| 基于ECMWF集合预报产品的降水相态客观预报方法 |
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| 引用本文： | 董全,张峰,宗志平.基于ECMWF集合预报产品的降水相态客观预报方法[J].应用气象学报,2020,31(5):527-542. |
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| 作者姓名： | 董全 张峰 宗志平 |
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| 作者单位： | 1.国家气象中心, 北京 100081 |
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| 摘 要： | 基于欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts，ECMWF）集合预报系统的降水相态产品（precipitation type，PTYPE），分别以HSS评分最优、TS评分最优和频率偏差最优为标准，运用最优概率阈值法，生成雨、雨夹雪、雪和冻雨4类降水相态的确定性预报产品，并与ECMWF集合预报系统控制成员及细网格模式确定性预报进行对比。最优概率阈值显示:3种最优标准下，不同相态降水最优概率阈值不同，但冻雨和降雪最优概率阈值均最大，为40%~80%，雨夹雪最优概率阈值最小，约为10%，三者最优概率阈值均随预报时效延长而减小；降雨最优概率阈值为7%~25%，随预报时效延长而增大。对比检验结果显示:最优概率阈值法明显提高了降水相态预报能力，且以HSS评分最优时预报效果最佳；最优概率阈值法有效减小冻雨空报，同时显著改善降雨和降雪预报的频率偏差和TS评分，对雨夹雪预报改进效果有限。
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| 关 键 词： | 集合预报系统 降水相态 最优概率阈值法 |
| 收稿时间： | 2020-06-01 |
Objective Precipitation Type Forecast Based on ECMWF Ensemble Prediction Product |
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| Affiliation: | 1.National Meteorological Center, Beijing 1000812.Dalian Meteorological Bureau, Dalian 116001 |
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| Abstract: | Ensemble prediction system usually improves forecast skill compared to deterministic model under the same model system. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system precipitation type product (PTYPE) is used with the approach of optimal probability threshold (OPT) to produce precipitation type deterministic forecast. The history dataset of winter half year (October to the next March) from 2016-2017 of ECMWF ensemble prediction system and observations of 2515 surface weather stations are used firstly to estimate optimal probability thresholds for different lead times of rain, sleet, snow and freezing rain under criteria of Optimal HSS (OHSS), optimal TS (OTS) and Optimal forecast bias (OB). Then deterministic precipitation type forecast is calculated from the probabilistic forecast of ensemble prediction system, verified by data of 2018 winter half year and compared with the precipitation type forecasts of ECMWF high-resolution deterministic model (HRD) and ensemble prediction system control forecast (CF). It indicates that optimal probability thresholds under three criteria are different. However, optimal thresholds of snow and freezing rain are the largest which are between 80% and 40%, and optimal thresholds of sleet are the smallest which are under 10%. They all decrease with elongating lead times. Optimal thresholds of rain are small which are between 7% and 25%, increasing with elongating lead times. Verification results show that performances of CF are the lowest with the proportion correct between 92% and 91% and HSS between 0.74 and 0.55. The performance of HRD is better than CF with the proportion correct about 93% and HSS between 0.77 and 0.65. OPT based on ensemble prediction system probabilistic forecast improves the forecast skill of precipitation type significantly. The improvement of OHSS is the most significant with the proportion correct about 94% and HSS from 0.81 to 0.68. From the verification of every kind of precipitation types and case analysis, it demonstrates that the performance of HRD and CF for sleet is similar. However, for the other three precipitation types, the performance of HRD is better than that of CF and the performance of OPT is the best. For freezing rain, the forecast bias of CF and HRD is larger than 2 which means too more false alarm. OPT reduces the forecast area and false alarm of freezing rain and improves the performance. HRD and CF forecast less rains and more snows with poor forecast biases. OPT corrects these errors and makes better forecast bias and TS scores for rain and snow. For sleet, the forecast bias of OPT is less than 1 significantly and the TS score is nearly zero which is worse than HRD and CF. |
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