Hong Wei, Zheng Yulan. A method of short-time strong rainfall forecasting during pre-rainy season in Fujian based on ECMWF productions. J Appl Meteor Sci, 2018, 29(5): 584-595. DOI:  10.11898/1001-7313.20180507.
Citation: Hong Wei, Zheng Yulan. A method of short-time strong rainfall forecasting during pre-rainy season in Fujian based on ECMWF productions. J Appl Meteor Sci, 2018, 29(5): 584-595. DOI:  10.11898/1001-7313.20180507.

A Method of Short-time Strong Rainfall Forecasting During Pre-rainy Season in Fujian Based on ECMWF Productions

DOI: 10.11898/1001-7313.20180507
  • Received Date: 2018-01-16
  • Rev Recd Date: 2018-05-18
  • Publish Date: 2018-09-30
  • 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.
  • 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)

    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)

    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)

    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

    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

    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

    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
    DownLoad: Download CSV

    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
    DownLoad: Download CSV

    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
    DownLoad: Download CSV

    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
    DownLoad: Download CSV
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    • Received : 2018-01-16
    • Accepted : 2018-05-18
    • Published : 2018-09-30

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