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A Probabilistic Forecast Experiment of Short-duration Heavy Rainfall in Beijing Based on CMA-BJ
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摘要: 基于2019—2021年4—9月北京快速更新数值预报系统(CMA-BJ)产品以及北京地区地面气象站逐时降水实况, 从表征水汽条件、热力和能量条件以及动力条件的多个物理量中筛选出在有无降水、是否强降水情形中有显著差异的物理量作为因子, 采用配料法和模糊逻辑算法构建北京地区0~12 h时效逐小时短时强降水概率预报模型。以2019—2021年4—9月最优TS评分和偏差评分的概率值和组合反射率因子为确定性预报的概率阈值和消空处理阈值, 运用该预报模型对2022年4—9月每日4次0~12 h预报时效北京地区短时强降水产品进行预报和检验。结果表明:北京地区短时强降水TS评分和偏差评分分别为0.104和1.341, 预报效果明显优于CMA-BJ预报产品。概率预报模型能够有效提升强降水高发地区, 即山前及平原地区的短时强降水预报技巧, 获得较为平衡的命中率和空报率, 但对山区预报技巧的提升有限。Abstract: Based on numerical prediction products from China Meteorological Administration Beijing model (CMA-BJ), precipitation observation of ground weather stations in Beijing and ECMWF ERA5 dataset, the hourly rainfall samples from April to September during 2019-2021 are divided into short-duration heavy rainfall (SDHR, greater than 20 mm·h-1), ordinary rainfall (between 0.1 and 19.9 mm·h-1) and no rainfall (less than 0.1 mm·h-1). The probability density distribution characteristics of physical parameters are comparatively analyzed, including moisture conditions, thermal and energy conditions, and dynamic conditions for three categories. Monthly predictors are selected from these parameters by comparing their ability to discriminate among SDHR, ordinary rainfall and no rainfall weather. It is found that the distributions and thresholds of physical parameters differ between months to some extent. Among that, moisture conditions, thermal and energy conditions, and dynamic conditions are relatively stronger but with less discrimination degrees among SDHR, ordinary rainfall and no rainfall weather in July and August. The background circulation and the distributions of physical parameters show obvious monthly differences, so the forecast model is established for each different period. After that, forecast model of SDHR for 0-12 h at 1 h intervals in different periods is established by using the ingredients-based method and fuzzy logic algorithms. When probabilistic and composite reflectivity thresholds are 0.6 dBZ and 15 dBZ, threat score (TS) and bias of SDHR are 0.14 and 1.14 in Beijing from April to September during 2019-2021, showing relatively better forecasts. Therefore, the probabilistic and composite reflectivity thresholds corresponding to the optimal TS and bias for 2019-2021 are taken as the forecast probability and eliminating false thresholds of SDHR, and 0-12 h hourly forecast products of SDHR four times a day are tested from April to September of 2022. Results show that TS and bias of SDHR are 0.104 and 1.341, respectively, indicating that the probability prediction products are better than that of CMA-BJ. SDHR products achieve greater improvement, and balance hit rate and false alarm rate well in the piedmont and plain areas with high SDHR frequency. But performances in mountainous areas are not as good as that in plain areas, which may be related to less stations in the mountainous areas of the forecast model. In addition, the result based on case analysis show the predicted area of the products is relatively larger, but high probability area has a good indication for SDHR in Beijing.
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Key words:
- short-duration heavy rainfall;
- CMA-BJ;
- physical parameters;
- forecast model
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图 1 北京地区193个地面气象观测站的分布(填色为地形高度)
Fig. 1 Spatial distribution of 193 weather stations in Beijing (the shaded denotes elevation)
图 2 2019—2022年4—9月北京地区短时强降水发生频次的空间分布(a)和月发生频次占总发生频次比例(b)
Fig. 2 Distribution of frequency(a) and ratio of monthly frequency to total frequency(b) of short-duration heavy rainfall in Beijing from Apr to Sep during 2019-2022
图 3 1991—2020年4—9月气候平均逐月500 hPa位势高度(黑线，单位：dagpm)、850 hPa风场(风羽)、850 hPa假相当位温(红线，单位：K) 和850 hPa相对湿度(填色)
Fig. 3 Monthly averaged 500 hPa geopotential height (the black line, unit:dagpm), 850 hPa wind (the barb), 850 hPa pseudo-equivalent potential temperature (the red line, unit:K), and 850 hPa relative humidity (the shaded) during 1991-2020
图 4 2019—2021年4—9月北京地区短时强降水(红色)、普通降水(蓝色) 和无降水(黑色) 天气样本各物理参量的箱线图
Fig. 4 Box plots of physical parameters of short-duration heavy rainfall (the red), ordinary rainfall (the blue), and no rainfall (the black) in Beijing from Apr to Sep during 2019-2021
图 5 2019—2021年4—9月概率预报模型不同概率阈值对应的短时强降水预报TS评分和偏差评分
Fig. 5 Threat score and bias with respect to different probability thresholds of short-duration heavy rainfall for probabilistic model forecast from Apr to Sep during 2019-2021
图 6 2022年6—9月概率预报模型的短时强降水预报TS评分和偏差评分
Fig. 6 Threat score and bias of short-duration heavy rainfall for probabilistic model forecast from Jun to Sep in 2022
图 7 2022年4—9月北京地区概率预报模型和CMA-BJ的短时强降水预报TS评分和偏差评分
Fig. 7 Threat score and bias of short-duration heavy rainfall for probabilistic model forecast and CMA-BJ model in Beijing from Apr to Sep in 2022
图 8 2022年8月4日17：00—20：00北京地区短时强降水实况(绿色点)、CMA-BJ (黑色等值线，单位：mm·h-1) 和概率预报模型预报(填色)
Fig. 8 Short-duration heavy rainfall observation (the green dot), forecasts for CMA-BJ (the black contour, unit:mm·h-1) and probabilistic model (the shaded) in Beijing from 1700 BT to 2000 BT on 4 Aug 2022
图 9 2022年8月4日19：00整层可降水量、K指数、强天气威胁指数和700 hPa经向风
Fig. 9 Precipitable water, KI, ISWEAT and V700 at 1900 BT 4 Aug 2022
表 1 短时强降水与普通降水(S1)和短时强降水与无降水(S2)天气的水汽条件、热力和能量条件以及动力条件的概率密度分布重叠区(单位：%)
Table 1 Overlappingsize (unit:%) of probability density distributions of moisture conditions, thermal and energy conditions, and dynamic conditions of short-duration heavy rainfall with ordinary rainfall(S1) and with no rainfall(S2) weather types
类别 物理参量 4月和5月 6月 7月 8月 9月 S1 S2 S1 S2 S1 S2 S1 S2 S1 S2 水汽条件 整层可降水量 47.6 22.8 72.1 39.8 81.7 52.7 78.7 42.3 59.3 27.7 925 hPa比湿 41.7 24.6 57.8 40.9 79.1 69.8 71.8 53.4 51.5 35.6 850 hPa比湿 41.6 22.0 49.0 36.7 76.8 64.8 73.0 48.4 53.6 31.3 700 hPa比湿 64.1 24.8 70.9 54.5 85.5 57.8 76.7 48.4 76.2 30.0 925 hPa相对湿度 61.7 38.2 72.1 49.0 89.1 69.6 85.7 57.3 79.2 43.3 850 hPa相对湿度 67.1 36.4 76.5 43.7 90.8 68.0 86.9 52.2 81.0 35.0 700 hPa相对湿度 53.9 36.4 63.8 61.1 88.4 56.5 89.0 46.9 59.9 36.0 925 hPa水汽通量 66.2 49.6 71.4 57.9 85.3 69.8 76.9 61.8 80.2 72.5 850 hPa水汽通量 67.7 47.2 72.6 57.2 83.9 69.6 79.3 64.4 76.2 64.9 700 hPa水汽通量 65.2 45.4 74.6 55.6 85.5 61.6 78.8 54.8 78.4 60.2 热力和能量条件 对流有效位能 48.6 44.4 43.7 41.3 78.0 82.8 74.2 76.9 66.6 70.5 对流抑制能量 57.5 48.3 65.9 56.8 82.5 82.1 79.2 80.5 75.2 80.1 最优抬升指数 38.9 34.1 37.9 33.7 76.0 82.4 65.4 60.3 55.7 65.6 沙氏指数 38.3 32.3 41.8 36.2 74.7 79.8 67.1 57.3 55.9 58.8 总指数 48.1 50.7 46.3 46.1 81.6 82.4 72.1 77.5 62.3 71.1 K指数 36.9 21.0 67.8 41.2 79.8 56.1 68.1 34.5 63.4 32.8 强天气威胁指数 36.5 28.0 43.6 33.2 75.1 62.9 74.1 45.1 57.2 37.2 850 hPa和500 hPa假相当位温差 40.0 37.5 43.6 49.5 79.2 80.8 69.6 87.5 60.1 71.4 850 hPa和500 hPa温差 49.3 62.4 48.3 66.4 80.1 73.3 77.1 77.5 61.8 49.2 动力条件 925 hPa散度 72.1 69.9 74.8 67.6 87.2 74.3 79.3 67.4 81.2 80.1 850 hPa散度 74.5 67.0 74.5 67.5 87.4 75.6 81.1 71.2 78.5 77.1 300 hPa散度 68.4 68.3 63.1 55.3 83.3 66.1 76.9 63.2 78.4 75.7 850 hPa经向风 56.6 60.5 64.0 60.7 85.2 71.4 82.8 57.7 67.6 66.0 700 hPa经向风 63.5 42.0 72.1 56.7 86.7 57.4 80.1 50.5 74.4 49.4 700 hPa垂直速度 68.4 67.7 68.4 58.4 82.0 63.7 73.7 58.7 86.5 80.7 0~1 km垂直风切变 72.6 72.6 67.1 63.1 84.1 69.9 81.5 63.2 76.6 68.8 0~3 km垂直风切变 60.5 57.0 78.2 70.0 87.4 71.9 82.8 61.9 73.0 72.4 0~6 km垂直风切变 68.2 63.4 64.0 57.8 80.2 72.8 86.3 81.6 74.8 76.0 3~6 km垂直风切变 69.4 71.8 61.6 57.8 89.0 86.2 86.5 84.3 70.4 79.2 
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