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Objective Precipitation Type Forecast Based on ECMWF Ensemble Prediction Product
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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。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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图 1 不同最优标准、不同相态、不同时效预报最优概率阈值
(以OHSS为标准时无雨夹雪阈值)
Fig. 1 Estimated optimal probability thresholds under criteria of OTS, OB, OHSS as a function of lead times for different initial times
(no sleet threshold under criteria of OHSS)
图 2 2018年冬半年全国降水相态、不同时效预报的HSS评分和正确率
Fig. 2 HSS and proportion correct of precipitation type forecast at different lead times for 2018 winter half year
图 3 2018年冬半年全国降水相态不同时效预报TS评分
Fig. 3 TS scores of precipitation type forecast at different lead times for 2018 winter half year
图 4 2018年冬半年全国降水相态不同时效预报频率偏差
Fig. 4 Forecast bias of precipitation type at different lead times for 2018 winter half year
图 5 2018年12月7日20：00起报30 h时效降水相态确定性预报(填色)与实况(离散点)对比
(a)OPTH，(b)HRD，(c)CF
Fig. 5 Precipitation type deterministic forecasts at lead time of 30 h from 2000 BT 7 Dec 2018(the shaded) and corresponding observations(the scattered)
(a)OPTH, (b)HRD, (c)CF
图 6 2018年12月7日20：00起报30 h时效ECMWF集合预报系统PTYPE概率预报(填色)和实况(离散点)对比
(a)雨，(b)雨夹雪，(c)雪，(d)冻雨
Fig. 6 ECMWF ensemble prediction system precipitation type probabilistic forecasts at lead time of 30 h from 2000 BT 7 Dec 2018(the shaded) and corresponding observations(the scattered)
(a)rain, (b)sleet, (c)snow, (d)freezing rain
图 7 2018年12月3日20:00起报126 h时效降水相态确定性预报(填色)与实况(离散点)对比
(a)OPTH，(b)HRD，(c)CF
Fig. 7 Precipitation type deterministic forecasts at lead time of 126 h from 2000 BT 3 Dec 2018(the shaded) and the corresponding observations(the scattered)
(a)OPTH, (b)HRD, (c)CF
图 8 2019年1月8日08:00起报24 h时效降水相态确定性预报(填色)与实况(离散点)对比
(a)OPTH，(b)HRD，(c)CF
Fig. 8 Precipitation type forecasts at lead time of 24 h from 0800 BT 8 Jan 2019(the shaded) and corresponding observations(the scattered)
(a)OPTH, (b)HRD, (c)CF
图 9 2019年1月4日08:00起报120 h时效降水相态确定性预报(填色)与实况(离散点)对比
(a)OPTH，(b)HRD，(c)CF
Fig. 9 Precipitation type forecasts at lead time of 120 h from 0800 BT 4 Jan 2019(the shaded) and corresponding observations(the scattered)
(a)OPTH, (b)HRD, (c)CF
表 1 雨、雨夹雪、雪和冻雨预报检验的4分类列联表
Table 1 The contingency table for four categories of rain, sleet, snow and freezing rain
项目 相态 观测 雨 雨夹雪 雪 冻雨 预报 雨 a e r g 雨夹雪 h b u v 雪 z l c m 冻雨 n s q d 
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