Wen Huayang, Tian Hong, Tang Weian, et al. Establishment of meteorological model for estimating standard ice thickness in Anhui Province. J Appl Meteor Sci, 2011, 22(6): 747-752.
Citation: Wen Huayang, Tian Hong, Tang Weian, et al. Establishment of meteorological model for estimating standard ice thickness in Anhui Province. J Appl Meteor Sci, 2011, 22(6): 747-752.

Establishment of Meteorological Model for Estimating Standard Ice Thickness in Anhui Province

  • Received Date: 2011-11-10
  • Rev Recd Date: 2011-09-12
  • Publish Date: 2011-12-31
  • An extreme frozen ice and snow disaster brings severe losses in Anhui Province during early 2008. Some overhead transmission lines are destroyed seriously during this event. It is important to investigate the characteristics of ice accumulation and develop appropriate models to estimate the disaster, but the research doesn't go smoothly due to the lack of data. If a quantitative relationship can be built between the meteorological elements and the ice thickness by the wire icing observation data of fewer stations, the ice-covered mechanism can be understood better, which can also lay the foundation for disaster risk regionalization.Based on the long-term wire icing observation data and climate data of 16 meteorological stations in recent 50 years, several models are built by stepwise multiple linear regression and artificial neural networks, to calculate standard ice thickness of wire icing in Anhui Province.Through coordinated experiment, the optimal models built by stepwise multiple linear regression are confirmed which handle temperature, humidity, wind speed of the icing day, one day before and two days before. The models by artificial neural network perform better in simulating comparatively, but in the prediction they are very unstable, and less effective. The model driven by 26 meteorological factors (data from 1987 to 2008) perform better than the model driven by 24 factors (data of from 1960 to 2008). The best models are picked out by the results of simulating and predicting. And the absolute deviation is 1.1 mm to the north of the Huaihe River, 1.3 mm to the south of the Huaihe River, 6.6 mm in the area of high mountains, and the relative deviation rates of the three models are about 60%—70%. The result is better than other references. For the mechanism of icing, temperature, humidity and wind are key factors, among which temperature is the most influential one. The ice thickness of the plains and hilly areas are mainly affected by weather conditions of the day, while that of mountain areas are also affected by the weather conditions of a few days before. Finally, the ice-wire thickness at the meteorological stations without wire icing observation is calculated by the optimal model, which proves this method feasible.
  • Table  1  Error analysis of the models to the north of the Huaihe River

    方法 因子 误差类型 估计偏差/mm 相对估计偏差/% 样本量 入选因子
    逐步多元线性回归 26个 拟合
    预测
    0.8
    1.1
    62.7
    61.5
    199
    66
    Tave0Tmin0Tg
    24个 拟合
    预测
    1.4
    1.5
    66.2
    83.9
    414
    112
    Tave0Tmin0Tmax0U0
    人工神经网络 26个 拟合
    预测
    0.6
    1.6
    47.0
    93.7
    199
    66
    Tave0Tmin0Tmax0E0Tave1Tmin1
    Tmax1TgTave2Tmin2Tmax2Fg
    24个 拟合
    预测
    0.8
    1.8
    37.1
    102.3
    414
    112
    Tave0Tmin0Tmax0E0Tave1Tmin1Tmax1
    E1Tave2Tmin2Tmax2E2U0F0S1
    DownLoad: Download CSV

    Table  2  Error Analysis of the models to the south of the Huaihe River

    方法 因子 类型 估计偏差/mm 相对估计偏差/% 样本量 入选因子
    逐步多元线性回归 26个 拟合
    预测
    0.8
    1.3
    66.1
    79.9
    52
    17
    Tave0
    24个 拟合
    预测
    83
    29
    人工神经网络 26个 拟合
    预测
    0.1
    1.4
    7.1
    87.4
    52
    17
    Tave0Tmin0Tmax0E0Tave1
    Tmin1Tmax1F0Tave2Tmax2
    24个 拟合
    预测
    0.8
    2.1
    57.1
    94.1
    83
    29
    Tave0Tmin0Tmax0E0U0Tave1Tmin1
    Tmax1E1S1Tave2Tmin2Tmax2E2F0
    DownLoad: Download CSV

    Table  3  Error analysis of the models in the area of high mountains

    方法 因子 类型 估计偏差/mm 相对估计偏差/% 样本量 入选因子
    逐步多元线性回归 26个 拟合
    预测
    6.6
    6.8
    61.4
    67.3
    329
    168
    U0E0TgU1F1
    24个 拟合
    预测
    9.0
    7.9
    67.2
    84.1
    421
    402
    Tmin0U0Tave1U1F1E1E2R2
    人工神经网络 26个 拟合
    预测
    3.1
    9.6
    28.5
    94.9
    329
    168
    Tave0Tmin0Tmax0U0E0TgFg
    R2U2E2Tave1Tmin1U1S1
    24个 拟合
    预测
    4.8
    9.2
    35.8
    97.9
    421
    402
    Tave0Tmin0Tmax0E0Tave1Tmin1E1
    F1Tave2Tmin2E2R2U0U1R1
    DownLoad: Download CSV
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    • Received : 2011-11-10
    • Accepted : 2011-09-12
    • Published : 2011-12-31

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