| 天津市多模式气温集成预报方法 |
| |
| 引用本文： | 吴振玲,潘璇,董昊,徐姝,汪靖.天津市多模式气温集成预报方法[J].应用气象学报,2014,25(3):293-301. |
| |
| 作者姓名： | 吴振玲 潘璇 董昊 徐姝 汪靖 |
| |
| 作者单位： | 1.天津市气象台，天津 300074 |
| |
| 基金项目： | 资助项目：中国气象局气象关键技术集成与应用项目（CMAGJ2012M04） |
| |
| 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%。
|
| 关 键 词： | 混合演化算法 滚动建模 多模式集成 气温预报 |
| 收稿时间： | 2013-06-13 |
Forecast Method of Multi model Air Temperature Consensus in Tianjin |
| |
| Wu Zhenling,Pan Xuan,Dong Hao,Xu Shu and Wang Jing.Forecast Method of Multi model Air Temperature Consensus in Tianjin[J].Quarterly Journal of Applied Meteorology,2014,25(3):293-301. |
| |
| Authors: | Wu Zhenling Pan Xuan Dong Hao Xu Shu and Wang Jing |
| |
| Affiliation: | 1.Tianjin Municipal Meteorological Observatory, Tianjin 3000742.Tianjin University of Science & Technology, Tianjin 300222 |
| |
| Abstract: | 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 is 76.34%, 77.88% and 78.00% from May to October, respectively. |
| |
| Keywords: | genetic algorithm rolling modeling multi model consensus air temperature forecast |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《应用气象学报》浏览原始摘要信息 |
|
点击此处可从《应用气象学报》下载全文 |
|
