Research on Fengyun-4A cloud base height estimation method based on Extreme Gradient Boosting

Inversion model of Fengyun-4A (FY-4A) satellite cloud base height based on ensemble learning. The research contents include: data preprocessing, screening of influencing factors that are highly correlated with cloud base height, such as cloud top height, infrared brightness temperature, etc., and matching CloudSat and FY-4A data; The inversion model was constructed, the convolutional neural network was used to extract the cloud image features, and the cloud base height was inverted by combining the XGBoost algorithm. The inversion results are compared and analyzed with the CloudSat product. The key problems to be solved include dataset preparation, model training optimization, and model evaluation and validation. The aim of this study is to improve the accuracy of weather forecasting, improve the parameterization scheme of climate models, improve aviation safety, and explore the application potential of FY-4A satellite data in cloud base altitude retrieval.