| 基于集合成员订正的强降水多模式集成预报 |
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| 引用本文： | 智协飞,赵忱.基于集合成员订正的强降水多模式集成预报[J].应用气象学报,2020,31(3):303-314. |
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| 作者姓名： | 智协飞 赵忱 |
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| 作者单位： | 南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京 210044 |
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| 摘 要： | 基于TIGGE资料集下欧洲中期天气预报中心（ECMWF）、日本气象厅（JMA）、英国气象局（UKMO）、美国国家环境预报中心（NCEP）和中国气象局（CMA）5个气象预报中心2016年5月1日—8月31日中国地区逐日起报预报时效为24~168 h的24 h累积降水量集合预报的结果，对各个集合预报成员进行了频率匹配法的订正，并对订正前后的多模式集成预报效果进行评估。结果表明:采用频率匹配法订正后的降水预报，有效改善了集合平均预报中强降水（日降水量25 mm以上）预报由平滑作用产生的量级偏小现象，使预报的降水量级更接近实况，但对降水落区预报改进不明显。基于卡尔曼滤波技术的集成预报效果优于基于线性回归的超级集合预报和消除偏差集合平均预报，对强降水落区的预报较单模式更优。基于集合成员订正的降水多模式集成预报在强降水的落区预报和降水中心的量级预报更接近实况，效果优于原始多模式集成预报与单模式结果。
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| 关 键 词： | 频率匹配法 集合预报 多模式集成 预报误差 TS评分 |
| 收稿时间： | 2019-10-28 |
Heavy Precipitation Forecasts Based on Multi-model Ensemble Members |
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| Institution: | Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044 |
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| Abstract: | Based on the daily 24-168 h ensemble precipitation forecasts over China from 1 May to 31 August in 2016 from the global ensemble models of ECMWF, JMA, NCEP, CMA and UKMO extracted from the TIGGE archives, the frequency matching method is tested to calibrate the precipitation frequency of each ensemble member. Then results of multi-model ensemble forecasts before and after calibration, including Kalman filter(KF), multi-model super-ensemble (SUP) and bias-removed ensemble mean(BREM), are analyzed in order to improve the prediction of precipitation based on numerical weather forecast data. Results show that precipitation forecasts calibrated by the frequency matching method, which uses the moderate precipitation to correct light and heavy precipitation, can effectively improve the problem of the underestimation of heavy precipitation caused by ensemble mean forecast and improve the positive deviation of the ensemble forecasting system, so that the precipitation forecast category is closer to the observation. However, the frequency matching method can barely improve the prediction of precipitation area. Different from frequency matching method, multi-model ensemble forecasts can extract and consider features of each model, therefore the prediction of precipitation area is more accurate than each single model, but the result is not as good as the frequency matching method in terms of the prediction of precipitation category. Among different multi-model methods, because of the updated weights over time, the result of Kalman filter forecast is superior to SUP and BREM in terms of threat scores, root mean square error (RMSE) and anomaly correlation coefficient (ACC). Furthermore, combining advantages of the above two methods, the multi-model ensemble precipitation after calibration based on ensemble members is more effective in the prediction of heavy precipitation category and area, which is closer to the observation. Results improve the threat score (TS) of the precipitation in all forecast lead times, especially in the heavy precipitation with the TS of 24 h forecast reaching 0.26, indicating a lower false alarm rate and missing rate compared with single model. Results also improve ACC and RMSE of the heavy precipitation and this method produces the best results among all the other methods, especially in the coastal areas in the south of China. In terms of the prediction of precipitation area, results effectively optimize the area of heavy and light precipitation, making the multi-model ensemble precipitation after calibration best in predicting heavy precipitation processes. |
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