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几种气温客观预报方法对比及最优集成预报研究*
盛春岩, 范苏丹, 荣艳敏, 孙文奇
(山东省气象科学研究所)
Comparison of Several Objective Methods and Optimal Consensus Forecast Study of Temperature
Sheng Chunyan, Fan Sudan, Rong Yanmin, Sun Wenqi
(Shandong Institute of Meteorological Sciences)
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投稿时间:2020-03-13    修订日期:2020-08-03
中文摘要: 由于模式本身的误差以及地形等影响,对模式产品进行订正释用是提高气温客观预报准确率的重要手段。基于ECMWF细网格预报产品研发了气温偏差订正和准对称混合滑动训练期MOS预报系统,在此基础上,设计提出了一种气温最优集成预报方法。对不同模式和不同客观方法日最高最低气温预报准确率进行了对比分析,结果表明:通过10?30d的偏差滑动订正可以较好提高ECMFW细网格模式日最高最低气温预报准确率。偏差滑动订正在短期内订正效果较显著,对区域站和鲁中山区订正效果尤其明显,对最低气温预报订正效果好于最高气温。MOS客观预报对日最高最低气温预报也有较好的订正效果,但ECMWF细网格、偏差订正、MOS客观预报产品在不同地区、不同季节预报准确率有所不同,采用动态最优集成的方法进行最优集成预报,可以集成不同客观方法的预报优势,在多种客观预报产品的基础上再次提高预报准确率,达到优中选优的目的。
Abstract:Correlation and interpretation of the model’s temperature forecast is the key point due to the model’s system error and the impact of terrain. In this paper bias correction and the quasi-symmetrical mixed running training period MOS forecast systems are developed based on ECMWF fine-resolution model products. With the different methods products an optimal consensus forecast method of temperature is designed. The accuracy rates of different models and different objective methods of daily maximum and minimum temperature are compared. Results show that bias running correction of daily maximum and minimum temperature forecast with 10 to 30 days can improve the ECMWF fine-resolution model’s temperature forecast. Bias running correction can improve the daily maximum and minimum temperature forecast of the models significantly in short range, especially in the mountain area. Bias running correction of daily minimum temperature can give larger improvement of the model’s forecast than that of daily maximum temperature. The MOS system can improve the daily maximum and minimum temperature forecast too, while the accuracy rates of ECMWF fine-resolution model, bias correction and MOS temperature objective forecast are different in different regions and different seasons in Shandong province. Running optimal consensus forecast method can give further improvement of daily maximum and minimum temperature forecast by integrating the advantages of different objective methods.
文章编号:202003130077     中图分类号:    文献标志码:
基金项目:山东省重点研发计划项目(2016GSF120017),“十三五”山东现代农业气象服务保障工程(鲁发改农经〔2017〕97号)和中国气象局省级气象科研所科技创新发展项目(ssfz201714)共同资助
引用文本:
盛春岩,范苏丹,荣艳敏,孙文奇,0.[en_title][J].Meteor Mon,():-.
Sheng Chunyan,Fan Sudan,Rong Yanmin,Sun Wenqi,0.Comparison of Several Objective Methods and Optimal Consensus Forecast Study of Temperature[J].Meteor Mon,():-.