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基于形状匹配的雷达临近预报误差评价计算方法

曹春燕 陈凯 陈元昭 刘军 陈劲松 贺佳佳 陈训来

气象2017,Vol.43Issue(8):987-997,11.
气象2017,Vol.43Issue(8):987-997,11.DOI:10.7519/j.issn.1000-0526.2017.08.009

基于形状匹配的雷达临近预报误差评价计算方法

A Novel Method of Error Evaluation for Radar Nowcasting Based on Shape Matching

曹春燕 1陈凯 2陈元昭 3刘军 1陈劲松 2贺佳佳 3陈训来3

作者信息

  • 1. 深圳市气象局,深圳518040
  • 2. 深圳南方强天气研究重点实验室,深圳518040
  • 3. 中国科学院深圳先进技术研究院,深圳518040
  • 折叠

摘要

Abstract

The relatively conventional pattern forecast method of local short-time rainfall is mainly based on satellite images and radar echo extrapolation,but it is also worth finding a better method which can make good use of ground observation data to effectively test the radar forecast products.In the actual weather forecast,rainfall is often characteristiced regional and planar spatial distribution.So the test about shape of rainfall area is more important and more significant.In order to solve the problem in shape test with variety of difficulties and characteristics.This paper proposed a comprehensive evaluating method of 0-3 h QPF (quantitative precipitation forecast) radar rainfall forecast and quantitative indexes of shape test.We also did some experiments including grading test and error analysis on a typical radar forecast of continuous raining process in Guangdong Province in 08:30 11:24 BT 22 April 2016.To some extent,this method also has a good solution of poor data quality and accuracy control in temporal and spatial scale.The TSshape,PODshape,FARshape,Ratiop,Ratiot,Jaccard of a half hour and 1 h radar rainfall forecast shape test are above40%,above40%,below30%,above40%,above80%,above 40% respectively.The experimental results reflect the effect of radar rainfall forecast well and show that the indexes of shape test are basically consistent with conventional quantitative indexes.

关键词

雷达/降雨/插值/形状检验/质量控制

Key words

radar/rainfall/interpolation/shape test/quality control

分类

天文与地球科学

引用本文复制引用

曹春燕,陈凯,陈元昭,刘军,陈劲松,贺佳佳,陈训来..基于形状匹配的雷达临近预报误差评价计算方法[J].气象,2017,43(8):987-997,11.

基金项目

广东省科技厅项目(2014A020218014和2016A020223016)共同资助 (2014A020218014和2016A020223016)

气象

OA北大核心CSCDCSTPCD

1000-0526

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