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Forecast Method of Multi-model Air Temperature Consensus in Tianjin
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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%。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.
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图 1 2012年5—10月集成预报与GRAPES，BJ-RUC，T639，TJWRF模式预报总体平均绝对误差
Fig. 1 Mean absolute error of temperature from consensus forecast and GRAPES, BJ-RUC, T639, TJWRF forecasts from May to October in 2012
图 2 2012年5—10月集成预报与GRAPES，BJ-RUC，T639，TJWRF模式预报气温平均绝对误差
Fig. 2 Monthly mean absolute error of temperature from consensus forecast and GRAPES, BJ-RUC, T639, TJWRF forecasts
图 3 2012年5—10月35个区域自动气象站气温集成预报的平均准确率
Fig. 3 Mean accuracy of temperature at 35 automatic meteorological stations from May to October in 2012
图 4 2012年9—10月最低气温、最高气温集成预报与实况对比
Fig. 4 Comparison between consensus forecast and observation of minimin and maximum temperatures from September to October in 2012
表 1 2012年5—10月集成预报与GRAPES，BJ-RUC，T639，TJWRF模式预报气温平均绝对误差 (单位：℃)
Table 1 Monthly mean absolute error of temperature from consensus forecast and GRAPES, BJ-RUC, T639, TJWRF forecasts from May to October in 2012(unit:℃)
预报模式 5月 6月 7月 8月 9月 10月 集成预报 1.44 1.37 1.46 1.49 1.35 1.40 GRAPES模式 3.36 3.49 3.52 2.92 3.64 2.92 BJ-RUC模式 2.46 2.39 2.67 2.53 2.45 1.93 T639模式 4.88 4.00 4.03 4.29 4.09 4.02 WRF模式 2.30 2.11 2.61 2.51 2.28 1.81 
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表 2 2012年5—10月定时气温集成预报平均准确率
Table 2 Mean accuracy of specified temperature consensus forecast from May to Octorber in 2012
Ea范围 02:00 08:00 14:00 20:00 Ea≤1℃ 51.04% 52.25% 41.67% 43.76% 1℃＜Ea≤2℃ 30.35% 28.53% 28.96% 28.80% Ea≤2℃ 81.39% 80.78% 70.63% 72.56%
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表 3 2012年6月平均最低气温集成预报准确率 (单位：%)
Table 3 Mean accuracy of minimin temperature from consensus forecast in June 2012(unit:%)
Ea范围 遗传算法 粒子群优化算法 等权重混合算法 平均误差计算混和算法 Ea≤1℃ 45.5 45.0 45.3 45.5 1℃＜Ea≤2℃ 28.9 29.2 28.9 29.0 Ea≤2℃ 74.4 74.2 74.2 74.5
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