Climatological Observation and Model Simulation of Near-Surface Hourly Maximum Gust Wind in Northern China

Lu Yang aInstitute of Urban Meteorology, China Meteorological Administration, Beijing, China

Search for other papers by Lu Yang in
Current site
Google Scholar
PubMed
Close
,
Linye Song aInstitute of Urban Meteorology, China Meteorological Administration, Beijing, China

Search for other papers by Linye Song in
Current site
Google Scholar
PubMed
Close
,
Mingxuan Chen aInstitute of Urban Meteorology, China Meteorological Administration, Beijing, China
bCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

Search for other papers by Mingxuan Chen in
Current site
Google Scholar
PubMed
Close
, and
Conglan Cheng aInstitute of Urban Meteorology, China Meteorological Administration, Beijing, China

Search for other papers by Conglan Cheng in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

While previous work on the climatology of Northern China has focused on mean wind speed, wind gusts have received comparatively less attention but are equally important to various users. In this paper, an observed hourly maximum gust wind speeds (HMGS) dataset across North China has been created by using time series from 174 meteorological stations. The dataset offers superior quality, high spatiotemporal near-surface HMGS series for North China spanning from 2015 to 2022. The objective of this study is first to improve our understanding of the spatiotemporal gusts climatology in North China by analyzing the observed gust data. Second, we aim to supplement the observational data by using gust analysis and forecast data with a high spatial–temporal resolution from model simulations. The spatial characteristics of the seasonal cycle of the simulated analysis of mean HMGS and the performance in predicting gusts based on the geographical locations and elevations of the validation stations were investigated by comparing it with the observations. Results indicate the following: 1) Wind direction and intensity are affected by the terrain and climate conditions of different weather stations. Stations situated along the Bohai Bay coastal region and at higher-elevation areas of North China exhibit a higher mean HMGS than those located in the coastal and inland plains. 2) The probability density function curves for wind speed and wind direction exhibit notable variations across different elevation intervals. The contribution of moderate and strong gust wind speeds increases gradually with increasing altitude, while the gust directions in mountainous areas exhibit relatively consistent patterns due to the increased exposure to synoptic-scale forcing at higher elevations. 3) The nowcasting prediction system analysis of mean HMGS provides a higher horizontal resolution that is capable of capturing the contrasts between land and sea, as well as the influence of high HMGS associated with large-scale circulations in high-elevation regions.

Significance Statement

The purpose of this study is to better understand the spatiotemporal gust climatology in North China and the performance of the model-simulated gust analysis and forecast data. This is important because gusts conditions differ due to varying topographic and climatic conditions of different weather stations. Our results provide a valuable insight into the climatological variations of HMGS, their drivers, and identify the deficiencies in the model-simulation gusts.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mingxuan Chen, mxchen@ium.cn and mxchen@bjmb.gov.cn

Abstract

While previous work on the climatology of Northern China has focused on mean wind speed, wind gusts have received comparatively less attention but are equally important to various users. In this paper, an observed hourly maximum gust wind speeds (HMGS) dataset across North China has been created by using time series from 174 meteorological stations. The dataset offers superior quality, high spatiotemporal near-surface HMGS series for North China spanning from 2015 to 2022. The objective of this study is first to improve our understanding of the spatiotemporal gusts climatology in North China by analyzing the observed gust data. Second, we aim to supplement the observational data by using gust analysis and forecast data with a high spatial–temporal resolution from model simulations. The spatial characteristics of the seasonal cycle of the simulated analysis of mean HMGS and the performance in predicting gusts based on the geographical locations and elevations of the validation stations were investigated by comparing it with the observations. Results indicate the following: 1) Wind direction and intensity are affected by the terrain and climate conditions of different weather stations. Stations situated along the Bohai Bay coastal region and at higher-elevation areas of North China exhibit a higher mean HMGS than those located in the coastal and inland plains. 2) The probability density function curves for wind speed and wind direction exhibit notable variations across different elevation intervals. The contribution of moderate and strong gust wind speeds increases gradually with increasing altitude, while the gust directions in mountainous areas exhibit relatively consistent patterns due to the increased exposure to synoptic-scale forcing at higher elevations. 3) The nowcasting prediction system analysis of mean HMGS provides a higher horizontal resolution that is capable of capturing the contrasts between land and sea, as well as the influence of high HMGS associated with large-scale circulations in high-elevation regions.

Significance Statement

The purpose of this study is to better understand the spatiotemporal gust climatology in North China and the performance of the model-simulated gust analysis and forecast data. This is important because gusts conditions differ due to varying topographic and climatic conditions of different weather stations. Our results provide a valuable insight into the climatological variations of HMGS, their drivers, and identify the deficiencies in the model-simulation gusts.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mingxuan Chen, mxchen@ium.cn and mxchen@bjmb.gov.cn
Save
  • Abolude, A. T., W. Zhou, and A. A. Akinsanola, 2020: Evaluation and projections of wind power resources over China for the energy industry using CMIP5 models. Energies, 13, 2417, https://doi.org/10.3390/en13102417.

    • Search Google Scholar
    • Export Citation
  • Ashley, W. S., and A. W. Black, 2008: Fatalities associated with nonconvective high-wind events in the United States. J. Appl. Meteor. Climatol., 47, 717725, https://doi.org/10.1175/2007JAMC1689.1.

    • Search Google Scholar
    • Export Citation
  • Azorin-Molina, C., S. Rehman, J. A. Guijarro, T. R. McVicar, L. Minola, D. Chen, and S. M. Vicente-Serrano, 2018: Recent trends in wind speed across Saudi Arabia, 1978–2013: A break in the stilling. Int. J. Climatol., 38, e966e984, https://doi.org/10.1002/joc.5423.

    • Search Google Scholar
    • Export Citation
  • Bechtold, P., and J. R. Bidlot, 2009: Parametrization of convective gusts. ECMWF Newsletter, No. 199, ECMWF, Reading, United Kingdom, 15–18, https://doi.org/10.21957/kfr42kfp8c.

  • Bei, N., L. Zhao, J. Wu, X. Li, T. Feng, and G. Li, 2018: Impacts of sea-land and mountain-valley circulations on the air pollution in Beijing-Tianjin-Hebei (BTH): A case study. Environ. Pollut., 234, 429438, https://doi.org/10.1016/j.envpol.2017.11.066.

    • Search Google Scholar
    • Export Citation
  • Chen, C., and G. Y. Ren, 2016: The updated understanding of the change in near surface and upper air wind and wind energy. Climate Change Res. Lett., 5, 4147, https://doi.org/10.12677/CCRL.2016.51006.

    • Search Google Scholar
    • Export Citation
  • Chen, L., M. Zhang, J. Zhu, Y. Wang, and A. Skorokhod, 2018: Modeling impacts of urbanization and urban heat island mitigation on boundary layer meteorology and air quality in Beijing under different weather conditions. J. Geophys. Res. Atmos., 123, 43234344, https://doi.org/10.1002/2017JD027501.

    • Search Google Scholar
    • Export Citation
  • Coburn, J., and S. C. Pryor, 2022: Do machine learning approaches offer skill improvement for short-term forecasting of wind gust occurrence and magnitude? Wea. Forecasting, 37, 525543, https://doi.org/10.1175/WAF-D-21-0118.1.

    • Search Google Scholar
    • Export Citation
  • Coburn, J., J. Arnheim, and S. C. Pryor, 2022: Short-term forecasting of wind gusts at airports across CONUS using machine learning. Zenodo, https://doi.org/10.5281/ZENODO.6635178.

  • Cui, J., 2011: Surface Meteorological Observations (in Chinese). China Meteorological Press, 323 pp.

  • Cusack, S., 2013: A 101 year record of windstorms in the Netherlands. Climatic Change, 116, 693704, https://doi.org/10.1007/s10584-012-0527-0.

    • Search Google Scholar
    • Export Citation
  • Dou, Y. W., B. Qu, Y. Tao, and S. Hu, 2008: The application of quality control procedures for real-time data from automatic weather stations (in Chinese). Meteor. Mon., 34, 7781.

    • Search Google Scholar
    • Export Citation
  • Fujii, T., 2007: On geographical distributions and decadal changes of the annual maximum wind speeds caused by typhoons in Japan (in Japanese). J. Nat. Disaster Sci., 26, 267277.

    • Search Google Scholar
    • Export Citation
  • Gutiérrez, A., C. Porrini, and R. G. Fovell, 2020: Combination of wind gust models in convective events. J. Wind Eng. Ind. Aerodyn., 199, 104118, https://doi.org/10.1016/j.jweia.2020.104118.

    • Search Google Scholar
    • Export Citation
  • Han, Y. M., Z. W. Han, J. J. Cao, J. C. Chow, J. G. Watson, Z. S. An, S. X. Liu, and R. J. Zhang, 2008: Distribution and origin of carbonaceous aerosol over a rural high-mountain lake area, northern China and its transport significance. Atmos. Environ., 42, 24052414, https://doi.org/10.1016/j.atmosenv.2007.12.020.

    • Search Google Scholar
    • Export Citation
  • Harper, B. A., J. D. Kepert, and J. D. Ginger, 2010: Guidelines for converting between various wind averaging periods in tropical cyclone conditions. WMO/TD-1555, 64 pp., https://library.wmo.int/viewer/48652/download?file=wmo-td_1555_en.pdf&type=pdf&navigator=1.

  • Harris, A. R., and J. D. W. Kahl, 2017: Gust factors: Meteorologically stratified climatology, data artifacts, and utility in forecasting peak gusts. J. Appl. Meteor. Climatol., 56, 31513166, https://doi.org/10.1175/JAMC-D-17-0133.1.

    • Search Google Scholar
    • Export Citation
  • Helbig, N., R. Mott, A. van Herwijnen, A. Winstral, and T. Jonas, 2017: Parameterizing surface wind speed over complex topography. J. Geophys. Res. Atmos., 122, 651667, https://doi.org/10.1002/2016JD025593.

    • Search Google Scholar
    • Export Citation
  • Hu, H., and Coauthors, 2016: Study on risk and risk assessment of strong wind (in Chinese). Nat. Sci., 8, 223224.

  • Jia, C., J. Dou, S. Miao, and Y. Wang, 2019: Analysis of the characteristics of mountain-valley wind in the complex terrain over Yanqing-Zhangjiakou area in winter (in Chinese). Acta Meteor. Sin., 77, 475488, https://doi.org/10.11676/qxxb2019.033.

    • Search Google Scholar
    • Export Citation
  • Jiang, Y., Y. Luo, Z. Zhao, and S. Tao, 2010: Changes in wind speed over China during 1956–2004. Theor. Appl. Climatol., 99, 421430, https://doi.org/10.1007/s00704-009-0152-7.

    • Search Google Scholar
    • Export Citation
  • Jiménez, P. A., and J. Dudhia, 2012: Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF Model. J. Appl. Meteor. Climatol., 51, 300316, https://doi.org/10.1175/JAMC-D-11-084.1.

    • Search Google Scholar
    • Export Citation
  • Jung, C., D. Schindler, A. Buchholz, and J. Laible, 2017: Global gust climate evaluation and its influence on wind turbines. Energies, 10, 1474, https://doi.org/10.3390/en10101474.

    • Search Google Scholar
    • Export Citation
  • Kahl, J. D. W., B. R. Selbig, and A. R. Harris, 2021: Meteorologically stratified gust factors for forecasting peak wind gusts across the United States. Bull. Amer. Meteor. Soc., 102, E1665E1671, https://doi.org/10.1175/BAMS-D-21-0013.1.

    • Search Google Scholar
    • Export Citation
  • Klink, K., 2015: Seasonal patterns and trends of fastest 2-min winds at coastal stations in the conterminous USA. Int. J. Climatol., 35, 41674175, https://doi.org/10.1002/joc.4275.

    • Search Google Scholar
    • Export Citation
  • Lang, V. A., T. J. Turner, B. R. Selbig, A. R. Harris, and J. D. W. Kahl, 2022: Predicting peak wind gusts during specific weather types with the meteorologically stratified gust factor model. Wea. Forecasting, 37, 14351446, https://doi.org/10.1175/WAF-D-21-0201.1.

    • Search Google Scholar
    • Export Citation
  • Letson, F., S. C. Pryor, R. J. Barthelmie, and W. Hu, 2018: Observed gust wind speeds in the coterminous United States, and their relationship to local and regional drivers. J. Wind Eng. Ind. Aerodyn., 173, 199209, https://doi.org/10.1016/j.jweia.2017.12.008.

    • Search Google Scholar
    • Export Citation
  • Li, Z., Z.-n. Xiao, and C.-w. Zheng, 2021: Observation analysis of wind climate in China for 1971–2017 under the demand of wind energy evaluation and utilization. Energy Rep., 7, 35353546, https://doi.org/10.1016/j.egyr.2021.06.012.

    • Search Google Scholar
    • Export Citation
  • Liu, S., and Coauthors, 2009: Numerical simulation for the coupling effect of local atmospheric circulations over the area of Beijing, Tianjin and Hebei Province. Sci. China., 52D, 382392, https://doi.org/10.1007/s11430-009-0030-2.

    • Search Google Scholar
    • Export Citation
  • Miao, Y., J. Guo, S. Liu, H. Liu, G. Zhang, Y. Yan, and J. He, 2017: Relay transport of aerosols to Beijing-Tianjin-Hebei region by multi-scale atmospheric circulations. Atmos. Environ., 165, 3545, https://doi.org/10.1016/j.atmosenv.2017.06.032.

    • Search Google Scholar
    • Export Citation
  • Minola, L., F. Zhang, C. Azorin-Molina, A. A. Safaei Pirooz, R. G. J. Flay, H. Hersbach, and D. Chen, 2020: Near-surface mean and gust wind speeds in ERA5 across Sweden: Towards an improved gust parametrization. Climate Dyn., 55, 887907, https://doi.org/10.1007/s00382-020-05302-6.

    • Search Google Scholar
    • Export Citation
  • Panofsky, H. A., H. Tennekes, D. H. Lenschow, and J. C. Wyngaard, 1977: The characteristics of turbulent velocity components in the surface layer under convective conditions. Bound.-Layer Meteor., 11, 355361, https://doi.org/10.1007/BF02186086.

    • Search Google Scholar
    • Export Citation
  • Rotach, M. W., A. Gohm, M. N. Lang, D. Leukauf, I. Stiperski, and J. S. Wagner, 2015: On the vertical exchange of heat, mass, and momentum over complex, mountainous terrain. Front. Earth Sci., 3, 76, https://doi.org/10.3389/feart.2015.00076.

    • Search Google Scholar
    • Export Citation
  • Sinden, G., 2007: Characteristics of the UK wind resource: Long term patterns and relationship to electricity demand. Energy Policy, 35, 112127, https://doi.org/10.1016/j.enpol.2005.10.003.

    • Search Google Scholar
    • Export Citation
  • Suomi, I., and T. Vihma, 2018: Wind gust measurement techniques-from traditional anemometry to new possibilities. Sensors, 18, 1300, https://doi.org/10.3390/s18041300.

    • Search Google Scholar
    • Export Citation
  • Van Den Bossche, N., M. A. Lacasse, and A. Janssens, 2013: A uniform methodology to establish test parameters for watertightness testing: Part I: A critical review. Build. Environ., 63, 145156, https://doi.org/10.1016/j.buildenv.2012.12.003.

    • Search Google Scholar
    • Export Citation
  • Yang, L., F. Han, M. Chen, and J. Meng, 2018a: Thunderstorm gale identification method based on support vector machine (in Chinese with English abstract). J. Appl. Meteor. Sci., 29, 680689, https://doi.org/10.11898/1001-7313.20180604.

    • Search Google Scholar
    • Export Citation
  • Yang, L., and Coauthors, 2018b: Radar statistical characteristics and warning lead analysis of thunderstorm gales in different life periods in Beijing (in Chinese with English abstract). Meteor. Mon., 44, 802813.

    • Search Google Scholar
    • Export Citation
  • Yang, L., M. Chen, M. Chen, F. Gao, R. Qin, L. Song, and C. Cheng, 2019: Fusion of 3D high temporal and spatial resolution wind field and its application in nowcasting of severe convective weather. Acta Meteor. Sin., 77, 243255, https://doi.org/10.11676/qxxb2019.010.

    • Search Google Scholar
    • Export Citation
  • Yang, L., M. Chen, X. Wang, L. Song, M. Yang, R. Qin, C. Cheng, and S. Li, 2021: Classification of precipitation type in North China using model-based explicit fields of hydrometeors with modified thermodynamic conditions. Wea. Forecasting, 36, 91107, https://doi.org/10.1175/WAF-D-20-0005.1.

    • Search Google Scholar
    • Export Citation
  • Yang, L., L. Song, H. Jing, M. Chen, W. Cao, and J. Wu, 2022: Fusion prediction and correction technique for high-resolution wind field in winter Olympic Games area under complex terrain (in Chinese with English abstract). Meteor. Mon., 48, 162176, https://doi.org/10.7519/j.issn.1000-0526.2021.092902.

    • Search Google Scholar
    • Export Citation
  • Yang, L., C.-L. Cheng, Y. Xia, M. Chen, M.-X. Chen, H.-B. Zhang, and X.-Y. Huang, 2023: Evaluation of the added value of probabilistic nowcasting ensemble forecasts on regional ensemble forecasts. Adv. Atmos. Sci., 40, 937951, https://doi.org/10.1007/s00376-022-2056-8.

    • Search Google Scholar
    • Export Citation
  • Zhang, G., and Coauthors, 2020: Variability of daily maximum wind speed across China, 1975–2016: An examination of likely causes. J. Climate, 33, 27932816, https://doi.org/10.1175/JCLI-D-19-0603.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, R., S. Zhang, J. Luo, Y. Han, and J. Zhang, 2019: Analysis of near-surface wind speed change in China during 1958–2015. Theor. Appl. Climatol., 137, 27852801, https://doi.org/10.1007/s00704-019-02769-0.

    • Search Google Scholar
    • Export Citation
  • Zheng, Z. F., G. Y. Ren, and H. Gao, 2018: Analysis of the local circulation in Beijing area (in Chinese). Meteor. Mon., 44, 425433.

All Time Past Year Past 30 Days
Abstract Views 451 451 350
Full Text Views 72 72 50
PDF Downloads 84 84 52