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Verification and integration of TIGGE multimode precipitation forecast products
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摘要: 数值预报产品的检验与集成是使用和发展数值预报的重要环节。以TIGGE数据中心的NCEP、ECMWF、JMA和KMA等4种模式控制预报产品为基础资料，选取福建池潭水库流域为研究对象，从降水分级预报、降水量级和过程预报等方面对数值预报产品进行了综合评估，同时采用回归集成、TS集成和Nash系数集成等3种方法对多模式产品开展了降水集成预报，在此基础上，探讨了不同集成方法对最终降水预报效果的影响。结果表明，4种产品对无雨和小雨的预报效果均较好，在不同量级降水预报中，JMA模式更适合于25 mm以下量级的降水预报，而ECMWF对25 mm以上量级的降水预报效果较好。对于日降水量预报，NCEP模式的预报效果较差，而ECMWF模式的预报相对更准确。对于降水过程预报，KMA的预测性能则明显差于其他3个模型。集成预报对降低预报误差并提高降水过程预报有较好的效果，其中针对不同量级分类加权的TS集成方法预报效果最优，且提升了高量级降水预测效果。Abstract: Verification and integration of numerical weather prediction (NWP) products is an important step in the application and development of numerical prediction. Based on the NCEP, ECMWF, JMA and KMA NWP models from TIGGE center, the basin of Chitan Reservoir in Fujian province was selected as the research object, the multimodal precipitation forecast products were evaluated from the aspects of precipitation classification forecast, precipitation level and process forecast comprehensively. Meanwhile, three methods of regression integration, TS integration and Nash coefficient integration were used to integrate multimodal precipitation forecast products, and the influence of different integration methods on the final precipitation forecast effect was discussed. The results show that the four products had good prediction effect for both no rain and light rain events. In the precipitation forecast of different magnitudes, the JMA model was the best for the precipitation forecast of the magnitude less than 25 mm, but the prediction of ECMWF for precipitation over 25 mm was better. For the daily precipitation forecast, the NCEP model had a poor performance, while the ECMWF model was the most accurate. For the precipitation process forecast, the prediction performance of KMA was obviously worse than the other three models. The integrated forecast had a good effect on reducing the prediction error and improving the precipitation process forecast, among which the TS integrated method weighted by different magnitudes had the best forecast effect, and had better improved the prediction effect of high level precipitation events.
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图 1 池潭流域及TIGGE网格地理位置
Figure 1. Geographical location of Chitan watershed and TIGGE grid
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图 2 NCEP、ECMWF、JMA和KMA模式预报分级检验结果
Figure 2. Classification test results of NCEP, ECMWF, JMA and KMA model
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图 3 各个模式预报检验指标的月变化
Figure 3. Monthly variation of forecast verification indexes of each model
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图 4 各个模式预报检验指标的季节变化
Figure 4. Seasonal variation of the forecast verification indexes of each model
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图 5 集成预报与常规预报的降水分级TS评分检验结果
Figure 5. The classification TS score test results of integrated forecast and conventional forecast products
表 1 数值预报产品基本信息
Table 1 Basic information of the numerical prediction products
数据中心 数据同化 垂直层数 (模式顶层/hPa) 分辨率 成员数目 预报时长/d 更新时间(UTC) 欧洲中期天气预报中心(ECMWF) 4D-Var 137 (0.01) TL1279 50+1 15/1 00/12 日本气象局(JMA) 4D-Var 100 (0.1) TL959 50+1 10/1 00/12 韩国气象局(KMA) 4D-Var 70 (0.1) N512 24+1 10/1 00/12 美国气象局(NCEP) GSI 64 (2.73) TL1534 20+1 16/1 00/06/12/18 
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表 2 集成预报与常规预报产品检验指标统计
Table 2 Verification index statistics of integrated forecast and conventional forecast products
评估指标 回归集成 Nash集成 TS集成 NECP ECMWF JMA KMA MAE/mm 3.71 3.67 3.66 4.30 3.91 3.79 3.96 RMSE/mm 8.08 8.19 8.24 9.03 8.69 8.89 8.92 Sl 0.49 0.27 0.27 0.24 0.27 0.36 0.34 Xl/mm −3.89 −7.59 −7.50 −7.60 −7.12 −6.98 −7.37 Sg 0.51 0.66 0.65 0.64 0.58 0.51 0.54 Xg/mm 3.53 2.52 2.51 3.86 3.41 2.53 2.78 AS 0.84 0.84 0.86 0.81 0.83 0.83 0.82 NSE 0.61 0.60 0.61 0.52 0.55 0.53 0.49
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