陕西省精细化网格预报业务系统技术方法  

Operation System of Fine Grid Forecast in Shaanxi Province:Technical Methods

作　　者：王建鹏[1] 薛春芳[2] 潘留杰[1] 胡皓[1] 戴昌明[1] 王丹 Wang Jianpeng;Xue Chunfang;Pan Liujie;Hu Hao;Dai ChangMing;Wang Dan(Shaanxi Meteorological Observatory,Xian 710014;Shaanxi Meteorological Service,Xian 710014;Shaanxi Meteorological Service Centre,Xian 710014)

机构地区：[1]陕西省气象台,西安710014 [2]陕西省气象局,西安710014 [3]陕西省气象服务中心,西安710014

出　　处：《气象科技》2018年第5期910-918,共9页Meteorological Science and Technology

基　　金：中国气象局预报员专项(CMAYBY2018-074;CMAYBY2018-075);气象预报业务关键技术发展专项(YBGJXM2018:03-13)资助

摘　　要：精细化网格预报不仅是目前中国气象局主推的预报业务,而且是未来天气预报的发展方向。本文详细阐述了陕西省精细化网格预报业务系统中数据产品的技术方法。主要包括4个方面:(1)建立了陕西网格预报技术框架,提出"动态交叉最优要素预报"(DCOEF)的方法来建立基础网格预报场。(2)提出"站点订正值向格点场传递"的格点连续性要素订正方法,交叉检验表明该方法在格点场上24h最低、最高温度<2℃的准确率较模式降尺度数据分别提高34%和23%,此外,该方法在背景场协同,主观站点预报和客观格点预报要素值融合一致方面有较好的应用价值。(3)基于"偏差订正"方法订正格点降水,结果表明通过计算预报偏差Bias,来"消空"小雨频率,"补漏"暴雨频率,ECMWF降水预报24h小雨、暴雨TS评分较原模式分别提高2.5%和4.82%。(4)提出"反向离差数据归一化"算法,处理因客观方法或主观订正后数据在时间序列上的矛盾问题,该方法不改变原模式对要素的预报趋势,同时使得要素在时间上协同一致,很好地解决了网格要素预报的时间协同性问题。Fine grid forecast is the main service of the China Meteorological Administration,and also the future development direction of weather forecast.This system improves the spatial resolution（0.025°×0.025°）,and at the same time,meteorological elements such as precipitation and temperature forecast quality.This article described the technical methods in the data products of this system,from four aspects：（1）established the technical framework for grid forecast,using the Dynamic Cross Optimal Elements Forecast（DCOEF）method to establish the background field of grid forecast,which means comparing different models element forecast results and selecting that with higher forecast quality in past15 days as the base field for forecasters;（2）proposed the method ofstation-revised value transmitting to the grid field＂for consecutive elements correction.The cross test shows that the accurate rate of 24-hour minimum and maximum temperature（〈2 ℃）are improved by 34% and 23%,respectively,by this method compared to the model downscaling data,and also,the method has better application value in the combination of the background field collaborative and subjective station forecast and objective grid element forecasts;（3）based on the Bias Correction method to correct grid precipitation;the results show that through calculating forecast bias to decrease light rain frequency and increase rainstrom frequency,the 24-hour TS（Threat Score）improved by 2.5%and 4.82%,respectively,compared to the original model.（4）proposed the reverse deviation data normalization algorithm to deal the inconsistent problem of the objective or subjective correction data in the time series,which does not change the elements forecast trends of original models,and at the same time,the elements are coordinated in time,so to solve the problem of time coordination of grid elements.

关 键 词：网格预报 动态交叉最优要素预报 偏差订正 温度站点逼近 要素协同 降尺度 

分 类 号：P456.7[天文地球—大气科学及气象学]