利用贵州省铜仁市新一代多普勒天气雷达资料和贵州省气象局闪电定位仪资料,对黔东北2013-2015年5-8月对流性降雨过程中的雷电活动与雷达回波单体之间的相关性进行统计分析,提取出反映雷电活动的雷达产品特征量,得出该区域雷电预警阈值...利用贵州省铜仁市新一代多普勒天气雷达资料和贵州省气象局闪电定位仪资料,对黔东北2013-2015年5-8月对流性降雨过程中的雷电活动与雷达回波单体之间的相关性进行统计分析,提取出反映雷电活动的雷达产品特征量,得出该区域雷电预警阈值。结果表明:35.0 d Bz回波顶高(ET)突破-10℃层高度和25.0 d Bz回波顶高突破-20℃层高度这两个指标预警效果较好,成功预警率POD为0.99和0.96,预警提前时间Tw分别为23和17 min;回波顶高大于9.0 km预警雷电发生,预警提前时间为11 min;垂直液态水含量可以作为雷电预警的必要条件,其值介于5.0~15.0 kg·m-3。展开更多
The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). I...The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.展开更多
文摘利用贵州省铜仁市新一代多普勒天气雷达资料和贵州省气象局闪电定位仪资料,对黔东北2013-2015年5-8月对流性降雨过程中的雷电活动与雷达回波单体之间的相关性进行统计分析,提取出反映雷电活动的雷达产品特征量,得出该区域雷电预警阈值。结果表明:35.0 d Bz回波顶高(ET)突破-10℃层高度和25.0 d Bz回波顶高突破-20℃层高度这两个指标预警效果较好,成功预警率POD为0.99和0.96,预警提前时间Tw分别为23和17 min;回波顶高大于9.0 km预警雷电发生,预警提前时间为11 min;垂直液态水含量可以作为雷电预警的必要条件,其值介于5.0~15.0 kg·m-3。
基金jointly supported by the National Science Foundation of China (Grant Nos. 42275007 and 41865003)Jiangxi Provincial Department of science and technology project (Grant No. 20171BBG70004)。
文摘The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.
