Articles | Volume 15
https://doi.org/10.5194/asr-15-159-2018
https://doi.org/10.5194/asr-15-159-2018
31 Jul 2018
 | 31 Jul 2018

Current gust forecasting techniques, developments and challenges

Peter Sheridan

Cited articles

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Antonakis, A., Lone, M., and Cooke, A.: Neural Network Based Dynamic Model and Gust Identification System for the Jetstream G-NFLA, in: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, 1138–1153, 2016. a
Baker, L. H., Gray, S. L., and Clark, P. A.: Idealised simulations of sting-jet cyclones, Q. J. Roy. Meteor. Soc., 140, 96–110, 2014. a, b
Beljaars, A. C. M.: The influence of sampling and filtering on measured wind gusts, J. Atmos. Ocean. Tech., 4, 613–626, 1987. a, b
Boutle, I. A., Eyre, J. E. J., and Lock, A. P.: Seamless Stratocumulus Simulation across the Turbulent Gray Zone, Mon. Weather Rev., 142, 1655–1668, 2014. a
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Short summary
Gusts represent short-lived extremes within the time variation of the wind. Despite their short duration, they are responsible for the majority of damage and disruption due to wind. This study reviews new literature and innovative methods regarding the forecasting of gusts for weather and climate applications, the challenges and benefits that come with increasing resolution in computer models of the weather, and areas, such as wind power, where gust forecasting attains increasing importance.