| 多模式降水分级最优化权重集成预报技术 |
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| 引用本文： | 危国飞,刘会军,吴启树,陶玮,潘宁.多模式降水分级最优化权重集成预报技术[J].应用气象学报,2020,31(6):668-680. |
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| 作者姓名： | 危国飞 刘会军 吴启树 陶玮 潘宁 |
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| 作者单位： | 1.福建省灾害天气重点实验室, 福州 350001 |
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| 摘 要： | 为综合不同模式对不同量级降水的预报优势，设计一种全球模式与区域模式结合的降水分级最优化权重集成预报算法，集成经最优TS评分订正法（optimal threat score，OTS）订正后的欧洲中期天气预报中心降水预报产品（以下简称EC-OTS）和华东区域中尺度模式降水预报产品（以下简称SMS-OTS）。以泛长江区域（23°~39°N，101°~123°E）为研究范围，基于2018年不同降水量级的TS评分最优化确定SMS-OTS和EC-OTS在不同降水量级时的最优权重系数以及最优集成方案，并以2019年降水数据为独立样本进行预报试验。结果表明:对于最优权重系数，EC-OTS在低降水量级权重较大，随着降水量级的加大，SMS-OTS的权重也逐渐加大；最优集成方案为初始集成降水量预报取SMS-OTS，集成运算迭代3次；集成预报在几乎所有预报时效、所有降水量级的TS评分均高于EC-OTS和SMS-OTS，其平均绝对误差略小于EC-OTS，显著小于SMS-OTS；集成预报12 h累积降水预报的TS评分较省级预报员主观预报高-0.009~0.041，24 h累积降水预报的TS评分较国家气象中心预报员主观预报高0.009~0.023。
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| 关 键 词： | 降水 分级最优化权重 多模式集成 TS评分 |
| 收稿时间： | 2020-06-27 |
Multi-model Consensus Forecasting Technology with Optimal Weight for Precipitation Intensity Levels |
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| Affiliation: | 1.Fujian Key Laboratory of Severe Weather, Fuzhou 3500012.Fujian Provincial Meteorological Observatory, Fuzhou 3500013.Anhui Provincial Meteorological Observatory, Hefei 230001 |
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| Abstract: | In the daily weather forecasting business, different model outputs are available for forecasters, but it's difficult to quickly and accurately make quantitative precipitation forecasts based on subjective analysis. Therefore, statistical post-processing techniques are required to scientifically and rationally integrate the multi-model forecast results, so as to obtain a forecast result that take advantages of each model, and the multi-model consensus forecasting technology is introduced. In the past, the research of multi-model consensus precipitation forecast is either based on global model or regional model, but they are rarely integrated. In addition, for a certain forecast, weights are constant, rarely considering variation of forecast ability among different models and precipitation intensity levels. It is found that the unrevised global and regional model present different advantages in forecasting precipitation of different intensities. Multi-model consensus forecasting for precipitation based on both global and regional models, integrating respective advantages of models at different precipitation levels would produce better objective forecasts.To synthesize both forecasting advantages in global and regional models, a consensus forecasting technology combining global and regional models with optimized weights for different precipitation intensity levels is designed. The consensus forecast combined revised ECMWF-IFS's(European Center for Medium-Range Weather Forecasts-Integrated Forecast System) and SMS-WARMS's(Shanghai Meteorological Service WRF ADAS Real-Time Modeling System) precipitation forecasts, which are revised by optimal threat score method(abbreviated as EC-OTS and SMS-OTS) in the Pan-Yangtze River region(23°-39°N, 101°-123°E). Take 2018 as the model training period of consensus weight and 2019 as the independent sample forecast test period. Comparing the consensus forecast with EC-OTS, SMS-OTS and subjective forecast of forecasters, results show that EC-OTS has a greater weight at low precipitation levels, with the increase of precipitation level, the weight of SMS-OTS gradually increases. The average absolute error of the consensus forecast is slightly smaller than EC-OTS and significantly smaller than SMS-OTS with all lead times. The consensus forecast has higher threat scores than EC-OTS and SMS-OTS with almost all lead times at all precipitation levels. The threat score of the 12 h accumulated precipitation of the consensus forecast is -0.009 to 0.041 higher than the subjective forecast of local forecasters, and the threat score of 24 h accumulated precipitation forecast is 0.009 to 0.023 higher than the subjective forecast of China National Meteorological Center forecasters. |
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