Application of Segmental-Correction Machine Learning Methods in Radar Quantitative Precipitation Estimation Correction by Rain Gauge

Radar quantitative precipitation estimation (QPE) is one of important applications of Doppler weather radar. The use of the rain gauge data to correct and improve the accuracy of radar QPE is important for weather forecasting. Based on observation data of rain gauges, this study proposes two machine learning methods to correct radar QPE product. The Support Vector Regression (SVR) and Random Forest (RF) models are used as the regression model. The rain gauge data is used as the ground truth. The radar QPE data is divided into three intervals: 0–10 mm/h, 10–30 mm/h, and> 30 mm/h. One traditional method, i.e., the PDF method, is selected for comparison. The experimental results show that the machine learning methods achieve better performance than the PDF method. The scatter plot shows that SVR effectively mitigate the overestimation problem which is a main problem of the original radar QPE product.