基于云团分型动态Z-R算法的浙赣铁路沿线面雨量估算

干旱气象 ›› 2019, Vol. 37 ›› Issue (6): 972-978.

基于云团分型动态Z-R算法的浙赣铁路沿线面雨量估算

段艺萍1，王冠华1，支树林2，凌婷1，傅文兵1

1.江西省气象服务中心,江西南昌330096；2.江西省气象台,江西南昌330096

出版日期:2019-12-31 发布日期:2020-01-02
作者简介:段艺萍（1979— ），女，硕士，高级工程师，主要从事应用气象方面研究. E-mail:54373336@qq.com。

Area Rainfall Estimation Based on Cloud Grouping Z-R Relationship Along Zhejiang-Jiangxi Railway Lines

DUAN Yiping1, WANG Guanhua1, ZHI Shulin2, LING Ting1, FU Wenbing1#br#

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1. Jiangxi Meteorological Service Center, Nanchang 330096, China;
2. Jiangxi Meteorological Observatory, Nanchang 330096, China

Online:2019-12-31 Published:2020-01-02

摘要/Abstract

摘要： 利用雷达组网数据和自动站逐分钟雨量数据，选取浙赣铁路灾害降水个例，运用云团分型的动态Z-R关系拟合算法进行定量降水估测。结果表明：（1）基于铁路沿线的雷达-雨量计降水估测方案可以有效识别降水类型和降水区域的差异性特征，其估测精度优于简单分组Z-R关系拟合算法；（2）对降水估测结果进行效果检验，评估结果可以有效改善雷达QPE的系统误差和局部误差。

关键词: 三维组网, Z-R关系, 定量降水估测

Abstract: Based on the multi-radar mosaic and minute-by-minute gauge observations in five provinces including Jiangxi, Zhejiang, Fujian, Anhui and Hunan, the cloud grouping Z-R relationships fitting method was used to estimate precipitation quantitatively along Zhejiang-Jiangxi railway lines, and five rainfall cases in history resulting in railway disaster were selected to test the effect of precipitation estimation. The results are as follows: (1) The method of quantitative precipitation estimation (QPE) based on the radar-gauge mergence information along the railway lines could effectively capture the difference of the type and area of rainfall, and the prediction result provided better calibration effects than the simple grouping Z-R relationships fitting scheme. (2) The effect test of radar QPE indicated that the system error and local error were improved effectively.

Key words: three-dimensional mosaic, Z-R relationships, QPE (quantitative precipitation estimation)

中图分类号:

段艺萍, 王冠华, 支树林, 凌婷, 傅文兵. 基于云团分型动态Z-R算法的浙赣铁路沿线面雨量估算[J]. 干旱气象, 2019, 37(6): 972-978.

DUAN Yiping, WANG Guanhua, ZHI Shulin, LING Ting, FU Wenbing. Area Rainfall Estimation Based on Cloud Grouping Z-R Relationship Along Zhejiang-Jiangxi Railway Lines[J]. Journal of Arid Meteorology, 2019, 37(6): 972-978.