摘要
为提高短时强降水的监测和预警技术在山区等复杂地形中的应用,本文选取相关的探空资料分析天气背景,划分降水类型,与自动站订正技术相结合,采用Z-I分型最优化技术估测降水,利用交叉相关法对雷达反射率因子进行外推,并预测30min后的累积降水量。根据近3年的观测资料,制定出适合岳阳地区雷达反射率因子以及雷达估测降水对应的降水量级。通过与自动站雨量对比,Z-I分型最优化技术估测降水的相对误差较小;与Z-I经验公式估测降水对比分析,雷达估测降水的准确率有一定提高。经过岳阳示范区一年的实际应用表明:该方法对短时强降水的估测和预警均取得良好的效果,对强降水引起的山体滑坡等灾害具有一定的防范作用。
In order to improve the monitoring and early warning technology of short-time strong rainfall and its application to mountain areas,selecting the relevant radiosonde data,identifying the type of precipitation,and combined with the automatic correction technology,the classification optimization technique of Z-I relationship is used to estimating rainfall.Besides,the radar reflectivity factor is extrapolated by using the cross-correlation extrapolation method,and the cumulative precipitation in 30 minutes is predicted.Based on the observation data of nearly three years,the grades of precipitation corresponding to the radar reflection and radar rainfall estimate at Yueyang are determined.Compared with the data of automatic stations,the relative error of the precipitation estimate is smaller;compared with the empirical formula of Z-I relationship,the accuracy of the rainfall estimate by the classification optimization technology of Z-I relationship shows improvement.After a year of application in Yueyang Meteorological Service,the early warning technology has a good result in the precipitation estimation and short-time strong rainfall warning.
作者
周北平
杜爱军
苟尚
杜宇飞
Zhou Beiping;Du Aijun;Gou Shang;Du Yufei(Weather Forecast Training Center,Institute of Atmospheric Sciences,Nanjing 210044;Banan Meteorological Service,Chongqing 401320;Lanzhou Central Meteorological Observatory,Lanzhou 730020;Chongqing Flood Prevention and Drought Resisting Center,Chongqing 401120)
出处
《气象科技》
2018年第3期490-496,共7页
Meteorological Science and Technology
基金
重庆市青年基金项目(QNJJ-201710)
中国气象局预报员专项(CMAYB2018-078)资助
关键词
短时强降水
雷达估测降水
最优化技术
Z-I分型
预警
山区
short-time strong rainfall
radar precipitation estimate
optimization technique
Z-I classification
earlywarning
mountain area
