of 2.1 km and a correlation index of 0.8. The algorithm is used to retrieve the CTH during Hurricane Maria and during a mid-latitude autumn storm. This new algorithm will allow to provide estimates of CTH, at world scale, for a 20-year period.
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