Abstract
Abstract. Wind gusts are extreme events which can cause severe damage. Gusts can reach significant values even during medium winds. However, numerical atmospheric models are designed to reproduce average wind speed, not gusts. There are several approaches to estimating wind gusts. Seven different methods are applied to WRF-ARW model output. Results are compared to high-frequency wind speed measurements using ultrasonic anemometers and temperature profiler measurement at the same point in Moscow. Data gathered from synoptic station network over the European part of Russia were also included in the analysis to increase the statistics. None of the wind gust estimation methods shows best results at every skill score. The proposed hybrid method shows good balance between the probability of detection and the false alarm ratio estimates.
Highlights
Even when average wind speed is low gusts can reach significant values
Our preliminary study showed the first Turbulent kinetic energy (TKE) method to give good results
To combine advantages of both groups of methods we propose a hybrid method: U + 3√q, Ri > 0 wge =
Summary
Even when average wind speed is low gusts can reach significant values. Gusts carry a high amount of wind energy and can cause severe damage and affect transport functioning. The greatest damage from storms is usually caused by strong wind gusts leading to constructions collapse and falling trees. Major damage appeared because of tornoff roofs, weak constructions, breakage of power lines, trees falling on cars – the reason for all of this was the strong wind. Forecast of strong wind gusts can help to organize preventive actions (strengthening structures, warning people, etc.) to reduce damage, so realistically forecasting wind gusts is a very important task of numerical meteorology. The undoubted advantage of the second approach is that these methods help to better understand the nature and mechanisms of the formation of this natural phenomenon. Let us analyse this group of methods in more detail
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