Evaluation of a deep learning model to classify convective and stratiform precipitation patterns

The Mediterranean Sea is often affected by tropical-like cyclones, which cause heavy rainfall, strong winds, storm surges and flooding. The accurate classification of precipitation into convective and stratiform within these systems is essential for understanding storm dynamics and improving predictive models. In this study, we developed a deep learning approach based on U-Net architecture to classify convective and stratiform precipitations during Mediterranean cyclones using the Global Precipitation Measurement (GPM) IMERG product. We derived physically consistent labels for training through an exponential distribution-based thresholding of rainfall intensities. The trained U-Net model effectively reproduced the spatial structure of convective rainbands and surrounding stratiform regions within the cyclone structure. In addition to validating the convective precipitation detection using brightness temperature satellite observations and ERA5 reanalysis, we also incorporated pluviometer records. These ground-based measurements confirmed the model’s strong capability to identify areas affected by convective precipitation. This study demonstrates the potential of integrating a physics-based approach with deep learning for high-resolution characterization of precipitation in Mediterranean cyclones. While the segmentation of convective precipitation alone does not directly quantify coastal hazard, these results provide essential input layers for downstream coastal-impact assessments. In particular, the high-resolution identification of convective rainfall can be integrated into hydrological and hydraulic models (e.g., HEC-RAS or similar) to simulate surface runoff, flash-flood dynamics, and related coastal impacts under a changing climate.