The construction and comparison of high resolution precipitation type prediction models based on three machine learning methods

KeyWord:Precipitation phase;Objective forecast;Numerical model;Climate statistics;Machine learning method

Abstract:

      Refined and objective prediction of precipitation type and its transition time in winter is of great practical significance for improving the quality of forecast service. This paper establishes a high-resolution precipitation type prediction model based on temperature and weather phenomena data collected at 174 national automatic weather stations for the period 1955—2019 in Beijing-Tianjin-Hebei and the high-resolution forecast products of rapid update multi-scale analysis and forecast system-integrated subsystem (RMAPS-IN) using three machine learning methods, i.e., the XGBboost, the support vector machine (SVM) and the depth neural network (DNN) prediction models. Firstly, differences in spatial distribution between various precipitation types and corresponding climatologically mean probabilities of air temperature and wet bulb temperature at 174 national stations in Beijing-Tianjin-Hebei region are statistically analyzed. The fine integrated products provided by RMAPS-IN, i.e., 2 m air temperature, dew point temperature, relative humidity, snowline height, the ratio of frozen precipitation to total precipitation in the near surface atmosphere for different precipitation types, and the analysis fields of three-dimensional meteorological elements such as temperature and wet bulb temperature are analyzed. The observational weather phenomena, climatological characteristics of precipitation type over complex terrain and high-resolution model output products are taken as feature vectors. The classification model of precipitation type is then established based on the XGBboost, SVM and DNN, and the prediction effects of three machine learning algorithms on rain, sleet and snow are compared and evaluated. The results show that: (1) the accuracy of the three machine learning methods for rain, sleet and snow prediction can be significantly improved by adding climatological features of precipitation type over complex terrain to the feature parameters; (2) the prediction ability of the three machine learning methods for rain and snow is better than that for sleet; (3) the XGBboost and DNN have the same prediction ability, which are obviously better than SVM.