Multi-Paradigm Machine Learning for Opportunistic Rainfall Estimation from Satellite Microwave Links

This study presents a framework for opportunistic rainfall detection and estimation exploiting SNR data from satellite downlink signals. We develop a heterogeneous ensemble including diverse ML architectures for the classification task, namely CRF, GRU, Chronos-Bolt, and TSPulse. Evaluating models characterized by distinct assumptions, scales, and computational complexities provides critical insights into their behavior for the weather and climate remote sensing domain. The resulting ensemble yields a significant improvement in precipitation classification performance. For the regression task, a traditional model leveraging the well-known ITU recommendations is compared against a GBM, with the ML-based approach demonstrating a substantial improvement in KPI. Finally, we tested the integration of the classification and regression modules via a hard and a soft gating mechanism. While these combinations enhance several point-to-point metrics, they do not improve the estimation of event-total accumulated rainfall. These findings highlight the potential of ML approaches for satellite-based opportunistic environmental monitoring.