An Interpretable Machine Learning Approach for Quantitative Precipitation Estimation from Multi-Source Remote Sensing Data

Machine Learning (ML) models are powerful tools for meteorological applications but often operate as "black boxes", hindering scientific understanding. This study addresses this challenge by implementing an interpretable ML approach Rulefit for Quantitative Precipitation Estimation (QPE). The algorithm was applied to a multisource dataset from eastern China, incorporating composite radar reflectivity from China’s New Generation DopplerWeather Radar, Digital Elevation Model data, and Himawari-8 satellite bands 7-10. A geographical mask based on China’s national borders was applied to exclude data points outside the land area. Additionally, standard scaling normalization was applied to all input features to account for different units and value ranges across datasets. The model’s performance was evaluated against six baseline models and traditional Z-R relationship. The RuleFit model demonstrated strong predictive performance, achieving a Critical Success Index (CSI) of 0.6015 and a Probability of Detection (POD) of 0.9359 for 10-minute precipitation events exceeding a 2mm threshold. This accuracy was comparable to other ML models and significantly surpassed traditional Z-R relationship methods. Crucially, the generated decision rules and Partial Dependence Plots(PDP) provided transparent insights into the model’s logic, revealing key non-linear interactions between radar and satellite features and showing an emphasis on predicting heavy rainfall. Our findings show that RuleFit is not only an accurate QPE tool but also a framework for uncovering meteorological relationships, thereby building researcher confidence and addressing the critical need for interpretability in ML-based atmospheric science.