Relationship Analysis of Radar Echo and Rainfall Based on Machine Learning

Abstract

多普勒天气雷达产生的雷达回波数据是降雨分析及预测的重要依据，针对如何有效利用雷达回波进行降雨等级分析问题，本文研究了一种基于XGBoost集成学习算法的雷达回波与降雨关系分析模型。本文使用辽宁省气象台提供的历年雷达和降雨气象观测数据，经过数据解码、清洗、匹配后，使用经网格搜索算法优化后的XGBoost方法训练，建立多层雷达回波数据与降雨等级的分类关系。最后通过实验结果表明，基于XGBoost方法得到的结果更接近实际，能够较好地反映云团雷达回波和降雨的关系。 Radar echo data generated by Doppler weather radar is an important basis for rainfall analysis and prediction. Aiming at the problem of how to make effective use of radar echo for rainfall grade analysis, this paper studies an analysis model of the relationship between radar echo and rainfall based on XGBoost ensemble learning algorithm. In this paper, we use the radar and rainfall mete-orological observation data provided by Liaoning Meteorological Station over the years. After data decoding, cleaning and matching, we use XGBoost method optimized by grid search algorithm to establish the classification relationship between multi-layer radar echo data and rainfall level. Fi-nally, the experimental results show that the results based on XGBoost method are closer to reality and can better reflect the relationship between cloud radar echo and rainfall.