Yang Lu, Han Feng, Chen Mingxuan, et al. Thunderstorm gale identification method based on support vector machine. J Appl Meteor Sci, 2018, 29(6): 680-689. DOI:  10.11898/1001-7313.20180604.
Citation: Yang Lu, Han Feng, Chen Mingxuan, et al. Thunderstorm gale identification method based on support vector machine. J Appl Meteor Sci, 2018, 29(6): 680-689. DOI:  10.11898/1001-7313.20180604.

Thunderstorm Gale Identification Method Based on Support Vector Machine

DOI: 10.11898/1001-7313.20180604
  • Received Date: 2018-05-11
  • Rev Recd Date: 2018-08-22
  • Publish Date: 2018-11-30
  • A thunderstorm gale recognition model is established using support vector machine based on data of radar and automatic weather stations from Beijing Weather Observatory. Firstly, 18 thunderstorms in Beijing during 2010-2014 are analyzed quantitatively in terms of the statistical method and 9 forecast factors are selected, i.e., the height of the echo top, the maximum albedo, the height of the maximum reflectivity, the total vertical liquid water content, the time rate change of total vertical liquid water content, the total vertical liquid water content density, the height of the maximum reflectivity factor, the storm moving speed and the width of the velocity spectrum. 451 non-high wind samples and 425 high wind samples are selected by matching the time and place of automatic weather stations with the value of the quantitative index of the PUP storm monomer recognition product in all the cases. Secondly, the probability distribution of prediction factors in the wind and non-wind samples are calculated, and relationships with corresponding forecast factors are obtained, and then sample data are normalized by the obtained membership function. Finally, the kernel function and model parameters are established, and the thunderstorm gale recognition model is established using support vector machine. Two typical cases in Beijing are analyzed and tested, one caused by a line thunderstorm which happened on 7 July 2017, and the other caused by an isolated single-cell storm which happened on 19 May 2012. Results show that the identified wind range is consistent with reality, and the hit rate, the false alarm rate and the critical success index are 92.0%, 22.1%, 73.0% and 99.1%, 40.5%, 59.2%, respectively. It will help to improve the accuracy of thunderstorm gale warning and forecasting. However, sometimes multiple thunderstorm cells can be misjudged as one according to these forecast factors. In this case, it is necessary for forecasters to conduct manual intervention in combination with the overall radar base reflectivity and weather conditions, to reduce the misjudgment rate of gale. In the future, long time series radar data should be used to carry out "large sample census" research and an automatic thunderstorm identification system based on weather radar can be established.
  • Fig. 1  Flow chart of the thunderstorm gale identification model based on SVM

    Fig. 2  The function member of forecast factors

    Fig. 3  The position of gale and identified thunderstorm cells with gale on 7 Jul 2017

    Fig. 4  Base reflectivity(the shaded), the position of gale and identified thunderstorm cells at 1511 UTC(a) and 1523 UTC(b) on 7 Jul 2017

    Fig. 5  The position of gale and identified thunderstorm cells with gale on 19 May 2012

    Fig. 6  Base reflectivity(the shaded), the position of gale and identified thunderstorm cells at 1247 UTC(a) and 1253 UTC(b) on 19 May 2012

    Table  1  Comparision of forecast factors of misjudged samples with the maximum instantaneous wind speed at automatic weather stations

    预报因子 样本1 样本2 样本3 样本4 样本5 样本6 样本7
    回波顶高/km 8.6 6.4 6.4 7.9 6.7 7.9 11.1
    最大反射率因子/dBZ 52 50 58 51 54 52 62
    最大反射率因子所在高度/km 2.4 4.6 1.7 3.0 3.7 3.4 4.6
    最大反射率因子下降高度/km 2.9 2.1 4.4 3.1 0.9 1.2 1.7
    垂直积分液态水含量/(kg·m-2) 17 10 31 10 18 18 54
    垂直积分液态水含量随时间变率/(kg·m-2·(6 min)-1) 2 17 21 4 8 1 21
    垂直积分液态水含量密度/(g·m-3) 2.1 2.8 5.5 1.6 6.0 2.6 5.6
    风暴移动速度/(km·h-1) 5 6 7 5 6 7 4
    速度谱宽/(m·s-1) 0 0 0 0 14 7 13
    匹配站点风速/(m·s-1) 6.2 5.9 3.4 2.8 5.9 5.0 2.0
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    • Received : 2018-05-11
    • Accepted : 2018-08-22
    • Published : 2018-11-30

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