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The Influence of Ensemble Size on Precipitation Forecast in a Convective Scale Ensemble Forecast System
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摘要: 选取2022年川渝地区发生的16个强降水个例开展对流尺度集合预报批量试验，并通过对31组初值采用不同集合成员数时的降水集合预报技巧进行检验评估和综合分析。结果表明：集合成员的降水预报技巧总体上大致相当，因而采用不同成员数时预报技巧差异也不明显；表征降水总体分布特征的Talagrand分布和预报失误概率以及表征降水概率预报技巧的相对作用特征面积随着成员数的增加而逐渐改进，但当成员数达到一定数值后继续增大成员数对预报改进不明显。总体而言，对流尺度集合预报成员数设置为16~18最适宜。Abstract: In order to provide more powerful support for forecasters in Sichuan and Chongqing with complicated terrain to carry out short-term (0-12 h) precipitation forecast, a convective scale ensemble forecast operational system is designed based on ensemble Kalman filter data assimilation method (31 ensemble samples) and WRF model with 3 km resolution (model domain: 24.5°-34.5°N, 99°-113°E) and lead time of 12 h, which is started by 3 h cycle. It is urgent to decide how many members should be used for the 12 h ensemble forecast to achieve the most representative probability distribution and optimal ensemble forecast skills. An ensemble forecast experiment is carried out on 16 heavy convective scale precipitation cases occurred in Sichuan and Chongqing with different amount of ensemble members, and the results are analyzed comprehensively. It is concluded that the precipitation forecast skills of the ensemble members for different magnitude of precipitation are roughly the same, so there is little difference in the totally averaged prediction skills of different ensemble size. Talagrand distribution becomes better with the increase of ensemble size first. However, when the ensemble size is larger than 17, the improvement by increasing ensemble size is no longer significant. Meanwhile, the forecast error probability becomes smaller with the increase of ensemble size first, but when the ensemble size reaches 16 to 18, the difference between the forecast error probability and the ideal value tends to be stable, indicating that the improvement by further increasing the ensemble size is no longer significant. The relative area of operational characteristic (AROC) score which represents the prediction probability forecast skills, improves gradually with the increase of ensemble size. However, when the ensemble size is large enough, the improvement by lager ensemble size is no longer significant and the AROC scores tend to be stable. The ensemble size required for stable AROC score increases with the magnitude of precipitation. Overall, when the AROC scores become stable, the ensemble size required for light rain, moderate rain, heavy rain, rainstorm (and heavy rainstorm) are 10, 14, 16 and 18, respectively. Based on the comparative analysis results and considering that there is generally little difference in forecasting skills when the number of members is different by 2, in order to achieve the most representative probability distribution and optimal ensemble forecast skills of precipitation, it is recommended to set the ensemble size of convective scale ensemble prediction system from 16 to 18.
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Key words:
- ensemble forecast;
- convective scale;
- probability forecast;
- ensemble member
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图 1 对流尺度集合预报系统中同化的雷达资料站点(红点) 分布和影响范围(蓝色圆圈)
Fig. 1 Location (the red dot) and influence range (the blue circle) of the assimilated radars in the convective scale ensemble prediction system
图 2 集合成员逐3 h累积降水量的预报技巧综合图
Fig. 2 Performance diagrams of the ensemble members for each 3 h accumulated precipitation forecast
图 3 采用不同成员数对应的逐3 h累积降水量的平均TS评分
Fig. 3 Averaged threat scores for each 3 h accumulated precipitation forecast using different ensemble size
图 4 采用不同成员数对应的0~3 h累积降水量Talagrand分布
Fig. 4 Talagrand distribution of 0-3 h accumulated precipitation forecast using different ensemble size
图 6 采用不同成员数对应的逐3 h累积降水量预报失误概率
Fig. 6 Forecast error probability for each 3 h accumulated precipitation forecast using different ensemble size
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