MLP在雷达定量降水估测中的应用

APPLICATION OF MLP IN RADAR QUANTITATIVE PRECIPITATION ESTIMATION

关键词:  快速动态分级法  K近邻  多层感知器  定量降水估测  Z-I关系

Key words:  Fast Dynamic Categorical method  K-Nearest Neighbor  multilayer perceptron  quantitative precipitation estimation  Z-I relations

摘要:

通过临近时次、临近空间降水回波性质相似假设，以人工智能技术结合快速动态分级法(Fast Dynamic Categorical method，FDC)为核心，设计广西区域分钟级雷达定量降水估测产品模型。在单部雷达估测降水时，分两层三次使用感知器寻求合理的降水类型分类Z-I关系，首先将FDC看为一种多分类算法，根据单站雷达各级回波的降水估测结果误差将回波区内的站点分为强站点和弱站点两类，然后分别对这两类站点再次使用FDC建立新的强、弱两类Z-I关系。在多部雷达组网联合估测定量降水时，将各雷达估测值等权重看待，将单部雷达估测作为一个分支，通过连结方式构建一个多层感知器(Multilayer Perceptron，MLP)。无站点回波区采用K近邻算法(K-Nearest Neighbor，KNN)选择合适的MLP求得的Z-I关系估算降水量。对2019年3—10月试验产品进行检验分析，结果表明以区域站组成的训练组小时降水相关系数达0.973 7，以国家级气象台站组成的测试组相关系数达0.825 6。

Abstract:

Based on the assumption of similar properties of precipitation radar echoes in adjacent time and space, a minute-level radar quantitative precipitation estimation(QPE) product model in Guangxi is designed according to a core method which combins AI with the fast dynamic classification method (FDC). When precipitation is estimated by a single radar, a reasonable Z-I relationship is calculated by using a two-layer perceptron for three times. First, FDC is considered d as a multi-classification algorithm. According to precipitation estimation error ofsingle radar echoes at all levels, the stations in the echo area are divided into two categories: strong stations and weak stations. And then，by means of FDC applied to these two types of stations respectively，new strong and weak Z-I relationships are found. As for multiple joint radars used in QPE，the estimated values of each radar are treated with equal weights, and the single radar estimation is considered as a branch. A Multilayer Perceptron(MLP) is constructed by connection. In the no-station echo area, the KNN algorithm is used to select the appropriate MLP to obtain the Z-I relationship for precipitation estimation. The test and analysis of the model products from March to October in 2019 showed that the correlation coefficient of hourly precipitation of the train group composed of regional stations was 0.973?7, and the correlation coefficient of the test group composed of national meteorological stations was 0.825?6.