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. For example, due to the storm over Moscow in May 2017, 16 people died, and according to insurance companies, the economic loss exceeded 25 million roubles (≈350000 EUR). Major damage appeared because of torn-off roofs, weak constructions, breakage of power lines, trees falling on cars – the reason for all of this was the strong wind. Early 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. However, there is a variety of methods for wind gust estimation. There is a need to know the applicability of the method used in a particular case. In this study we analyse different aspects of wind gust forecast approaches.

There are two ways to use numerical model output: to perform statistical postprocessing or to create a physical model of wind gust formation. 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.

Dynamical wind gust estimation methods can be divided into two groups. Methods in the first group are based on the fact that wind gusts are related to atmospheric turbulence and therefore can be estimated on the basis of turbulent characteristics determined within the parameterization of the atmospheric boundary layer. Turbulent kinetic energy (TKE) represents a deviation of the instantaneous wind from the mean, so we can consider TKE as wind speed dispersion. From the assumption that the wind speed distribution is normal, the following formula can be proposed (this method is further referred to as TKE): wge=U+3σ=U+3q, where “wge” is the wind gust estimate, U is average wind speed, σ is the standard deviation of wind speed, and q is TKE. If there is no TKE in the model output, we can use the relation between TKE and friction velocity to estimate the variance . If we assume that the TKE in the model represents the maximum deviation of instant wind from the mean, we can get the following estimate (this method is further referred to as TKE-2): wge=U+2q. Another TKE-based parameterization was developed by (this method is further referred to as Schreur): wge=U1+grσ2qU. Based on the observation data fit: g=1.42+0.3013ln⁡990Ut-4rσ=1-0.069exp⁡-2.3Utzexp⁡-2.3Utz0.555, where t=3 s is gust duration and z=10 m is height above ground.

Methods in the second group are based on an assumption that gusts are the result of air particles deflection from higher levels and carry speed from those levels. proposed to use the wind speed from the atmospheric boundary layer height (this method is further referred to as PBLH).

In the case of deep convection, vertical motion as well as precipitation should be taken into account (this method is further referred to as Nakamura): wge=α∫0H2gΔθθ+qrdz+βU(H), where H is boundary layer height, and qris rain mixing ratio, suggests α=0.25 and β=0.85.

suggests taking into account the energy of particles that can reach the surface (this method is further referred to as Brasseur): wge=maxUzp, for the levels zp satisfying Eq. (): 1zp∫0zpq(z)dz≥∫0zpgΔθv(z)Θv(z)dz, where θv is the virtual potential temperature.

The methods using TKE are not applicable to the prediction of gusts associated with strong convection. However, they work well in cases with the turbulence of mechanical origin. Our preliminary study showed the first TKE method to give good results. Otherwise, particle deflection methods are usually considered in cases of convection. The Brasseur method looks to be the most physically developed. To combine advantages of both groups of methods we propose a hybrid method: wge=U+3q,Ri>0maxUzp,Ri≤0, where zp satisfies Eq. (). We use the Richardson number Ri to separate the types of the instability of the atmospheric boundary layer.

Figure  shows the example of high-frequency wind speed measurements and the forecast of wind speed and gusts calculated by seven parameterizations. Mean wind speed is quite well reproduced by the model. Figure  shows an interval U±3q too; thus in the TKE method q is taken as the standard deviation of wind speed. Analysis of 1-year data (0–48 h lead-time forecasts starting every 24 h) showed that 74 % of all measurements fit this interval. Most deviations from observations are due to time-phase errors of mean wind speed. Though most methods capture the strongest gust in the shown period, they overestimate them most of the time.

Figure  presents the measured gust factor and the potential temperature gradients in the lower 100 m layer. It does not show a direct connection between them. However gust factor spreading increases if the potential temperature gradient decreases.

Figure  shows skill scores of 24 h forecasts of wind gusts exceeding 15 m s-1 for the three winter and three summer months in 2016. Methods using the principle of deviation from higher levels show a higher probability of detection of wind gusts but also a greater number of false alarms. ETS and PSS are low for all methods, especially for summer. Lower performance in summer can be due to the difficulty of describing convective winds. Convective events might not be captured by the model due to its resolution. ETS is slightly better for TKE using methods since they have a lower false alarm ratio. PSS is slightly greater for particle deflection methods since they have higher probability of detection. Despite the fact that the hybrid method does not show the best estimate, it has a greater probability of detection than TKE methods and a lower false alarm ratio then particle deflection methods. It can be seen that none of the existing methods provide high performance in every skill score, so there is a need for further investigation of gusts.

Seven wind gust estimation methods are applied to the WRF-ARW model output. A new hybrid method was proposed. It shows a good balance between the probability of detection and the false alarm ratio estimates. The conducted research shows the necessity of further studying the mechanisms of formation and methods for forecasting of wind gusts.