摘要
为评价用于公众气象服务的精细化多模式客观集成预报服务产品(refined multi-model objective consensus forecasting service products,以下简称OCF)多模式集成气温预报效果,分析其误差成因,以中国区域OCF日最高气温和日最低气温预报检验为切入点,对服务影响较大的大误差日及其典型特例——降温日开展检验分析,并与参与OCF集成的ECMWF和NCEP气温预报进行对比。结果表明:OCF日最高气温和日最低气温总体上预报性能优于参与集成的模式预报,准确率夏季高冬季低,拉开了气温变化范围,也有效减小了误差。OCF的大误差日较少,但2~3 d时效及冬半年的大误差日较ECMWF多,与集成的模式预报性能、降温天气相关。针对降温日的检验分析发现:OCF、ECMWF和NCEP在降温日的预报性能有所下降,OCF日最高气温预报误差增长尤其快;OCF对降温日的日最低气温、非降温区域的日最高气温进行了有效订正,但在降温日的降温区域里,其日最高气温预报有明显的正误差特征。基于以上分析,提出了OCF气温集成订正技术改进方向,说明针对性的检验更利于发现客观模式预报及集成订正的问题。
In order to evaluate the effect of refined multi-model objective consensus forecasting service products(OCF)temperature forecasts which are applied in public weather service,and analyze the causes of forecast errors,this paper makes objective verification on OCF daily maximum and minimum temperature forecasts in China,focuses on large error days with high service impact and the typical case:temperature-drop days,and also makes comparison among OCF,ECMWF and NCEP.The results show that OCF daily maximum and minimum temperature forecasts perform better than models in the consensus generally,and the forecast accuracy is higher in summer but lower in winter.OCF enlarges the range of daily temperature variation and effectively reduces forecast errors.OCF has fewer large error days than models in the consensus,but shows larger errors in 2-3 d forecasting periods and winter half year.The large error days of OCF are related to models in the consensus and obvious temperature-drop.It is found that the forecast performances of OCF,ECMWF and NCEP decline in temperature-drop days,and the error of OCF daily maximum temperature forecast increases rapidly.In temperature-drop days,OCF effectively corrects daily minimum temperature and daily maximum temperature in non-temperature-drop areas,but the daily maximum temperature forecast performs with obvious positive error in temperature-drop areas.Finally,based on analysis above,the improvement direction for OCF consensus methods is proposed.The process verification is conducive to discovering the defects of objective forecasts and methods of temperature consensus and correction.
作者
唐延婧
慕建利
袁彬
廖波
裴兴云
杜正静
TANG Yanjing;MU Jianli;YUAN Bin;LIAO Bo;PEI Xingyun;DU Zhengjing(Meteorological Service Center of Guizhou Province,Guiyang 550002;CMA Public Meteorological Service Centre,Beijing 100081)
出处
《气象》
CSCD
北大核心
2023年第4期467-477,共11页
Meteorological Monthly
基金
国家重点研发计划(2018YFC1507802)资助。
关键词
气温集成预报
检验
大误差
降温日
temperature consensus forecast
verification
large error
temperature-drop day
