A Study of Precipitation Inversion in Xinjiang Region Based on FY-4A and Machine Learning Models

Abstract

To compensate for the fact meteorological observation stations in Xinjiang are sparse, and spatial and temporal resolution of precipitation monitoring is insufficient in existing studies, in this study the authors proposed a precipitation inversion model that is based on infrared observation data from the Fengyun-4A satellite and the machine learning method. By combining multichannel satellite remote sensing data with ground meteorological observations, they constructed various machine learning models, such as deep forest, random forest, LightGBMs (light gradient-boosting machines), and XGBoost (extreme gradient boosting), using root-mean-square error, mean absolute error, and the coefficient of determination to evaluate and compare the model performance. The trained deep forest model was used to invert the precipitation in Xinjiang from June to August2023. The results show that the machine learning method is effective in exploiting the nonlinear relationship between the satellite observation features and the ground precipitation, and the inversion results are in good agreement with the ground observation data. Among these models, the deep forest model performs best in daytime conditions, and LightGBM is slightly better in nighttime conditions.