Vol.29, NO.3, 2018
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Article Navigation >  Journal of Applied Meteorological Science  > 2018  >  29(3): 321-332
Guo Dafeng, Duan Mingkeng, Xia Minhui, et al. The inconsistency of forecasting in operational numerical prediction products. J Appl Meteor Sci, 2018, 29(3): 321-332. DOI:  10.11898/1001-7313.20180306.
Citation: Guo Dafeng, Duan Mingkeng, Xia Minhui, et al. The inconsistency of forecasting in operational numerical prediction products. J Appl Meteor Sci, 2018, 29(3): 321-332. DOI:  10.11898/1001-7313.20180306.
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The Inconsistency of Forecasting in Operational Numerical Prediction Products

DOI: 10.11898/1001-7313.20180306
  • 1.

    Meteorological Center of Jiangxi Province, Nanchang 330046

  • 2.

    State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

  • 3.

    Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education, and International Joint Research Laboratory on Climate and Environment Change, and School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044

  • Received Date: 2017-10-17
  • Rev Recd Date: 2018-03-05
  • Publish Date: 2018-05-31
    Abstract
  • The inconsistency of the forecast reflects evolution characteristics with time of the prediction error of continuous multiple prediction at a fixed time in the future. In order to explore the inconsistency of forecasting products in operational numerical forecasting applications, 12 h precipitation and 2 m ground temperature, which are predicted by three numerical models GQEC, T639 and GQJP from November 2015 to October 2016 are analyzed. Quantitative calculation method of Jumpiness index is adopted by considering the sensitivity of the index to the target region.The inconsistency of the numerical model in three different regions is studied by means of statistical analysis and typical case study. Results show that for statistical average, the inconsistency of the numerical models increases with the extension of the forecast time. Long time prediction inconsistency is greater. Jumpiness index of precipitation and temperature is related to the magnitude of the change, and Jumpiness index of precipitation is larger than that of temperature. It also shows that results of two consecutive temperature forecast are more consistent. The temperature prediction ability of the model is better than that of precipitation forecast. The comparison of different numerical models shows that GQEC has obvious advantages in many aspects. Although Jumpiness index of GQJP is less than T639, its jumping frequency is greater, indicating its prediction consistency is inferior to T639. There are seasonal differences in jumping frequency of model products. Both the jump frequency of precipitation and temperature is the highest in summer and the lowest in winter. The inconsistency test of two typical cases of rainstorm and cold air cooling process further corroborates statistical analysis results. Results also show that the forecast inconsistency of the numerical model is not only related to the geographical position, but also to the selected area size. The larger the region is, the smaller Jumpiness index becomes, and vice versa. In addition, the spatial distribution of Jumpiness index in the region is related to geographical location and topography. In general, where elements change bigger, Jumpiness index becomes greater there. The regional distribution of Jumpiness index of different meteorological elements is different. The index value of Jumpiness index of 12 h precipitation forecast increases gradually from north to south in region Ⅰ. While 2 m ground temperature prediction, Jumpiness index from north to south in region Ⅰ gradually decreases.
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    References(29)

  • Fig. 1  Selection of target areas

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    Fig. 2  Averaged regional distribution of Jumpiness index of 12 h precipitation prediction (a)GQEC, (b)GQJP, (c)T639

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    Fig. 3  Jumpiness index of 12 h precipitation prediction with different leading times (a)region Ⅰ, (b)region Ⅱ, (c)region Ⅲ

    (08:00, 20:00 represent initial time, similarly hereinafter)

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    Fig. 4  Averaged regional distribution of Jumpiness index of 2 m temperature prediction (a)GQEC, (b)GQJP, (c)T639

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    Fig. 5  Jumpiness index of 2 m temperature prediction with different leading times (a)region Ⅰ, (b)region Ⅱ, (c)region Ⅲ

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    Fig. 6  Jumping frequency of 12 h precipitation prediction with different leading times (a)region Ⅰ, (b)region Ⅱ, (c)region Ⅲ

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    Fig. 7  Jumping frequency of 2 m temperature prediction with different leading times (a)region Ⅰ, (b)region Ⅱ, (c)region Ⅲ

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    Table  1  Jumping frequency of 12 h precipitation prediction

    模式 起报时间 区域Ⅰ 区域Ⅱ 区域Ⅲ
    GQEC 08:00 0.478 0.481 0.435
    20:00 0.481 0.480 0.450
    GQJP 08:00 0.627 0.621 0.600
    20:00 0.656 0.622 0.605
    T639 08:00 0.574 0.547 0.494
    20:00 0.563 0.567 0.511
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    Table  2  Jumping frequency of 2 m temperature prediction

    模式 起报时间 区域Ⅰ 区域Ⅱ 区域Ⅲ
    GQEC 08:00 0.421 0.466 0.431
    20:00 0.415 0.462 0.442
    GQJP 08:00 0.635 0.639 0.628
    20:00 0.700 0.651 0.657
    T639 08:00 0.511 0.542 0.509
    20:00 0.549 0.558 0.524
    DownLoad: Download CSV

    Table  3  Averaged jumping frequency of different seasons in region Ⅰ

    季节 起报时间 GQEC GQJP T639
    12 h降水 2 m温度 12 h降水 2 m温度 12 h降水 2 m温度
    春季 08:00 0.502 0.405 0.601 0.590 0.574 0.530
    20:00 0.501 0.410 0.659 0.670 0.604 0.561
    夏季 08:00 0.523 0.484 0.634 0.661 0.613 0.569
    20:00 0.541 0.456 0.656 0.721 0.605 0.572
    秋季 08:00 0.491 0.413 0.616 0.586 0.616 0.530
    20:00 0.487 0.421 0.663 0.693 0.571 0.659
    冬季 08:00 0.478 0.410 0.634 0.631 0.601 0.505
    20:00 0.470 0.392 0.626 0.652 0.569 0.522
    DownLoad: Download CSV

    Table  4  Daily Jumpiness index and jumping frequency of rainstorm process from 15 June to 16 Jun in 2016

    时段 跳跃指数 跳跃频率
    GQEC GQJP T639 GQEC GQJP T639
    14日20:00—15日08:00 0.211 0.099 0.267 0.526 0.714 0.615
    15日08:00—15日20:00 0.179 0.108 0.295 0.526 0.571 0.538
    15日20:00—16日08:00 0.191 0.085 0.293 0.579 0.615 0.714
    DownLoad: Download CSV

    Table  5  Daily Jumpiness index and jumping frequency of strong cold air process from 13 Feb to 15 Feb in 2016

    起报时间 预报时间 跳跃指数 跳跃频率
    GQEC GQJP T639 GQEC GQJP T639
    20:00 预报14日08:00 0.206 0.082 0.110 0.421 0.714 0.692
    08:00 预报14日20:00 0.202 0.165 0.209 0.421 0.714 0.462
    20:00 预报15日08:00 0.250 0.097 0.108 0.368 0.571 0.692
    08:00 预报15日20:00 0.256 0.126 0.211 0.473 0.429 0.386
    DownLoad: Download CSV
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    • Received : 2017-10-17
    • Accepted : 2018-03-05
    • Published : 2018-05-31
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