VERIFICATION AND EVALUATION OF TYPHOON PRECIPITATION FORECAST BY SHENZHEN STORM-SCALE ENSEMBLE FORECAST SYSTEM
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摘要: 目前数值模式对台风降水预报的准确率仍有待提高。为了评估深圳对流尺度集合预报系统对台风降水预报能力，选取了2015—2018年共14个影响广东台风个例，利用广东省2 300多个自动气象观测站的24小时累计降水观测资料，检验该系统的集合预报方法(含集合平均方法和概率匹配平均方法)和控制预报方法的24小时降水预报结果。(1)系统对台风24小时降水预报具有较好参考价值，三种方法的暴雨等级预报TS评分均达到0.39以上。(2)集合预报方法总体上优于控制预报方法，可改善珠江口两侧暴雨中心降水预报。其中集合平均方法总体预报效果最好，其降水预报均方根误差为38.1 mm，比控制预报方法减少18.8%，对暴雨等级预报TS评分为0.469比控制，预报方法提升20.1%，但是对特大暴雨等级预报能力不足；而概率匹配平均方法改善了小雨和特大暴雨的预报能力。(3)系统对较强台风的降水预报能力优于弱台风。在较强台风情形下，系统对粤东暴雨中心降水预报明显偏小且控制预报方法偏差最大，其他地方降水预报偏大为主；在弱台风情形下，系统对降水预报存在明显系统性偏大，但对粤西暴雨中心降水预报明显偏小且控制预报偏差最大。Abstract: At present, the accuracy of typhoon precipitation prediction by numerical models still needs to be improved. To evaluate the ability of typhoon precipitation forecast by the high resolution (4km) storm-scale ensemble forecast system in Shenzhen, 14 typhoons that affected Guangdong Province from 2015 to 2018 were selected. Based on the 24-hour accumulative precipitation observational data from more than 2300 automatic observation stations in Guangdong, the 24-hour precipitation forecast results of the system by using ensemble forecast method, including ensemble average method and probability matching average method, and the control forecast method were verified. The main conclusions are as follows: (1) The system can provide a good reference for typhoon 24-hour precipitation forecast, and the TS scores of rainstorm grade forecast by using the three methods are all above 0.39. (2) The ensemble forecast method is better than the control forecast method as the former can improve the rainfall forecast of the rainstorm center on both sides of the Pearl River estuary. Besides, the ensemble average method has the best overall prediction. Its root mean square error of precipitation prediction is 38.1mm, which is 18.8% less than that of the control method; the TS score of rainstorm grade prediction is 0.469, which is 20.1% higher than that of the control method. Its ability to predict extraordinary rainstorm grade is insufficient and the probability matching mean method improves its ability to predict light rain and extraordinary rainstorm. (3) The precipitation forecast ability of the system for strong typhoons is better than that for weak typhoons. In the cases of strong typhoons, there is an obvious negative deviation between the precipitation forecast for and observations in the rainstorm center in the east of Guangdong, and the deviation of the control method is the largest; in other places, the deviation is mainly positive. In the cases of weak typhoons, the precipitation forecast by the system has an obvious systematic positive deviation, but there is an obvious negative deviation for the rainstorm center in the west of Guangdong, and the deviation of the control method is the largest.
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Key words:
- typhoons /
- precipitation /
- ensemble forecast /
- storm-scale /
- verification
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图 6 2015—2018年共14个台风根据登陆中国华南沿海地区时强度进行分类
a.较强台风；b.弱台风。台风路径中临近两点的时间间隔为3小时或者6小时，具体间隔由台风最佳路径数据集决定；路径上的虚线表示选取的数值预报个例起始时间到结束时间(24小时)对应的台风路径。
表 1 深圳对流尺度集合预报系统外层d01不同成员配置
成员 微物理 短波辐射 边界层 陆面 积云对流 初始和边界条件 M00 WSM6 Goddard MYJ Noah KF ECMWF M01 WSM6 Dudhia YSU Noah BMJ GEFS p01 M02 Thompson Dudhia ACM2 RUC KF GEFS p02 M03 WSM5 Dudhia MYJ RUC GD-3d GEFS p03 M04 WSM6 Dudhia MYNN Noah KF GEFS p04 M05 Ferrier Goddard ACM2 Noah KF GEFS p05 M06 Thompson New Goddard MYJ RUC BMJ GEFS p06 M07 Thompson Goddard YSU Noah GD-3d GEFS p07 M08 WSM6 New Goddard MYNN Noah BMJ GEFS p08 M09 WSM 5 Goddard ACM2 Noah BMJ GEFS p09 M10 Ferrier New Goddard MYJ RUC GD-3d GEFS p10 
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表 2 2015—2018年影响广东的14个台风个例
序号 编号 台风名称 登陆强度 登陆地区 数值预报个例选择(北京时) 1 1510 莲花 台风 汕尾 2015070820 2 1604 妮妲 强台风 深圳 2016080120 3 1608 电母 热带风暴 湛江 2016081720 4 1621 莎莉嘉 强台风 万宁 2016101808 5 1622 海马 强台风 汕尾 2016102020 6 1702 苗柏 热带风暴 深圳 2017061208 7 1707 洛克 热带风暴 香港 2017072220 8 1713 天鸽 强台风 珠海 2017082220 9 1714 帕卡 台风 江门 2017082620 10 1716 玛娃 热带风暴 汕尾 2017090308 11 1804 艾云尼 热带风暴 湛江 2018060708 12 1809 山神 热带风暴 万宁 2018071720 13 1816 贝碧嘉 强热带风暴 琼海 2018081020 14 1822 山竹 台风 江门 2018091520
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表 3 平均误差和均方根误差
单位：mm。 样本 产品 平均误差 均方根误差 14个个例 PM 8.3 44.9 Mean 6.4 38.1 M00 3.6 46.9 较强台风 PM 1.1 36.7 Mean 0.9 34.7 M00 1.0 44.0 弱台风 PM 15.4 51.6 Mean 11.7 41.2 M00 6.1 49.5
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