Cloud-Type Classification for Southeast China Based on Geostationary Orbit EO Datasets and the LighGBM Model

Abstract

The study of clouds and their characteristics provides important information for understanding climate change and its impacts as it provides information on weather conditions and forecasting. In this study, Earth observation (EO) data from the FY4A AGRI and Himawari-8 CLP products were used to classify and identify distinct cloud types in southeastern China. To reduce the impact of parallax between geostationary satellites, we proposed adopting a sliding detection method for quality control of cloud-type data. Additionally, the Bayesian optimization method was employed herein to tune the hyperparameters of the LightGBM model. Our study results demonstrated that Bayesian optimization significantly increased model performance, resulting in successful cloud-type classification and identification. The simultaneous use of visible and shortwave infrared channels, and brightness temperature difference channels, enhanced the model’s classification performance. Those channels accounted for 43.79% and 21.84% of the overall features, respectively. Certainly, the model in this study outperformed compared with the traditional thresholding method (TT), support vector machine (SVM), and random forest (RF). Results showed a model prediction accuracy of 97.54%, which was higher than that of TT (51.06%), SVM (96.47%), and RF (97.49%). Additionally, the Kappa coefficient of the model was 0.951, indicating the model’s classification results were consistent with the true values. Notably, this performance also surpassed TT (0.351), SVM (0.929), and RF (0.950).