Editorial Type:
Article
Article Type:
Research Article

Predicting Peak Wind Gusts during Specific Weather Types with the Meteorologically Stratified Gust Factor Model

Victoria A. Lang aAtmospheric Science Program, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin

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Teresa J. Turner aAtmospheric Science Program, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin

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Brandon R. Selbig aAtmospheric Science Program, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin

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Austin R. Harris aAtmospheric Science Program, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin

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Jonathan D. W. Kahl aAtmospheric Science Program, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin

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Online Publication:
03 Aug 2022
Print Publication:
01 Aug 2022
DOI:
https://doi.org/10.1175/WAF-D-21-0201.1
Page(s):
1435–1446
Restricted access

Abstract

Wind gusts present challenges to operational meteorologists, both to forecast accurately and also to verify. Strong wind gusts can damage structures and create costly risks for diverse industrial sectors. The meteorologically stratified gust factor (MSGF) model incorporates site-specific gust factors (the ratio of peak wind gust to mean wind speed) with wind speed and direction forecast guidance. The MSGF model has previously been shown to be a viable operational tool that exhibits skill (improvement over climatology) in forecasting peak wind gusts. This study assesses the performance characteristics of the MSGF model by evaluating peak gust predictions during several types of gust-producing weather phenomena. Peak wind gusts were prepared and verified for seven specific weather conditions over an 8-yr period at 16 sites across the United States. When coupled with two forms of model output statistics (MOS) wind guidance, the MSGF model generally shows skill in predicting peak wind gusts at forecast projections ranging from 6 to 72 h. The model performed best during high pressure and nocturnal conditions and was also skillful during conditions involving snow. The model did not perform well during the “rain with thunder” weather type. The MSGF model is a viable tool for the operational prediction of peak gusts for most gust-producing weather types.

Significance Statement

Wind gusts are an important and potentially costly environmental hazard. Wind gusts affect many industrial sectors, including transportation, power generation, forestry, construction, and insurance, but predicting gusts remains a challenging component of weather forecasting. Recent studies have demonstrated that the meteorologically stratified gust factor (MSGF) model shows skill in predicting peak gusts. This study shows that the MSGF model is skillful at predicting peak gusts during specific types of gust-producing weather phenomena at forecast projections up to 72 h, providing further confirmation that the MSGF model is a viable tool for the operational prediction of peak gusts.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Victoria A. Lang, valang@uwm.edu

Abstract

Wind gusts present challenges to operational meteorologists, both to forecast accurately and also to verify. Strong wind gusts can damage structures and create costly risks for diverse industrial sectors. The meteorologically stratified gust factor (MSGF) model incorporates site-specific gust factors (the ratio of peak wind gust to mean wind speed) with wind speed and direction forecast guidance. The MSGF model has previously been shown to be a viable operational tool that exhibits skill (improvement over climatology) in forecasting peak wind gusts. This study assesses the performance characteristics of the MSGF model by evaluating peak gust predictions during several types of gust-producing weather phenomena. Peak wind gusts were prepared and verified for seven specific weather conditions over an 8-yr period at 16 sites across the United States. When coupled with two forms of model output statistics (MOS) wind guidance, the MSGF model generally shows skill in predicting peak wind gusts at forecast projections ranging from 6 to 72 h. The model performed best during high pressure and nocturnal conditions and was also skillful during conditions involving snow. The model did not perform well during the “rain with thunder” weather type. The MSGF model is a viable tool for the operational prediction of peak gusts for most gust-producing weather types.

Significance Statement

Wind gusts are an important and potentially costly environmental hazard. Wind gusts affect many industrial sectors, including transportation, power generation, forestry, construction, and insurance, but predicting gusts remains a challenging component of weather forecasting. Recent studies have demonstrated that the meteorologically stratified gust factor (MSGF) model shows skill in predicting peak gusts. This study shows that the MSGF model is skillful at predicting peak gusts during specific types of gust-producing weather phenomena at forecast projections up to 72 h, providing further confirmation that the MSGF model is a viable tool for the operational prediction of peak gusts.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Victoria A. Lang, valang@uwm.edu
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  • Adame, J. A., L. Lope, P. J. Hidalgo, M. Sorribas, I. Gutiérrez-Álvarez, A. del Águila, A. Saiz-Lopez, and M. Yela, 2018: Study of the exceptional meteorological conditions, trace gases and particulate matter measured during the 2017 forest fire in Doñana Natural Park, Spain. Sci. Total Environ., 645, 710–720, https://doi.org/10.1016/j.scitotenv.2018.07.181.

    • Search Google Scholar
    • Export Citation

  • Caban, J., A. Nieoczym, and L. Gardynski, 2021: Strength analysis of a container semi-truck frame. Eng. Fail. Anal.,127, 105487, https://doi.org/10.1016/j.engfailanal.2021.105487.

  • Changnon, S. A., 2009: Temporal and spatial distributions of wind storm damages in the United States. Climatic Change, 94, 473–482, https://doi.org/10.1007/s10584-008-9518-6.

    • Search Google Scholar
    • Export Citation

  • De Petris, S., F. Sarvia, M. Gullino, E. Tarantino, and E. Borgogno-Mondino, 2021: Sentinel-1 polarimetry to map apple orchard damage after a storm. Remote Sens., 13, 1030, https://doi.org/10.3390/rs13051030.

    • Search Google Scholar
    • Export Citation

  • Durst, C., 1960: Wind speeds over short periods of time. Meteor. Mag., 89, 181–187.

  • Giess, P., A. J. H. Goddard, and G. Shaw, 1997: Factors affecting particle resuspension from grass swards. J. Aerosol Sci., 28, 1331–1349, https://doi.org/10.1016/S0021-8502(97)00021-9.

    • Search Google Scholar
    • Export Citation

  • Grubisic, V., and Coauthors, 2008: The Terrain-Induced Rotor Experiment: A field campaign overview including observational highlights. Bull. Amer. Meteor. Soc., 89, 1513–1533, https://doi.org/10.1175/2008BAMS2487.1.

    • 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

  • 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, World Meteorological Organization, 64 pp., https://library.wmo.int/doc_num.php?explnum_id=290.

  • Harris, A., and J. Kahl, 2017: Gust factors: Meteorologically stratified climatology, data artifacts, and utility in forecasting peak gusts. J. Appl. Meteor. Climatol., 56, 3151–3166, https://doi.org/10.1175/JAMC-D-17-0133.1.

    • Search Google Scholar
    • Export Citation

  • Hart, N. C. G., S. L. Gray, and P. A. Clark, 2017: Sting-jet windstorms over the North Atlantic: Climatology and contribution to extreme wind risk. J. Climate, 30, 5455–5471, https://doi.org/10.1175/JCLI-D-16-0791.1.

    • Search Google Scholar
    • Export Citation

  • Hewson, T. D., and U. Neu, 2015: Cyclones, windstorms and the IMILAST project. Tellus, A67, 27128, https://doi.org/10.3402/tellusa.v67.27128.

    • Search Google Scholar
    • Export Citation

  • Jung, C., and D. Schindler, 2019: Historical winter storm atlas for Germany (GeWiSA). Atmosphere, 10, 387, https://doi.org/10.3390/atmos10070387.

    • 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., 2020: Forecasting peak wind gusts using meteorologically stratified gust factors and MOS guidance. Wea. Forecasting, 35, 1129–1143, https://doi.org/10.1175/WAF-D-20-0045.1.

    • 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, E1665–E1671, https://doi.org/10.1175/BAMS-D-21-0013.1.

    • Search Google Scholar
    • Export Citation

  • Kim, S.-J., J.-H. Shim, and H.-K. Kim, 2020: How wind affects vehicles crossing a double-deck suspension bridge. J. Wind Eng. Ind. Aerodyn., 206, 104329, https://doi.org/10.1016/j.jweia.2020.104329.

    • Search Google Scholar
    • Export Citation

  • Krayer, W. R., and R. D. Marshall, 1992: Gust factors applied to hurricane winds. Bull. Amer. Meteor. Soc., 73, 613–618, https://doi.org/10.1175/1520-0477(1992)073<0613:GFATHW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kristensen, L., M. Casanova, M. Courtney, and I. Troen, 1991: In search of a gust definition. Bound.-Layer Meteor., 55, 91–107, https://doi.org/10.1007/BF00119328.

    • Search Google Scholar
    • Export Citation

  • Lei, Y., Y. Huang, and H. Wang, 2021: Effects of wind disturbance on the aerodynamic performance of a quadrotor MAV during hovering. J. Sens., 2021, 6681716, https://doi.org/10.1155/2021/6681716.

    • Search Google Scholar
    • Export Citation

  • Luchetti, N. T., K. Friedrich, and C. E. Rodell, 2020: Evaluating thunderstorm gust fronts in New Mexico and Arizona. Mon. Wea. Rev., 148, 4943–4956, https://doi.org/10.1175/MWR-D-20-0204.1.

    • Search Google Scholar
    • Export Citation

  • Manasseh, R., and J. H. Middleton, 1999: The surface wind gust regime and aircraft operations at Sydney Airport. J. Wind Eng. Ind. Aerodyn., 79, 269–288, https://doi.org/10.1016/S0167-6105(97)00293-6.

    • Search Google Scholar
    • Export Citation

  • Masters, F. J., P. J. Vickery, P. Bacon, and E. N. Rappaport, 2010: Toward objective, standardized intensity estimates from surface wind speed observations. Bull. Amer. Meteor. Soc., 91, 1665–1682, https://doi.org/10.1175/2010BAMS2942.1.

    • Search Google Scholar
    • Export Citation

  • Mendenhall, W., D. D. Wackerly, and R. L. Schaeffer, 1990: Mathematical Statistics with Applications. 4th ed. PWS-Kent Publishing, 818 pp.

  • Montenegro, P. A., R. Heleno, H. Carvalho, R. Calçada, and C. J. Baker, 2020: A comparative study on the running safety of trains subjected to crosswinds simulated with different wind models. J. Wind Eng. Ind. Aerodyn., 207, 104398, https://doi.org/10.1016/j.jweia.2020.104398.

    • Search Google Scholar
    • Export Citation

  • NOAA, 1998: Automated Surface Observing System (ASOS) user’s guide. NOAA Tech. Doc., 74 pp., https://www.weather.gov/media/asos/aum-toc.pdf.

  • OSHA, 2021: Standard 1917.45—Cranes and derricks. U.S. Department of Labor, Occupational Safety and Health Administration, accessed 24 February 2022, https://www.osha.gov/laws-regs/regulations/standardnumber/1917/1917.45.

  • Paulsen, B. M., and J. L. Schroeder, 2005: An examination of tropical and extratropical gust factors and the associated wind speed histograms. J. Appl. Meteor., 44, 270–280, https://doi.org/10.1175/JAM2199.1.

    • Search Google Scholar
    • Export Citation

  • Pinto, P., and M. Belo-Pereira, 2020: Damaging convective and non-convective winds in southwestern Iberia during Windstorm Xola. Atmosphere, 11, 692, https://doi.org/10.3390/atmos11070692.

    • Search Google Scholar
    • Export Citation

  • Pirooz, A. A. S., S. Moore, R. Turner, and R. G. J. Flay, 2021: Coupling high-resolution numerical weather prediction and computational fluid dynamics: Auckland Harbour case study. Appl. Sci., 11, 3982, https://doi.org/10.3390/app11093982.

    • Search Google Scholar
    • Export Citation

  • Sheridan, P., 2018: Current gust forecasting techniques, developments, and challenges. Adv. Sci. Res., 15, 159–172, https://doi.org/10.5194/asr-15-159-2018.

    • Search Google Scholar
    • Export Citation

  • Sherlock, R. H., 1952: Variation of wind velocity and gusts with height. Proc. Amer. Soc. Civ. Eng., 78, 1–24; 79, 1–20.

  • Sinden, G., 2007: Characteristics of the U.K. wind resource: Long term patterns and relationship to electricity demand. Energy Policy, 35, 112–127, https://doi.org/10.1016/j.enpol.2005.10.003.

    • Search Google Scholar
    • Export Citation

  • Siti, I., M. Mjahed, H. Ayad, and A. El Kari, 2019: New trajectory tracking approach for a quadcopter using genetic algorithm and reference model methods. Appl. Sci., 9, 1780, https://doi.org/10.3390/app9091780.

    • Search Google Scholar
    • Export Citation

  • Su, N., S. Peng, and N. Hong, 2021: Stochastic dynamic transient gusty wind effect on the sliding and overturning of quayside container cranes. Struct. Infrastruct. Eng., 17, 1271–1283, https://doi.org/10.1080/15732479.2020.1809465

    • Search Google Scholar
    • Export Citation

  • Suomi, I., T. Vihma, S.-E. Gryning, and C. Fortelius, 2013: Wind-gust parametrizations at heights relevant for wind energy: A study based on mast observations. Quart. J. Roy. Meteor. Soc., 139, 1298–1310, https://doi.org/10.1002/qj.2039.

    • Search Google Scholar
    • Export Citation

  • Taillardat, M., 2021: Skewed and mixture of Gaussian distributions for ensemble postprocessing. Atmosphere, 12, 966, https://doi.org/10.3390/atmos12080966.

    • Search Google Scholar
    • Export Citation

  • Teoh, Y. E., A. Alipour, and A. Cancelli, 2019: Probabilistic performance assessment of power distribution infrastructure under wind events. Eng. Struct., 197, 109199, https://doi.org/10.1016/j.engstruct.2019.05.041.

    • Search Google Scholar
    • Export Citation

  • Thalla, O., and S. C. Stiros, 2018: Wind-induced fatigue and asymmetric damage in a timber bridge. Sensors, 18, 3867, https://doi.org/10.3390/s18113867.

    • Search Google Scholar
    • Export Citation

  • U.S. Air Force, 2019: Air Force Handbook 15-101; Weather Meteorological Techniques. Air Force Handbook AFH15-101, 248 pp., https://static.e-publishing.af.mil/production/1/af_a3/publication/afh15-101/afh15-101.pdf.

  • Usbeck, T., T. Wohlgemuch, C. Pfister, R. Volz, M. Beniston, and M. Dobbertin, 2010: Wind speed measurements and forest damage in Canton Zurich (central Europe) from 1891 to winter 2007. Int. J. Climatol., 30, 347–358, https://doi.org/10.1002/joc.1895.

    • Search Google Scholar
    • Export Citation

  • Valta, H., I. Lehtonen, T. K. Laurila, A. Venäläinen, M. Laapas, and H. Gregow, 2019: Communicating the amount of windstorm induced forest damage by the maximum wind gust speed in Finland. Adv. Sci. Res., 16, 31–37, https://doi.org/10.5194/asr-16-31-2019.

    • 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, 145–156, https://doi.org/10.1016/j.buildenv.2012.12.003.

    • Search Google Scholar
    • Export Citation

  • Weggel, J. R., 1999: Maximum daily wind gusts related to mean daily wind speed. J. Struct. Eng., 125, 465–468, https://doi.org/10.1061/(ASCE)0733-9445(1999)125:4(465).

    • Search Google Scholar
    • Export Citation

  • Welker, C., O. Martius, P. Stucki, D. Bresch, S. Dierer, and S. Bronnimann, 2016: Modelling economic losses of historic and present-day high-impact winter windstorms in Switzerland. Tellus, 68A, 29546, https://doi.org/10.3402/tellusa.v68.29546.

    • Search Google Scholar
    • Export Citation

  • Wong, C. J., and M. D. Miller, Eds., 2010: Guidelines for Electrical Transmission Line Structural Loading. ASCE, 204 pp.

  • Yang, T.-H., and C.-C. Tsai, 2019: Using numerical weather model outputs to forecast wind gusts during typhoons. J. Wind Eng. Ind. Aerodyn., 188, 247–259, https://doi.org/10.1016/j.jweia.2019.03.003.

    • Search Google Scholar
    • Export Citation

  • Zhang, Y., and L. Li, 2021: Crosswind stability of metro train on a high-pier viaduct under spatial gust environment in mountain city. KSCE J. Civ. Eng., 25, 4661–4670, https://doi.org/10.1007/s12205-021-0706-5.

    • Search Google Scholar
    • Export Citation
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