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A Method of Short-time Strong Rainfall Forecasting During Pre-rainy Season in Fujian Based on ECMWF Productions
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摘要: 利用2014—2016年福建省1605个自动气象站逐时降水资料和ECMWF全球模式细网格预报产品，分析福建省前汛期短时强降水发生背景下模式预报物理量的分布特征，并基于阈值判定的方法建立短时强降水预报模型。结果表明：福建省内陆县市前汛期短时强降水发生频次较高，沿海县市发生频次低，且日变化特征表现出双峰结构。箱型图差异指数（Ibd）在评估相关变量对于区分短时强降水发生与否的敏感程度有较好的作用，比湿、整层可降水量等水汽变量Ibd最为显著，K指数、对流有效位能等变量的Ibd仅次于水汽变量，说明模式预报变量对于预测短时强降水有较好的表征作用。针对短时强降水事件的物理量集合，采用剔除异常值后的最小值作为判定阈值，通过训练集分析结果客观订正对流有效位能和3 h降水量两个高Ibd变量的阈值，建立潜势预报模型。对于福建省西部的关键区，检验集白天时段12 h时间分辨率预报TS评分可达0.5，夜间时段约为0.3。对于福建省进行分区建模预报，检验集预报结果显示白天时段比夜间准确率高、内陆县市比沿海县市准确率高。Abstract: Distribution features of model variables accompanied with short-time strong rainfall events are investigated based on hourly precipitation data from 1605 automatic weather stations and ECMWF 0.125°×0.125° fine grid model products, and a method of short-time strong rainfall forecasting based on threshold determination is established. Results show that short-time strong rainfall occur more frequently in Fujian inland area and less in Fujian coastal area during pre-rainy season, and the diurnal variation exhibits double peaks with the notable one at 1700 BT and the inapparent one at 0500 BT. The box difference index is useful to check whether a variable could differentiate short-time strong rainfall events well. The box difference index of humidity variables like specific humidity at 925 hPa and total column water vapor are most prominent followed by K index and convective available potential energy (CAPE) which shows these variables have good performances in distinguishing short-time strong rainfall events. Some variables like temperature difference between 850 hPa and 500 hPa and temperature change in 24 h at 500 hPa perform poorly in differentiating short-time strong rainfall events.The minimum threshold method based on the minimum values of variables after eliminating outliers works well in judging short-time strong rainfall events, which could decrease vacancy forecast rate effectively through increasing missing forecast rate appropriately compared with adopting real minimum values as threshold. In the key area (25.9°-27.1°N, 116.4°-117.4°E), TS (threaten score) of validation set in 2016 with 12 h interval reaches 0.5 at daytime and 0.3 at nighttime just based on the minimum threshold method. Revising threshold of variables with high box difference index values could improve the accuracy with the nighttime TS of validation set in 2016 increasing from 0.3 to 0.34. TS of 2016 is relatively lower compared with that of 2014-2015, and the cause may be that short-time strong rainfalls happen much more frequently in 2016 which is a very strong El Niño year.To establish a potential forecast model of short-time strong rainfall during pre-rainy season, Fujian is divided into grids of 1°×1°, and minimum threshold method is applied in each grid followed by threshold revise of variables with high box difference index values. This model could analyze all kinds of variables comprehensively besides those considered by weather forecasters. TS with 12 h interval at daytime mainly ranges from 0.3 to 0.5 while TS at nighttime is relatively lower. TS in inland area is much better than coastal area both at daytime and nighttime mainly because short-time strong rainfall occurs more frequently in inland area than coastal area during pre-rainy season climatologically.
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图 1 福建省2009—2016年前汛期短时强降水的空间频次分布(a)和日变化(b)特征
(黑色矩形框代表关键区)
Fig. 1 The spatial distribution(a) and diurnal variation(b) of short-time strong rainfall events during pre-rainy season from 2009-2016 in Fujian
(black box denotes the key area)
图 2 2014—2015年前汛期夜间时段关键区内短时强降水发生和不发生时的K指数分布箱型图
(黑点代表异常值)
Fig. 2 The boxplot of K index with short-time strong rainfall happened or not at nighttime during pre-rainy season in 2014-2015
(the black point denotes the outlier)
图 3 2014—2015年前汛期白天时段关键区内是否发生短时强降水时ECMWF模式预报物理量的箱型图分布
(黑点代表异常值)
Fig. 3 The boxplot of ECMWF variables with short-time strong rainfall happened or not at daytime during pre-rainy season from 2014-2015
(the black point denotes the outlier)
图 4 基于Ibd阈值和变量阈值百分位数变化的2014—2015年前汛期关键区白天时段(a)和夜间时段(b)3 h时间分辨率预报TS评分
Fig. 4 TS at daytime(a) and nighttime(b) varied with Ibd threshold and variable percentile threshold with time interval of 3 h during pre-rainy season in 2014-2015
图 5 福建省前汛期短时强降水12 h时间分辨率预报TS评分
(a)2014—2015年白天时段，(b)2014—2015年夜间时段，(c)2016年白天时段，(d)2016年夜间时段
Fig. 5 TS of short-time strong rainfall prediction during pre-rainy season with time interval of 12 h
(a)daytime in 2014-2015, (b)nighttime in 2014-2015, (c)daytime in 2016, (d)nighttime in 2016
图 6 2016年5月9日夜间时段模型预报结果(a)和ECMWF模式12 h累积降水量预报(b)
Fig. 6 The forecast of short-time strong rainfall with method in this study(a) and 12 h precipitation from ECMWF(b) at nighttime on 9 May 2016
表 1 关键区内模式预报变量的Ibd
Table 1 Ibd of variables in the key area
物理量 Ibd 白天 夜间 整层可降水量 0.44 0.49 925 hPa比湿 0.44 0.43 3 h降水量 0.42 0.49 850 hPa比湿 0.42 0.43 925 hPa露点 0.41 0.40 500 hPa垂直速度 0.39 0.40 850 hPa露点 0.37 0.35 K指数 0.36 0.35 对流有效位能 0.33 0.42 海平面气压 0.30 0.32 700 hPa垂直速度 0.29 0.36 500 hPa位势高度 0.28 0.23 850 hPa散度 0.22 0.19 200 hPa散度 0.16 0.23 850 hPa与500 hPa温差 0.14 0.06 500 hPa 24 h变温 0.04 0.03 
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表 2 仅采用最小阈值法针对关键区短时强降水的预报准确性检验
Table 2 The forecast verificaiton of short-time strong rainfall in the key area only using minimum threshold method
时段 2014—2015年 2016年 N1 N2 N3 N4 TS评分 N1 N2 N3 N4 TS评分 白天时段(3 h时间分辨率) 95 25 189 419 0.307 36 31 121 176 0.191 夜间时段(3 h时间分辨率) 59 13 230 426 0.195 24 27 114 199 0.145 白天时段(12 h时间分辨率) 51 16 31 84 0.520 31 10 20 30 0.508 夜间时段(12 h时间分辨率) 31 9 60 82 0.310 17 10 29 35 0.303
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表 3 同表 2，但潜势预报方法中加入对流有效位能和3 h降水量阈值的客观订正
Table 3 The same as in Table 2, but revising the threshold of convective available potential energy and 3 h preicipitation on the basis of minimum threshold method
时段 2014—2015年 2016年 N1 N2 N3 N4 TS评分 N1 N2 N3 N4 TS评分 白天时段(3 h时间分辨率) 86 34 150 458 0.319 33 34 92 205 0.208 夜间时段(3 h时间分辨率) 50 22 101 555 0.289 14 37 45 268 0.146 白天时段(12 h时间分辨率) 48 19 25 90 0.522 30 11 18 32 0.508 夜间时段(12 h时间分辨率) 30 10 36 106 0.395 14 13 14 50 0.341
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表 4 同表 2，但为基于华东区域中尺度模式的关键区短时强降水2016年预报检验
Table 4 The same as in Table 2, but verification of Shanghai Typhoon Institute-WRF ADAS Real-time Modeling System in the key area with data of 2016
时段 2016年 N1 N2 N3 N4 TS评分 白天时段(3 h时间分辨率) 16 51 32 265 0.161 夜间时段(3 h时间分辨率) 16 35 27 286 0.205 白天时段(12 h时间分辨率) 20 21 10 40 0.392 夜间时段(12 h时间分辨率) 9 18 9 55 0.250
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