• Adelekan, I. O., 2012: Vulnerability to wind hazards in the traditional city of Ibadan, Nigeria. Environ. Urbanization, 24, 597617, https://doi.org/10.1177/0956247812454247.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barthelmie, R. J., K. Dantuono, E. Renner, F. W. Letson, and S. C. Pryor, 2021: Extreme wind and waves in U.S. east coast offshore wind energy lease areas. Energies, 14, 1053, https://doi.org/10.3390/en14041053.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Booth, J. F., H. E. Rieder, D. E. Lee, and Y. Kushnir, 2015: The paths of extratropical cyclones associated with wintertime high-wind events in the northeastern United States. J. Appl. Meteor. Climatol., 54, 18711885, https://doi.org/10.1175/JAMC-D-14-0320.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Born, K., P. Ludwig, and J. G. Pinto, 2012: Wind gust estimation for Mid-European winter storms: Towards a probabilistic view. Tellus, 64A, 17471, https://doi.org/10.3402/tellusa.v64i0.17471.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brasseur, O., 2001: Development and application of a physical approach to estimating wind gusts. Mon. Wea. Rev., 129, 525, https://doi.org/10.1175/1520-0493(2001)129<0005:DAAOAP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, A., and A. Dowdy, 2021: Severe convection-related winds in Australia and their associated environments. J. South. Hemisphere Earth Syst. Sci., 71, 30, https://doi.org/10.1071/ES19052.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cadenas, E., W. Rivera, R. Campos-Amezcua, and C. Heard, 2016: Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 9, 109124, https://doi.org/10.3390/en9020109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, G., H. Lu, Y. Chang, and Y. Lee, 2017: An improved neural network-based approach for short-term wind speed and power forecast. Renewable Energy, 105, 301311, https://doi.org/10.1016/j.renene.2016.12.071.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Comarazamy, D., J. E. González-Cruz, and Y. Andreopoulos, 2020: Projections of wind gusts for New York City under a changing climate. ASME J. Eng. Sustainable Build. Cities, 1, 031004, https://doi.org/10.1115/1.4048059.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Da Silva, I. N., D. H. Spatti, R. A. Flauzino, L. H. B. Liboni, and S. F. dos Reis Alves, 2017: Artificial neural network architectures and training processes. Artificial Neural Networks, Springer, 2128, https://doi.org/10.1007/978-3-319-43162-8_2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Oliveira, M., A. B. R. Eufrásio, M. X. Guterres, M. C. R. Murça, and R. de Arantes Gomes, Eds., 2021: Analysis of airport weather impact on on-time performance of arrival flights for the Brazilian domestic air transportation system. J. Air Transp. Manage., 91, 101974, https://doi.org/10.1016/j.jairtraman.2020.101974.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dormann, C. F., and Coauthors, 2013: Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36, 2746, https://doi.org/10.1111/j.1600-0587.2012.07348.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dupont, S., D. Pivato, and Y. Brunet, 2015: Wind damage propagation in forests. Agric. For. Meteor., 214, 243251, https://doi.org/10.1016/j.agrformet.2015.07.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Earl, N., S. Dorling, M. Starks, and R. Finch, 2017: Subsynoptic‐scale features associated with extreme surface gusts in UK extratropical cyclone events. Geophys. Res. Lett., 44, 39323940, https://doi.org/10.1002/2017GL073124.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fonte, P. M., G. X. Silva, and J. Quadrado, 2005: Wind speed prediction using artificial neural networks. WSEAS Trans. Syst., 4, 379384.

    • Search Google Scholar
    • Export Citation
  • Fovell, R. G., and A. Gallagher, 2018: Winds and gusts during the Thomas fire. Fire, 1, 47, https://doi.org/10.3390/fire1030047.

  • Gardner, M. W., and S. Dorling, 1998: Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences. Atmos. Environ., 32, 26272636, https://doi.org/10.1016/S1352-2310(97)00447-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gultepe, I., and Coauthors, 2019: A review of high impact weather for aviation meteorology. Pure Appl. Geophys., 176, 18691921, https://doi.org/10.1007/s00024-019-02168-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, Z., W. Zhao, H. Lu, and J. Wang, 2012: Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy, 37, 241249, https://doi.org/10.1016/j.renene.2011.06.023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gutiérrez, A., and R. G. Fovell, 2018: A new gust parameterization for weather prediction models. J. Wind Eng. Ind. Aerodyn., 177, 4559, https://doi.org/10.1016/j.jweia.2018.04.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hahmann, A. N., and Coauthors, 2020: The making of the new European wind Atlas–Part 1: Model sensitivity. Geosci. Model Dev., 13, 50535078, https://doi.org/10.5194/gmd-13-5053-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harrouni, S., 2018: Using fractal dimension to evaluate wind gusts long-term persistence. 2018 Second European Conf. on Electrical Engineering and Computer Science (EECS), Bern, Switzerland, IEEE, 416420.

    • Search Google Scholar
    • Export Citation
  • Hermans, E., T. Brijs, T. Stiers, and C. Offermans, 2006: The impact of weather conditions on road safety investigated on an hourly basis. Proc. 85th Annual Meeting of the Transportation Research Board, Washington, DC, Transportation Research Board, 17 pp., https://trid.trb.org/view/776722.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

  • Hess, R., 2020: Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst. Nonlinear Processes Geophys., 27, 473487, https://doi.org/10.5194/npg-27-473-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoffmann, L., and Coauthors, 2019: From ERA-Interim to ERA5: The considerable impact of ECMWF’s next-generation reanalysis on Lagrangian transport simulations. Atmos. Chem. Phys., 19, 30973124, https://doi.org/10.5194/acp-19-3097-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurlbut, M. M., and A. E. Cohen, 2014: Environments of northeast U.S. severe thunderstorm events from 1999 to 2009. Wea. Forecasting, 29, 322, https://doi.org/10.1175/WAF-D-12-00042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janić, M., 2015: Reprint of “Modelling the resilience, friability and costs of an air transport network affected by a large-scale disruptive event.” Transp. Res. Part A: Policy Pract., 81, 7792, https://doi.org/10.1016/j.tra.2015.07.012.

    • Search Google Scholar
    • Export Citation
  • Kamimura, K., B. Gardiner, S. Dupont, and J. Finnigan, 2019: Agent-based modelling of wind damage processes and patterns in forests. Agric. For. Meteor., 268, 279288, https://doi.org/10.1016/j.agrformet.2019.01.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kani, S. P., and M. Ardehali, 2011: Very short-term wind speed prediction: A new artificial neural network–Markov chain model. Energy Convers. Manage., 52, 738745, https://doi.org/10.1016/j.enconman.2010.07.053.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khanduri, A., and G. Morrow, 2003: Vulnerability of buildings to windstorms and insurance loss estimation. J. Wind Eng. Ind. Aerodyn., 91, 455467, https://doi.org/10.1016/S0167-6105(02)00408-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kretzschmar, R., P. Eckert, D. Cattani, and F. Eggimann, 2004: Neural network classifiers for local wind prediction. J. Appl. Meteor., 43, 727738, https://doi.org/10.1175/2057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kulkarni, M. A., S. Patil, G. Rama, and P. Sen, 2008: Wind speed prediction using statistical regression and neural network. J. Earth Syst. Sci., 117, 457463, https://doi.org/10.1007/s12040-008-0045-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lagerquist, R., A. McGovern, and T. Smith, 2017: Machine learning for real-time prediction of damaging straight-line convective wind. Wea. Forecasting, 32, 21752193, https://doi.org/10.1175/WAF-D-17-0038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Layer, M., and B. A. Colle, 2015: Climatology and ensemble predictions of nonconvective high wind events in the New York City metropolitan region. Wea. Forecasting, 30, 270294, https://doi.org/10.1175/WAF-D-14-00057.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Letson, F., R. J. Barthelmie, W. Hu, and S. C. Pryor, 2019: Characterizing wind gusts in complex terrain. Atmos. Chem. Phys., 19, 37973819, https://doi.org/10.5194/acp-19-3797-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Letson, F., R. J. Barthelmie, K. I. Hodges, and S. C. Pryor, 2021: Windstorms in the Northeastern United States. Nat. Hazards Earth Syst. Sci., 21, 20012020, https://doi.org/10.5194/nhess-21-2001-2021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, F., D. R. Chavas, K. A. Reed, and D. T. Dawson II, 2020: Climatology of severe local storm environments and synoptic-scale features over North America in ERA5 reanalysis and CAM6 simulation. J. Climate, 33, 83398365, https://doi.org/10.1175/JCLI-D-19-0986.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, G., and J. Shi, 2010: On comparing three artificial neural networks for wind speed forecasting. Appl. Energy, 87, 23132320, https://doi.org/10.1016/j.apenergy.2009.12.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, M. Z., K. Gopalakrishnan, H. Balakrishnan, and K. Pantoja, 2019: A spectral approach towards analyzing air traffic network disruptions. 13th USA/Europe Air Traffic Management Research and Development Seminar (ATM2019), Vienna, Austria, EUROCONTROL, https://web.mit.edu/hamsa/www/pubs/LiGopalakrishnanPantojaBalakrishnanATM2019.pdf.

    • Search Google Scholar
    • Export Citation
  • Lodge, A., and X.-H. Yu, 2014: Short term wind speed prediction using artificial neural networks. 2014 Fourth IEEE Int. Conf. on Information Science and Technology, Shenzhen, China, IEEE, 539542, https://doi.org/10.1109/ICIST.2014.6920535.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGovern, A., 2019: Making the black box more transparent: Understanding the physical implications of machine learning. Bull. Amer. Meteor. Soc., 100, 21752199, https://doi.org/10.1175/BAMS-D-18-0195.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mercer, A. E., M. B. Richman, H. B. Bluestein, and J. M. Brown, 2008: Statistical modeling of downslope windstorms in Boulder, Colorado. Wea. Forecasting, 23, 11761194, https://doi.org/10.1175/2008WAF2007067.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minola, L., F. Zhang, C. Azorin-Molina, A. S. Pirooz, R. 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mohammadi, M., J. Finnan, C. Baker, and M. Sterling, 2020: The potential impact of climate change on oat lodging in the UK and Republic of Ireland. Adv. Meteor., 2020, 4138469, https://doi.org/10.1155/2020/4138469.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mohandes, M. A., S. Rehman, and T. O. Halawani, 1998: A neural networks approach for wind speed prediction. Renewable Energy, 13, 345354, https://doi.org/10.1016/S0960-1481(98)00001-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • More, A., and M. Deo, 2003: Forecasting wind with neural networks. Mar. Struct., 16, 3549, https://doi.org/10.1016/S0951-8339(02)00053-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • National Oceanic and Atmospheric Administration (NOAA), 1998: Automated Surface Observing System (ASOS) user’s guide. Accessed 2 January 2021, https://www.Weather.gov/asos/.

    • Search Google Scholar
    • Export Citation
  • Nauslar, N. J., J. T. Abatzoglou, and P. T. Marsh, 2018: The 2017 North Bay and Southern California fires: A case study. Fire, 1, 118, https://doi.org/10.3390/fire1010018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Gorman, P. A., and J. G. Dwyer, 2018: Using machine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events. J. Adv. Model. Earth Syst., 10, 25482563, https://doi.org/10.1029/2018MS001351.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Papageorgiou, E. I., and K. Poczęta, 2017: A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks. Neurocomputing, 232, 113121, https://doi.org/10.1016/j.neucom.2016.10.072.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinto, J. G., F. Pantillon, P. Ludwig, M.-S. Déroche, G. Leoncini, C. C. Raible, L. C. Shaffrey, and D. B. Stephenson, 2019: From atmosphere dynamics to insurance losses: An interdisciplinary workshop on European storms. Bull. Amer. Meteor. Soc., 100, ES175ES178, https://doi.org/10.1175/BAMS-D-19-0026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Powell, M. D., 1993: Wind measurement and archival under the Automated Surface Observing System (ASOS): User concerns and opportunity for improvement. Bull. Amer. Meteor. Soc., 74, 615624, https://doi.org/10.1175/1520-0477(1993)074<0615:WMAAUT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pryor, S., and A. N. Hahmann, 2019: Downscaling wind. Oxford Research Encyclopedia of Climate Science, https://oxfordre.com/climatescience/page/about.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pryor, S. C., R. Conrick, C. Miller, J. Tytell, and R. Barthelmie, 2014: Intense and extreme wind speeds observed by anemometer and seismic networks: An eastern U.S. case study. J. Appl. Meteor. Climatol., 53, 24172429, https://doi.org/10.1175/JAMC-D-14-0091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pryor, S. C., R. C. Sullivan, and J. T. Schoof, 2017: Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks. Atmos. Chem. Phys., 17, 14 45714 471, https://doi.org/10.5194/acp-17-14457-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roberts, J., and Coauthors, 2014: The XWS open access catalogue of extreme European windstorms from 1979 to 2012. Nat. Hazards Earth Syst. Sci., 14, 24872501, https://doi.org/10.5194/nhess-14-2487-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., 2009: Visualizing multiple measures of forecast quality. Wea. Forecasting, 24, 601608, https://doi.org/10.1175/2008WAF2222159.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rohrer, M., O. Martius, C. Raible, and S. Brönnimann, 2020: Sensitivity of blocks and cyclones in ERA5 to spatial resolution and definition. Geophys. Res. Lett., 47, e2019GL085582, https://doi.org/10.1029/2019GL085582.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rootzén, H., and N. Tajvidi, 1997: Extreme value statistics and wind storm losses: A case study. Scand. Actuarial J., 1997, 7094, https://doi.org/10.1080/03461238.1997.10413979.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sallis, P. J., W. Claster, and S. Hernández, 2011: A machine-learning algorithm for wind gust prediction. Comput. Geosci., 37, 13371344, https://doi.org/10.1016/j.cageo.2011.03.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sarli, P., M. Abdillah, and A. Sakti, 2020: Relationship between wind incidents and wind-induced damage to construction in West Java, Indonesia. IOP Conf. Series: Earth Environ. Sci., 592, 012001, https://doi.org/10.1088/1755-1315/592/1/012001.

    • Search Google Scholar
    • Export Citation
  • Schmitt, C. V., IV, 2009: A quality control algorithm for the ASOS ice free wind sensor. 13th Conf. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface (IOAS-AOLS), Phoenix, AZ, Amer. Meteor. Soc.,12A.3, https://ams.confex.com/ams/89annual/techprogram/paper_145755.htm.

    • Search Google Scholar
    • Export Citation
  • Schultz, M., S. Lorenz, R. Schmitz, and L. Delgado, 2018: Weather impact on airport performance. Aerospace, 5, 109128, https://doi.org/10.3390/aerospace5040109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheridan, P., 2018: Current gust forecasting techniques, developments and challenges. Adv. Sci. Res., 15, 159172, https://doi.org/10.5194/asr-15-159-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 30193032, https://doi.org/10.1175/MWR2830.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spataru, A., R. Faggian, V. Sposito, and A. Docking, 2018: Agricultural land suitability analysis of metropolitan peri-urban areas now and into the future—Case study of City of Whittlesea, Melbourne, Australia. Proc. Fourth Practical Responses to Climate Change Conf.: “Climate Adaption 2018: Learn, Collaborate, Act,” Melbourne, Australia, Engineers Australia, 80–88, https://search.informit.org/doi/10.3316/informit.678810533942947.

    • 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.

    • Crossref
    • 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, 12981310, https://doi.org/10.1002/qj.2039.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sweeney, C., R. J. Bessa, J. Browell, and P. Pinson, 2020: The future of forecasting for renewable energy. Wiley Interdiscip. Rev.: Energy Environ., 9, e365, https://doi.org/10.1002/wene.365.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 71837192, https://doi.org/10.1029/2000JD900719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thorarinsdottir, T. L., and M. S. Johnson, 2012: Probabilistic wind gust forecasting using nonhomogeneous Gaussian regression. Mon. Wea. Rev., 140, 889897, https://doi.org/10.1175/MWR-D-11-00075.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toms, B. A., E. A. Barnes, and I. Ebert‐Uphoff, 2020: Physically interpretable neural networks for the geosciences: Applications to earth system variability. J. Adv. Model Earth Syst., 12, e2019MS002002, https://doi.org/10.1029/2019MS002002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toth, E., A. Brath, and A. Montanari, 2000: Comparison of short-term rainfall prediction models for real-time flood forecasting. J. Hydrol., 239, 132147, https://doi.org/10.1016/S0022-1694(00)00344-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., Y.-M. Zhang, J.-X. Mao, and H.-P. Wan, 2020: A probabilistic approach for short-term prediction of wind gust speed using ensemble learning. J. Wind Eng. Ind. Aerodyn., 202, 104198, https://doi.org/10.1016/j.jweia.2020.104198.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. International Geophysics Series, Vol. 100, Academic Press, 704 pp.

    • Search Google Scholar
    • Export Citation
  • Wright, J., 1994: Surface Aviation Observations: Federal Meteorological Handbook 1. FCM-H1-1994. Federal Coordinator for Meteorological Services and Supporting Research, 87 pp.

    • Search Google Scholar
    • Export Citation
  • Yadav, A., and K. Sahu, 2017: Wind forecasting using artificial neural networks: A survey and taxonomy. Int. J. Res. Sci. Eng., 3, 148155.

    • Search Google Scholar
    • Export Citation
  • Zeverte-Rivza, S., D. Popluga, and L. Berzina, 2017: Evaluation of risks in agriculture in the context of climate change. 17th Int. Multidisciplinary Scientific GeoConf.: SGEM, Albena, Bulgaria, Bulgarian Academy of Sciences, 417424.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., and C. Proppe, 2020: Risk assessment of road vehicles under wind gust excitation. J. Comput. Nonlinear Dyn., 15, 101004, https://doi.org/10.1115/1.4047638.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Do Machine Learning Approaches Offer Skill Improvement for Short-Term Forecasting of Wind Gust Occurrence and Magnitude?

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  • 1 a Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York
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Abstract

Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s−1) and damaging (≥25.7 m s−1) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein.

Significance Statement

Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.

© 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 authors: Jacob Coburn, jjc457@cornell.edu; Sara C. Pryor, sp2279@cornell.edu

Abstract

Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s−1) and damaging (≥25.7 m s−1) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein.

Significance Statement

Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.

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Corresponding authors: Jacob Coburn, jjc457@cornell.edu; Sara C. Pryor, sp2279@cornell.edu

Supplementary Materials

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