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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,… Click to show full 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.
Journal Title: IEEE Geoscience and Remote Sensing Letters
Year Published: 2022
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