天津市多模式气温集成预报方法
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摘要
在遗传算法和粒子群算法的基础上,采用权重分配方法开展基于混合演化算法的多模式气温集成预报方法研究。利用2012年5-10月中国气象局GRAPES模式、北京市气象局BJ-RUC模式、中国气象局T639模式、天津市气象局TJWRF模式24 h预报时效的逐6 h地面2 m高度气温和35个天津区域自动气象站点资料,通过逐日滚动建立集成预报模型,对混合演化算法的多模式气温集成预报方法进行了绝对误差在2℃以内的分级、分类及分站检验分析。结果表明:使用该方法建立的气温集成预报模型具有比较可靠的预报能力,预报误差明显小于任一成员,预报准确率高。按绝对误差不大于2℃的检验标准,2012年35个站逐6 h气温、最低气温、最高气温的集成预报平均准确率分别为76.34%,77.88%,78.00%。
Based on genetic algorithm and particle swarm optimization,multi-model air temperature consensus forecast technology(MMATCFT)of hybrid evolutionary algorithm(HEG)is studied.The main technical thought of this method is that two integrated forecast models are set up respectively by using the genetic algorithm and particle swarm optimization,and then the final mixed forecasting model is established by the weight distribution scheme,which is founded through comparing forecast mean errors between the two models.In order to eliminate the impact of seasonal temperature characteristics of Tianjin,the daily rolling integrated forecast model based on 30-day data is adopted in practical operation applications with hybrid evolutionary algorithm.Using 2 m air temperature output data of four models of T639,GRAPES,TJWRF,BJ-RUC and observations of 35 automatic weather stations(AWS)in villages and towns of Tianjin from May to October in 2012,the forecast test of MMTCFT is carried out.Then the experimentation result is evaluated using the way of classification and station-separation,according to the meteorological standard that absolute error of temperature forecast is within 2℃.T639,GRAPES,TJWRF and BJ-RUC are separately run by China National Meteorological Center,Tianjin Meteorological Bureau and Beijing Meteorological Bureau.The analysis shows that the temperature consensus forecast model is effective and reliable.The technical scheme of the consensus forecast based on rolling model is more rational.The forecast errors are obviously smaller than any model mentioned above,and the forecast accuracy is higher.The average forecast accuracy of 6 h temperature,the daily maximum and minimum temperature in 35 AWS is76.34%,77.88%and 78.00%from May to October,respectively.
Based on genetic algorithm and particle swarm optimization,multi-model air temperature consensus forecast technology(MMATCFT)of hybrid evolutionary algorithm(HEG)is studied.The main technical thought of this method is that two integrated forecast models are set up respectively by using the genetic algorithm and particle swarm optimization,and then the final mixed forecasting model is established by the weight distribution scheme,which is founded through comparing forecast mean errors between the two models.In order to eliminate the impact of seasonal temperature characteristics of Tianjin,the daily rolling integrated forecast model based on 30-day data is adopted in practical operation applications with hybrid evolutionary algorithm.Using 2 m air temperature output data of four models of T639,GRAPES,TJWRF,BJ-RUC and observations of 35 automatic weather stations(AWS)in villages and towns of Tianjin from May to October in 2012,the forecast test of MMTCFT is carried out.Then the experimentation result is evaluated using the way of classification and station-separation,according to the meteorological standard that absolute error of temperature forecast is within 2℃.T639,GRAPES,TJWRF and BJ-RUC are separately run by China National Meteorological Center,Tianjin Meteorological Bureau and Beijing Meteorological Bureau.The analysis shows that the temperature consensus forecast model is effective and reliable.The technical scheme of the consensus forecast based on rolling model is more rational.The forecast errors are obviously smaller than any model mentioned above,and the forecast accuracy is higher.The average forecast accuracy of 6 h temperature,the daily maximum and minimum temperature in 35 AWS is76.34%,77.88%and 78.00%from May to October,respectively.
引文
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[12]熊聪聪,王静,宋鹏,等.遗传算法在多模式集成天气预报中的应用.天津科技大学学报,2008,23(4):80-84.
[13]熊聪聪,许淑祠;,徐姝.基于粒子群算法的天气滚动预报集成技术.天津科技大学学报,2012,27(1):75-78.
[14]牛保山,姚继先,郭文孝,等.县站温度集成预报方法.河南气象,2004(1):11.
[15]林春泽,智协飞,韩艳,等.基于TIGGE资料的地面气温多模式超级集合预报.应用气象学报,2009,20(6):706-712.
[16]杞明辉,许美玲,程建刚,等.天气预报集成技术和方法应用研究.北京:气象出版社,2006:77-87.
①国家气象信息中心.2012年1-12月全国城镇天气预报产品质量评估报告.2013.
[2]金龙,陈宁,林振山.基于人工神经网络的集成预报方法研究比较.气象学报,1999,57(2):198-207.
[3]魏风英.全国夏季降水区域动态权重集成预报试验.应用气象学报,1999,10(4):402-409.
[4]周家斌,彭瑶.制作汛期降水集成预报的分区权重法.应用气象学报,1999,10(4):428-435.
[5]周家斌,张海福.一种汛期降水分布的客观集成预报方法.应用气象学报,2000,11(增刊):93-97.
[6]周家斌,张海福,杨桂英,等.制作汛期降水集成预报的分区权重法.应用气象学报,1999,10(4):428-435.
[7]刘还珠,郝为.林孔元,等.基于智能计算的多模型气象综合预报暴雨落区预报实用方法.北京:气象出版社,2000:30-37.
[8]陈桂英.权重分布法集成预报试验.应用气象学报,2000,¨(增刊):51-57.
[9]苗爱梅,吴晓荃,董存仙,等.太原地区温度集成预报方法研究.山西气象,1997(3):2-6.
[10]赵声蓉.多模式温度集成预报.应用气象学报,2006,17(1):52-58.
[11]李倩,胡邦辉,王学忠,等.基于BP人工神经网络的区域温度多模式集成预报试验.干旱气象,2011,29(2):231-235.
[12]熊聪聪,王静,宋鹏,等.遗传算法在多模式集成天气预报中的应用.天津科技大学学报,2008,23(4):80-84.
[13]熊聪聪,许淑祠;,徐姝.基于粒子群算法的天气滚动预报集成技术.天津科技大学学报,2012,27(1):75-78.
[14]牛保山,姚继先,郭文孝,等.县站温度集成预报方法.河南气象,2004(1):11.
[15]林春泽,智协飞,韩艳,等.基于TIGGE资料的地面气温多模式超级集合预报.应用气象学报,2009,20(6):706-712.
[16]杞明辉,许美玲,程建刚,等.天气预报集成技术和方法应用研究.北京:气象出版社,2006:77-87.
①国家气象信息中心.2012年1-12月全国城镇天气预报产品质量评估报告.2013.