河西走廊降水预报模型的建立与样本检验  

Study on precipitation forecast model in the Hexi Corridor and sample test

作　　者：王海燕 田庆明 于亚楠 刘新雨 WANG Haiyan;TIAN Qingming;YU Yanan;LIU Xinyu(Jiuquan Meteorological Bureau of Gansu Province,Jiuquan 735000)

机构地区：[1]甘肃省酒泉市气象局,酒泉735000

出　　处：《暴雨灾害》2022年第5期564-570,共7页Torrential Rain and Disasters

基　　金：甘肃省气象局科技项目(Ms2022-14)。

摘　　要：利用2018年4-9月西北地区共10 189站逐小时降水观测资料及0.25°×0.25°分辨率的欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)预报资料,用V-3θ图及Keras方法,建立了由分类模型和降水量拟合模型组成的河西走廊降水预报模型,再通过“分类检验损失函数”、“TS检验”和“拟合检验均方根误差”三种方法检验了该降水预报模型的预报效果。结果表明:基于Keras建立的两个神经网络框架,采用“k折交叉”和逻辑回归方法,使模型更加可靠、特征关系更加合理,降低特征量的误差影响;衍生V-3θ图,增加特征值的种类,可缩小河西走廊降水预报模型主观识别偏差,实现了降水垂直结构预报的客观定量化;通过三种方法进行检验,发现该模型总体白天预报结果优于夜间,12—18 h的预报结果与实际值最为符合;利用个例对比发现,该模型可较准确地预报降水过程的发生时间、主要降水时段、降水区域范围及降水中心强度,证实该模型对强降水天气有较强的预报能力。Based on hourly precipitation observation data at 10,189 meteorological stations from April to September 2018 in Northwest China and ECMWF prediction data with 0.25°×0.25° resolution, a precipitation prediction model for Hexi Corridor was established by using V-3θ diagram and Keras method. The prediction skill of the precipitation prediction model was examined by three methods: "loss function of classification test", "TS test" and "root mean square error of fitting(RMSE) test". The results show that the two neural network frameworks established by Keras method, using "K-fold crossover" and logistic regression method, can improve the rationality of feature relation, reduce the error of feature quantity, and make the model prediction results more reliable. The V-3θ diagram was derived, and the types of characteristic values were increased, which reduced the subjective identification deviation of precipitation prediction model in Hexi Corridor, and realized the objective quantification of precipitation vertical structure prediction. After being tested by three methods, it is found that the prediction result of the model in daytime is better than that at night, and the prediction result in the range of 12 hours and 18 hours is the most consistent with the actual value. Through the comparison of individual cases, it is found that the model can accurately predict the occurrence time of precipitation, the main precipitation period, the regional range of precipitation and the intensity of precipitation center, and has a strong ability to predict heavy precipitation weather.

关 键 词：河西走廊 V-3Θ图 Keras方法 降水预报模型 

分 类 号：P456[天文地球—大气科学及气象学]