Liu Xiaolu, Liu Jianxi, Zhang Shilin, et al. Hail forecast based on factor combination analysis method and sounding data. J Appl Meteor Sci, 2014, 25(2): 168-175.
Citation: Liu Xiaolu, Liu Jianxi, Zhang Shilin, et al. Hail forecast based on factor combination analysis method and sounding data. J Appl Meteor Sci, 2014, 25(2): 168-175.

Hail Forecast Based on Factor Combination Analysis Method and Sounding Data

  • Received Date: 2013-05-07
  • Rev Recd Date: 2013-11-20
  • Publish Date: 2014-03-31
  • The emergence of the small probability of severe weather events is attributed to specific factor combinations of some early meteorological elements. The nonlinear and complicated characteristics of factor combinations can be used to find the relationship between forecast object and forecast factors. Based on this method, the relationship between hail events in the south of Sichuan Basin and some meteorological elements calculated by the sounding data is investigated, and a hail forecast index discriminant is established. The discriminant is physically significant and applied in daily operation.Hailstorm is a meso-scale weather system with the temporal scale of several to dozens of hours, and the horizontal scale of several hundred kilometers. In real business, the T-lnp sounding data are observed at 0800 BT and 2000 BT every day, and the hail forecasting is carried out every 12 hours. A sample sets of 7 hail events and 38 non-hail events near Yibin Station is established. Using the T-lnp sounding data, 3422 meteorological elements are calculated as forecast factors, including temperature, height, moisture, saturation vapor pressure, potential pseudo-equivalent temperature, K index and so on. Based on factors combination analysis method, 2 main factors and 2 conditional factors are selected from 3422 meteorological elements and their critical values are calculated. The main factors are Tσ400*-Tσ850 and Gz400-Gzsurface, and the conditional factors are e700-es700 and Td700-Tσ700*, Tσ400* stands for saturated wet static temperature at 400 hPa, and Tσ850 stands for wet static temperature at 850 hPa; Gz400 and Gzsurface stand for vertical pressure gradient at 400 hPa and the surface level; e700 and es700 stand for vapour pressure and saturated vapour pressure at 700 hPa; Td700 and Tσ700* stand for dew point temperature and saturated wet static temperature at 700 hPa. The hail forecast indexes discriminant nearby Yibin Station is established using these data.The environmental state of hailstorm generation and the unstable mechanism of severe convective weather can be explained by the hail forecast indexes discriminant, which is evaluated using historical records of the year of 2008. Among 65 warnings, the real hail events never miss but the false alarm ratio reaches 67.7%, which should be further distinguished using radar observations. The overall probability of detection is 84%, and the critical success index is 30.4%. The result shows that factor combination analysis method is feasible to some extent.
  • Table  1  Hail event samples

    序号 探空资料时间 降雹时间 降雹地点 降雹地与探空站距离/km 降雹地海拔/m 冰雹直径/mm
    1 1983-04-10T20:00 22:47 宜宾 0 341 5
    2 1985-08-06T08:00 17:57 荣县 72 382 20
    3 1994-07-18T08:00 16:45 隆昌 88 374 9
    4 1994-07-27T08:00 15:12 井研 106 419 8
    5 1998-04-28T08:00 18:42 珙县 48 368 14
    6 2000-07-20T08:00 17:18 井研 106 419 20
    7 2002-04-04T08:00 18:50 自贡 61 357 8
    DownLoad: Download CSV

    Table  2  Sequence Y and sequence of top 10 major factors

    样本YX1X2X3X4X5X6X7X8X9X10
    111111111111
    211111111111
    311111111111
    411111111111
    511111111111
    611111111111
    711111111111
    801011111111
    901111111111
    1000000000000
    1100000000000
    1200000000000
    1300000000000
    1400000000000
    1500000000000
    1601111111111
    1700011001111
    1800000000000
    1900000000000
    2000000000000
    2100000000000
    2200000000000
    2300000000000
    2400000000000
    2500001001111
    2601111111111
    2701100110000
    2800000000000
    2900000000000
    3000000000000
    3100000000000
    3200000000000
    3300000000000
    3400000000000
    3500111111111
    3600111111111
    3700000001111
    3800000000000
    3900000000000
    4000010110000
    4101011111111
    4201111111111
    4300101111111
    4400110000000
    4500101111111
    DownLoad: Download CSV

    Table  3  Sequence of major factor combination and its top 8 condition factors

    样本YV1∧2YYX1X2X3X4X5X6X7X8
    1110111111110
    2110110000000
    3110111111111
    4110111111100
    5110111111111
    6110111100001
    7110101111111
    8000100101010
    9011000000000
    10000111111110
    11000111111111
    12000111111111
    13000111111111
    14000111111111
    15000111111111
    16011000000000
    17000111111101
    18000111101110
    19000111111110
    20000111111111
    21000111111110
    22000111111110
    23000111111111
    24000111111111
    25000100001011
    26011000000000
    27011000000000
    28000111111111
    29000111101101
    30000111111111
    31000111111111
    32000101111111
    33000100011110
    34000100000001
    35000111111101
    36000111100101
    37000111111101
    38000111111111
    39000100000101
    40000100011111
    41000100000000
    42011000000000
    43000111111101
    44000111111110
    45000101110100
    DownLoad: Download CSV
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    • Received : 2013-05-07
    • Accepted : 2013-11-20
    • Published : 2014-03-31

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