WRF-3DVAR逐小时雷达同化系统在华北地区降雨径流预报中的应用  

An hourly updated WRF-3DVar weather radar data assimilation system and its application for rainfall-runoff prediction in North China

作　　者：刘昱辰 刘佳[1] 李传哲[1] 王维 田济扬[1,3] LIU Yuchen;LIU Jia;LI Chuanzhe;WANG Wei;TIAN Jiyang(State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research,Beijing 100038,China;Power China Chengdu Engineering Corporation Limited,Chengdu 610072,China;Research Center on Flood&Drought Reduction of the Ministry of Water Resources,Beijing 100038,China)

机构地区：[1]中国水利水电科学研究院流域水循环模拟与调控国家重点实验室,北京100038 [2]中国电建集团成都勘测设计研究院有限公司,成都610072 [3]水利部防洪抗旱减灾工程技术研究中心,北京100038

出　　处：《遥感学报》2023年第7期1590-1604,共15页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:51822906);国家重点研发计划(编号:2017YFC1502405)。

摘　　要：本研究从当前数值大气模式应用于水文预报的现状出发,分析了将数据同化技术融入到陆气耦合水文预报中的重要性,针对时空分辨率较高的天气雷达测雨数据,在WRF模式耦合WRF-Hydro陆面水文模式的基础上,构建了逐小时快速更新的WRF-3DVAR同化系统,并进一步验证了其在华北地区降雨径流预报中的应用效果。结果表明,同化天气雷达结合传统气象监测数据可以有效提高WRF模式的预报降雨精度,特别是针对时空分布均匀型的降雨,同化后预报降雨的CSI/RMSE指标提高了23.24%—50.00%。在径流预报中,经过数据同化后,更准确的预报降雨使径流预报结果也一定程度上得到了改善,洪峰流量和洪量误差均有所降低,Nash系数也所提高,整体上数据同化对预报水文过程的改进效果也较为明显。但对于时空分布不均匀型的降雨,洪峰流量和峰现时间的预报结果仍不理想,后续需要从陆面水文参数的精准率定、预报误差的实时校正等方面进行改进。The frequency of extreme rainfall and flooding in North China has increased because of the influence of climate change and human activities.Convective and strong precipitation processes occur in summer.Under the influence of the mixed flow generation mechanism in semihumid and semiarid areas,the flood burst is strong and difficult to forecast.Based on the Weather Research Forecast(WRF)model,namely,coupled WRF-Hydro,this study uses three-dimensional variational data assimilation(3DVAR)in constructing the WRF-3DVAR assimilation system for a rapid hourly update to assimilate high spatial and temporal resolution radar reflectivity data with the traditional meteorological observed data from the Global Telecommunication System(GTS).The study of rainfall-runoff prediction based on the land-atmosphere coupling is conducted by taking the typical rainfall processes of the north and south branches of the Daqinghe River Basin as the research object.Moreover,the performance of the rainfall-runoff prediction method in North China is further verified.The research results have some theoretical and practical values for constructing the data assimilation system of the atmospheric model and flood forecast practice in northern China.We employ three nested domains and adopt the GFS data for driving the WRF model.This study evaluates the improvement effect of WRF on forecasting rainfall and WRF-Hydro forecasting runoff by assimilating radar reflectivity and GTS data.The GTS data are released every 6 h.Thus,in the hourly assimilation scheme,GTS is only assimilated at the 6th,12th,18th,and 24th h from the start of the storm.However,radar reflectivity is set to assimilate once every hour.The rainfall evaluation indexes include Root Mean Square Error(RMSE),Mean Bias Error(MBE),and Critical Success Index(CSI).CSI/RMSE is a comprehensive index for evaluating rainfall forecast results.RMSE,MBE,and Nash(Nash-Sutcliffe efficiency coefficient)are used to evaluate runoff.The results show that the precipitation forecasted by the WRF model is alwa

关 键 词：遥感 数据同化 天气雷达 快速更新 WRF-3DVAR 降雨径流预报 

分 类 号：P2[天文地球—测绘科学与技术]