A Machine Learning Model for FY-4A Cloud Detection Based on Physical Feature Fusion

Clouds critically influence Earth’s radiation balance and climate, making accurate cloud detection essential for improving climate models. This study develops the TSAR model to improve the cloud detection accuracy of the FY-4A CLM product by incorporating physical features. The input features include FY-4A brightness temperature (BT) data from channels 8–14, geometric parameters (satellite zenith angle (SAZ), satellite azimuth angle (SAA), solar zenith angle (SOZ), solar azimuth angle (SOA), and latitude), and four ERA5 meteorological factors (2 m air temperature (T2m), skin temperature (SKT), air temperature profiles (ATP), and relative humidity profiles (RH)). Using the CALIPSO cloud detection product as labels, the model outputs cloud/clear-sky classification results. Additionally, four machine learning (ML) algorithms—RF, LightGBM, XGBoost, and MLP—achieved overall accuracies of 91.5%, 92.2%, 92.5%, and 92.8%, respectively, considerably outperforming the FY-4A L2 CLM product (83.1%). The results demonstrate that incorporating physical factors significantly improves cloud detection performance regardless of the algorithm employed. Incorporating meteorological factors notably improved nighttime and water–cloud detection, narrowing day–night accuracy gaps. Shapley additive explanation (SHAP) analysis indicated feature contributions of 15.8%, 50.8%, and 33.3% from geometric, BT, and meteorological variables, respectively, with stronger meteorological effects at mid- to high-latitudes. These findings demonstrate that integrating meteorological factors significantly improves FY-4A cloud detection accuracy and consistency, highlighting the MLP-TSAR model’s effectiveness for reliable all-day operational applications.