Vol.34, NO.6, 2023
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Article Navigation >  Journal of Applied Meteorological Science  > 2023  >  34(6): 668-680
Hu Haichuan, Qian Chuanhai, Qu Hongyu. Correction of gust estimation in the Yellow and Bohai Seas and adjacent areas. J Appl Meteor Sci, 2023, 34(6): 668-680. DOI:  10.11898/1001-7313.20230603.
Citation: Hu Haichuan, Qian Chuanhai, Qu Hongyu. Correction of gust estimation in the Yellow and Bohai Seas and adjacent areas. J Appl Meteor Sci, 2023, 34(6): 668-680. DOI:  10.11898/1001-7313.20230603.
shu

Correction of Gust Estimation in the Yellow and Bohai Seas and Adjacent Areas

DOI: 10.11898/1001-7313.20230603
  • 1.

    National Meteorological Center, Beijing 100081

  • 2.

    CMA Institute for Development and Programme Design, Beijing 100081

  • Received Date: 2023-08-08
  • Rev Recd Date: 2023-10-17
  • Publish Date: 2023-11-27
    Abstract
  • Due to the influence of local factors such as underlying surface, the fitting performance of the gust factor method can vary among different observation stations in the Yellow and Bohai Seas and adjacent areas. Therefore, based on observations from January 2021 to December 2022, causes for different performances are analyzed, and a gust estimation correction (GECO) method is proposed based on the coefficient of difference and frequency matching to correct fitting results of the gust factor method. It's concluded that, as the gust wind speed increases, the fitting results tend to be biased towards the gust at the whole observation time rather than the maximum gust within the hour. The difference coefficient calculated from the maximum gust within the hour and the gust at the whole hour observation time can quantitatively characterize the influence of local characteristics of the observation station on fitting results. For stations with large difference coefficients, the gust factor method has a larger negative bias in fitting the strong gusts, so it needs to be corrected to a greater extent. Conversely, for stations with small differences, the gust factor method has a smaller negative bias in fitting the strong gusts, so it only needs to be corrected to a smaller extent. GECO method constructed based on statistical results of 12 benchmark observation stations can also be applied to 364 stations in the Yellow and Bohai Seas and adjacent areas, demonstrating the feasibility of quantitatively characterizing the influence of local factors such as underlying surface on fitting results of gusts, as well as the stability of GECO method. In the comparative test of fitting errors for 364 stations in the Yellow and Bohai Seas and adjacent areas, after correction by GECO method, the root mean square error of gusts above 12 m·s-1 and 16 m·s-1 is reduced by 12.3% and 11.5%, respectively, compared to the gust factor method. Although GECO method may slightly increase the fitting error of weak gusts, it can significantly improve the fitting performance of strong gusts. In the verification during the impact of Typhoon Muifa from 14 September to 16 September in 2022, the fitting performance of strong gusts is also significantly improved after correction by GECO method. The improvement of fitting performance is the basis for improving forecasting skill. Combining GECO method with other objective gust forecasting methods can further enhance the forecasting service capability of gusts.
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    References(38)

  • Fig. 1  Distribution of 364 representative stations in the Yellow and Bohai Seas and adjacent areas

    (the number denotes the order of benchmark observation stations)

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    Fig. 2  Scatter plots of gust factor fitting results and hourly maximum gust from Jan 2021 to Dec 2022

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    Fig. 3  Scatter plot of mean error and the ratio of closer to instantaneous gust at different wind speed magnitudes for 12 benchmark observation stations from Jan 2021 to Dec 2022

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    Fig. 4  Scatter plot of the difference coefficient and goodness of fit between gust factor fitting results and hourly maximum gust at different observation stations from Jan 2021 to Dec 2022

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    Fig. 5  Distribution of D values for C values ranging from 0 to 1 with a step size of 0.01 for 12 benchmark observation stations from Jan 2021 to Dec 2022

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    Fig. 6  Cumulative frequency distribution(a) and error comparison(b) after natural logarithm transformation for 12 benchmark observation stations from Jan 2021 to Dec 2022

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    Fig. 7  Cumulative frequency distribution(a) and error comparison(b) after natural logarithm transformation for 364 observation stations from Jan 2021 to Dec 2022

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    Fig. 8  Track of Typhoon Muifa(2212)

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    Fig. 9  Maximum hourly gust during the impact of Typhoon Muifa from 2000 BT 14 Sep to 2000 BT 16 Sep in 2022(a), the distribution of stations with positive bias in fitting results of gusts by applying the gust factor method(b) and GECO method(c)

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    Table  1  Benchmark observation station information

    序号 区站号 位置 海拔/m
    1 54337 41.1°N，121.1°E 65
    2 54497 40.1°N，124.3°E 13
    3 54511 39.8°N，116.5°E 33
    4 54539 39.4°N，118.9°E 8
    5 54662 38.9°N，121.6°E 90
    6 54727 36.7°N，117.6°E 122
    7 54857 36.1°N，120.3°E 75
    8 54778 37.2°N，122.5°E 61
    9 58027 34.3°N，117.2°E 42
    10 58150 33.8°N，120.3°E 1
    11 58238 31.9°N，118.9°E 36
    12 58362 31.4°N，121.5°E 6
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    Table  2  Comparison of fitting errors before and after correction for 12 benchmark observation stations with gusts no less than 12 m·s-1 from Jan 2021 to Dec 2022

    序号 订正前平均误差/(m·s-1) 订正后平均误差/(m·s-1) 均方根误差减少率/%
    1 -2.69 -1.40 19.14
    2 -1.76 -0.66 12.72
    3 -2.69 -1.43 16.44
    4 -2.42 -1.24 17.33
    5 -2.01 -0.70 12.78
    6 -1.25 0.40 6.01
    7 -1.45 -0.32 7.27
    8 -1.97 -0.78 13.27
    9 -3.56 -2.36 14.69
    10 -1.80 -0.84 12.32
    11 -2.90 -1.90 14.78
    12 -3.00 -1.82 14.18
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    Table  3  Comparison of fitting errors of gust factor method for land and buoy observation stations from Jan 2021 to Dec 2022

    统计量 风速范围/(m·s-1)
    10.8~13.8 13.9~17.1 17.2~20.7
    陆地样本量 48706 10374 1483
    浮标样本量 4433 1866 544
    陆地拟合误差标准差/(m·s-1) 2.705 3.516 4.979
    浮标拟合误差标准差/(m·s-1) 1.830 2.082 2.236
    陆地拟合误差平均值/(m·s-1) -2.47 -3.66 -5.54
    浮标拟合误差平均值/(m·s-1) -0.60 -0.83 -1.21
    DownLoad: Download CSV
  • [1]
    Dong S L. Gust extremes in China and its statistical study. Acta Meteor Sinica, 2001, 59(3): 327-333. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200103006.htm
    [2]
    Masters F J, Vickery P J, Bacon P, et al. Toward objective, standardized intensity estimates from surface wind speed observations. Bull Amer Meteor Soc, 2010, 91(12): 1665-1682. doi:  10.1175/2010BAMS2942.1
    [3]
    Kahl J D W. Forecasting peak wind gusts using meteorologically stratified gust factors and MOS guidance. Wea Forecasting, 2020, 35(3): 1129-1143. doi:  10.1175/WAF-D-20-0045.1
    [4]
    Quan J P, Li Q C, Zhong J Q, et al. Evaluation of three different gust diagnostic schemes in the CMA-BJ for gale forecasting over Beijing. Acta Meteor Sinica, 2022, 80(1): 108-123. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202201008.htm
    [5]
    Lang V A, Turner T J, Selbig B R, et al. Predicting peak wind gusts during specific weather types with the meteorologically stratified gust factor model. Wea Forecasting, 2022, 37(8): 1435-1446. doi:  10.1175/WAF-D-21-0201.1
    [6]
    Zhang X Y, Proppe C. Risk assessment of road vehicles under wind gust excitation. J Comput Nonlinear Dyn, 2020, 15(10). DOI:  10.1115/1.4047638.
    [7]
    Kahl J D W, Selbig B R, Harris A R. Meteorologically stratified gust factors for forecasting peak wind gusts across the United States. American Meteor Society, 2021, 102: 1665-1671. doi:  10.1175/BAMS-D-21-0013.1
    [8]
    European Center for Medium-Range Weather Forecasts(ECMWF). IFS Documentation CY47R3-Part Ⅳ: Physical Processes. 2021.
    [9]
    Mu M, Chen B Y, Zhou F F, et al. Methods and uncertainties of meteorological forecast. Meteor Mon, 2011, 37(1): 1-13. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201101002.htm
    [10]
    Han N F, Yang L, Chen M X, et al. Machine learning correction of wind, temperature and humidity elements in Beijing-Tianjin-Hebei Region. J Appl Meteor Sci, 2022, 33(4): 489-500. doi:  10.11898/1001-7313.20220409
    [11]
    Hu Y Y, Pang L, Wang Q G. Application of deep learning bias correction method to temperature grid forecast of 7-15 days. J Appl Meteor Sci, 2023, 34(4): 426-437. doi:  10.11898/1001-7313.20230404
    [12]
    Hu H C, Zhou J. Application of ensemble extreme wind forecast in Bohai Sea. Meteor Mon, 2019, 45(12): 1747-1755. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201912012.htm
    [13]
    Harris A R, Kahl J D W. Gust factors: Meteorologically stratified climatology, data artifacts, and utility in forecasting peak gusts. J Appl Meteor Climatol, 2017, 56(12): 3151-3166. doi:  10.1175/JAMC-D-17-0133.1
    [14]
    Schulz B, Lerch S. Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison. Mon Wea Rev, 2022, 150(1): 235-257. doi:  10.1175/MWR-D-21-0150.1
    [15]
    Zhu Z H, Zheng Y X, Guo J B. Refinement and improvement of the maximum wind speed prediction equation for Shanghai coastal stations. Mar Forecasts, 2022, 39(1): 32-38. https://www.cnki.com.cn/Article/CJFDTOTAL-HYYB202201004.htm
    [16]
    Hu H C, Liu J, Lin J. Application of prediction equation to gust forecasting for Chinese offshore areas. Meteor Mon, 2022, 48(3): 334-344. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202203007.htm
    [17]
    Shu Z R, Li Q S, He Y C, et al. Gust factors for tropical cyclone, monsoon and thunderstorm winds. J Wind Eng Ind Aerodyn, 2015, 142: 1-14. doi:  10.1016/j.jweia.2015.02.003
    [18]
    Zhou F, Jiang L L, Tu X P, et al. Near-surface gust factor characteristics in several disastrous winds over Zhejiang Province. J Appl Meteor Sci, 2017, 28(1): 119-128. doi:  10.11898/1001-7313.20170111
    [19]
    Yang L, Wang X L, Song L Y, et al. An algorithm for objective forecasting of gust winds at 100 m horizontal resolution based on a gust coefficient model. Acta Meteor Sinica, 2023, 81(1): 94-109. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202301006.htm
    [20]
    Chen Y, Zhang N. The wind turbulence of the near-surface layer of Jiangsu coastal area and its response to typhoon. J Appl Meteor Sci, 2019, 30(2): 177-190. doi:  10.11898/1001-7313.20190205
    [21]
    Yu B, Chowdhury G A. Gust factors and turbulence intensities for the tropical cyclone environment. J Appl Meteor Climatol, 2009, 48(3): 534-552.
    [22]
    Hu B. Analysis of gust factor associated with typhoons on Zhejiang coast. J Trop Meteor, 2017, 33(6): 841-849. https://www.cnki.com.cn/Article/CJFDTOTAL-RDQX201706005.htm
    [23]
    Letson F, Pryor S C, Barthelmie R J, et al. Observed gust wind speeds in the coterminous United States, and their relationship to local and regional drivers. J Wind Eng Ind Aerodyn, 2018, 173: 199-209.
    [24]
    Zhu Y J, Luo Y. Precipitation calibration based on the frequency-matching method. Wea Forecasting, 2015, 30(5): 1109-1124.
    [25]
    Yu J J, Shen Y, Pan Y, et al. Improvement of satellite-based precipitation estimates over China based on probability density function matching method. J Appl Meteor Sci, 2013, 24(5): 544-553. http://qikan.camscma.cn/article/id/20130504
    [26]
    He L F, Chyi D, Yu W. Development mechanisms of the Yellow Sea and Bohai Sea cyclone causing extreme snowstorm in Northeast China. J Appl Meteor Sci, 2022, 33(4): 385-399. doi:  10.11898/1001-7313.20220401
    [27]
    Liu T, Duan Y H, Feng J N, et al. Characteristics and mechanisms of long-lived concentric eyewalls in Typhoon Lekima in 2019. J Appl Meteor Sci, 2021, 32(3): 289-301. doi:  10.11898/1001-7313.20210303
    [28]
    Liang J, Zhang S J, Liu X C, et al. Numerical simulation of high wind events over the Yellow Sea and the Bohai Sea considering topographic effects of Liaodong Peninsula and Shandong Peninsula. J Trop Meteor, 2015, 31(3): 374-384. https://www.cnki.com.cn/Article/CJFDTOTAL-RDQX201503009.htm
    [29]
    Brasseur O. Development and application of a physical approach to estimating wind gusts. Mon Wea Rev, 2001, 129(1): 5-25.
    [30]
    Minola L, Zhang F, Azorin-Molina C, et al. Near-surface mean and gust wind speeds in ERA5 across Sweden: Towards an improved gust parametrization. Climate Dyn, 2020, 55(3): 887-907.
    [31]
    Xing C Y, Wu S A, Zhu J J. Comparison on the circulation background of tropical cyclone affecting the South China Sea based upon different reanalysis datasets. J Appl Meteor Sci, 2023, 34(2): 179-192. doi:  10.11898/1001-7313.20230205
    [32]
    Jiang Y, Wu S P, Hu K, et al. Imbalanced data classification method based on Lasso and constructive covering algorithm. J Comput Appl, 2023, 43(4): 1086-1093. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY202304013.htm
    [33]
    Liu Y, Yang K. Credit fraud detection for extremely imbalanced data based on ensembled deep learning. J Comput Res Dev, 2021, 58(3): 539-547. https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ202103009.htm
    [34]
    Wang Z C, Zhi S Q, Ding L Y. Observation and analysis on Giongzhou Strait gales of severe Typhoon Neasat(2011). J Appl Meteor Sci, 2013, 24(5): 595-605. http://qikan.camscma.cn/article/id/20130509
    [35]
    Hu H C, Zhao W, Dong L. Application of probability density function matching in the offshore 10 m wind speed forecasting in China. J Trop Meteor, 2021, 37(1): 91-101. https://www.cnki.com.cn/Article/CJFDTOTAL-RDQX202101009.htm
    [36]
    Zhi X F, Lyu Y. Calibration of the multimodel precipitation forecasts in China using the frequency matching method. Trans Atmos Sci, 2019, 42(6): 814-823. https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201906002.htm
    [37]
    Zhi X F, Zhao C. Heavy precipitation forecasts based on multi-model ensemble members. J Appl Meteor Sci, 2020, 31(3): 303-314. doi:  10.11898/1001-7313.20200305
    [38]
    Zhang J, Sun J, Shen X S, et al. Key model technologies of CMA-GFS V4.0 and application to operational forecast. J Appl Meteor Sci, 2023, 34(5): 513-526. doi:  10.11898/1001-7313.20230501
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    • Received : 2023-08-08
    • Accepted : 2023-10-17
    • Published : 2023-11-27
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