Vertical-UNet: A Deep Learning Framework for Vertical Structure Classification of Precipitation Clouds Using Multi-Source Satellite Data

The classification of precipitation clouds, particularly the identification of severe convective clouds, is of paramount importance for meteorological forecasting and disaster warning systems. Current precipitation cloud observations typically rely on either standalone satellites or ground-based meteorological stations conducting horizontal-layer detection, yet these methods suffer from limitations such as restricted detection ranges and single-source observation. Therefore, this paper employs multi-source satellite data to construct a vertically structured cloud-precipitation dataset. This dataset comprises four categories: clear skies, non-precipitating clouds, general precipitation clouds, and severe convective clouds. A self-developed DFConv attention mechanism is integrated into the UNet network framework to build the Vertical-UNet model for identifying precipitation cloud types within vertical structures. Experimental results demonstrate that Vertical-UNet achieves favorable performance in precipitation cloud classification using the vertical-structure precipitation cloud dataset. The probability of detection (POD) for precipitation clouds reaches 94.54%. The POD for severe convective clouds reached 87.29%, indicating an improvement of 19.9% compared to the CNN model and 17.04% compared to the UNet model. This conclusively validates the model’s efficacy and establishes a foundation for detecting vertically structured precipitation clouds from diverse satellites in future research.