CAJ | 학술논문

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％。
재유전산법화입자군산법적기출상，채용권중분배방법개전기우혼합연화산법적다모식기온집성예보방법연구。이용2012년5-10월중국기상국 GRAPES 모식、북경시기상국 BJ-RUC 모식、중국기상국 T639모식、천진시기상국 TJWRF 모식24 h 예보시효적축6 h 지면2 m 고도기온화35개천진구역자동기상참점자료，통과축일곤동건립집성예보모형，대혼합연화산법적다모식기온집성예보방법진행료절대오차재2℃이내적분급、분류급분참검험분석。결과표명：사용해방법건립적기온집성예보모형구유비교가고적예보능력，예보오차명현소우임일성원，예보준학솔고。안절대오차불대우2℃적검험표준，2012년35개참축6 h 기온、최저기온、최고기온적집성예보평균준학솔분별위76．34％，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. <br> In order to eliminate the impact of seasonal temperature characteristics of Tianjin,the daily rolling in-tegrated forecast model based on 30-day data is adopted in practical operation applications with hybrid evo-lutionary 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 sepa-rately run by China National Meteorological Center,Tianjin Meteorological Bureau and Beijing Meteoro-logical 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 is 76.34%,77.88% and 78.00% from May to October,respectively.