黑龙江省准对称混合训练期MOS气温预报性能分析  

Analysis of the Forecast Performance of MOS Temperature Forecast with Quasi-symmetric Mixed Training Period in Heilongjiang Province

作　　者：赵玲[1] 白雪梅[1] 孟莹莹[1] 邢程 刘松涛[1] 付雯[3] ZHAO Ling;BAI Xue-mei;MENG Ying-ying;XING Cheng;LIU Song-tao;FU Wen(Heilongjiang Meteorological Observatory,Heilongjiang Harbin 150030;Heilongjiang Meteorological Data Center,Heilongjiang Harbin 150030;Heilongjiang Meteorological Service Center,Heilongjiang Harbin 150030)

机构地区：[1]黑龙江省气象台,黑龙江哈尔滨150030 [2]黑龙江省气象数据中心,黑龙江哈尔滨150030 [3]黑龙江省气象服务中心,黑龙江哈尔滨150030

出　　处：《黑龙江气象》2024年第2期1-5,共5页Heilongjiang Meteorology

基　　金：黑龙江省气象局科学技术研究项目(HQZD2019002)。

摘　　要：本文选取ECMWF细网格地面2 m气温要素预报产品作为预报因子,选取中国气象局陆面数据同化系统(CLDAS-V2.0)地面2 m气温格点实况数据作为预报量,应用准对称混合训练期MOS方法,建立黑龙江省格点气温MOS方法,并对MOS方法在24 h预报时效内间隔3 h的格点气温预报性能进行检验分析。结果表明:MOS平均绝对误差≤1.5℃;MOS夏半年≤2℃预报准确率为84.1%,比ECMWF提高7.6%;冬半年预报准确率为71.5%,比ECMWF提高18.3%;预报技巧夏半年为14.2%,冬半年为29.8%。MOS夏半年预报效果好于冬半年,冬半年预报改善效果好于夏半年。大、小兴安岭和东南部山区MOS预报效果不如平原地区好,但是MOS改善效果明显好于平原地区。The quasi-symmetrical mixed training period MOS method was applied to establish a grid point MOS temperature forecast in Heilongjiang Province,with the ECMWF high-resolution grid surface 2 m temperature forecast products as the forecast factor and the 2 m temperature land data from the CMA Land Data Assimilation System(CLDAS-V2.0)as the forecast quantity.The forecast performance of grid point temperature with a 3-hour interval within 24 hour time was tested and analyzed.The results show that:the MOS forecast mean absolute error is≤1.5℃;the accuracy of MOS forecasts is 84.1%in the summer half-year,which is 7.6%higher than ECMWF;the accuracy of MOS forecasts is 71.5%in the winter half-year,which is 18.3%higher than ECMWF;the forecast skill score is 14.2%in the summer half-year and 29.8%in the winter half-year.The MOS forecast results are better in the summer half-year,and the improvements of forecasting performance are better in the winter half-year.The MOS forecast results in the Large Khingan mountainous,Lesser Khingan and the Southeastern mountainous areas are not as good as in the plains,but the improvements of forecast performance are significantly better than the plains.

关 键 词：准对称混合训练期MOS方法 气温 ECMWF CLDAS 预报性能 

分 类 号：P456.7[天文地球—大气科学及气象学]