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Accurate prediction of spatial-temporal extreme wind gust is vital for the wind farm dynamic regulation, the floating wind turbine deployment and its early warning. Deep-learning approaches have been applied for wind prediction to alleviate the computational challenges of traditional numerical models. Yet, most previous studies emphasized the prediction accuracy only employing location-specific dataset, such methodologies are site-specific and ignore the importance of spatial-temporal fidelity. Furthermore, the Recurrent Neural Networks (RNN)-based approach previous employed exhibit low efficiency in terms of model convergence and on the aspect of practical engineering purposes. This study firstly proposed the wind gust prediction net (WGPNet), using residual learning with attention modulations to predict the instantaneous spatial-temporal wind gust in the West Pacific region with great potential wind-energy. And a public reanalysis dataset with very high resolution (0.25° x 0.25°) was employed to verify the proposed method under different criteria. The overall RMSE of predicted gust fields obtained by the proposed method dropped to 0.18 m/s. Comprehensive discussions with both temporal and spatial perspective, revealing that the proposed model can offer an accurate 2D wind gust prediction along timeline (the PCC equals to 0.98).

Modelling wind speed and trends helps in estimating the energy produced from wind farms. This study uses statistical models to analyze wind patterns in Melbourne, Australia. Three-hourly wind data during 2004-2008 was obtained from the Australian Government, Bureau of Meteorology, for Avalon Airport, Essendon Airport, Point Wilson, and View Bank stations. A logistic regression model was used to investigate the pattern of 3-hourly winds and gust prevalence while a linear regression model was applied to investigate wind speed trends. The 3-hour periods of the day, month, and year were used as the independent variables in the analysis. At four stations, wind speed and wind gust prevalence were mostly high between 9 AM and 6 PM. The monthly wind and wind gust prevalence were high from November to January while the highest annual prevalence occurred in 2007. The wind speed increased from 7 AM to 6 PM within which the maximum occurred. The monthly wind speed increased from November to January where it attained the maximum, decreasing to a minimum in May. The annual mean wind speed was highest in 2007.

Severe wind gusts produced by squall lines are difficult to monitor and forecast. This paper assessed and improved the physics-based Brasseur WGE (wind gust estimate) method for diagnosing wind gust of squall lines by coupling the WGE methods with the WRF (Weather Research and Forecasting) model. The simulation results show that the Brasseur WGE method accurately captured the strong gust feature with 32 m·s −1 maximum wind speed during the disastering Shipwreck event occurred over Yangtze River on 1 June 2015, but overestimated the extended area of severe gust speeds. Analysis of the kinematic structure and boundary-layer conditions of the squall line confirmed the theoretical applicability of the Brasseur WGE method for squall lines. A novel gust-front-area limiting method was introduced to modify the Brasseur WGE method, which effectively reduces its gust wind overestimation area. Furthermore, five squall line events occurred in the middle China during 2021 were simulated to test the modified WGE method and the results exhibit significant improvements to the wind gust forecasts, with an average false alarm rate decreased from 0.89 to 0.54, and the critical success index(CSI) increased from 0.1 to 0.4.

The ERA5 reanalysis product has been compared with hourly near-surface wind speed and gust observations across Sweden for 2013–2017. ERA5 shows closer agreement than the previous ERA-Interim reanalysis with regard to both mean wind speed and gust measurements, although significant discrepancies are still found for inland and mountainous regions. Therefore, attempts have been made to improve formulations of the gust parametrization used in ERA5 by adding an elevation-dependency and by adjusting the convective gust contribution. Major improvements, especially over mountain regions, are achieved when the elevation differences among the stations are considered. Closer agreement between the observed and parametrized gusts is reached when the convective gust contribution is also tuned. The newly designed gust parametrization was also tested for Norway, which is characterized by more complex topography. Wind gusts from the selected Norwegian stations are more realistically simulated when both the elevation-dependency and the tuned convective contribution are implemented, although the parametrized gusts are still negatively biased. Such biases are not explained by the different in gust duration in recorded wind gusts between Sweden and Norway.

Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only a few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing that can be divided in three groups: state-of-the-art postprocessing techniques from statistics [ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression], established machine learning methods (gradient-boosting extended EMOS, quantile regression forests), and neural network–based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using 6 years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer.

This study analyses for the first time observed surface mean wind speed (SWS) and gusts over the Iberian Peninsula (IP) in the frequency domain for 1961–2019, with the goal of exploring sources of predictability in the interannual and decadal scales. The main result is the high significant correlation between surface winds and the stratospheric polar vortex for periods close to 1 year with a time lag of about 2–3 months with respect to the antiphase, that is, a negative correlation in which the polar vortex modulates winds in the region. Furthermore, we found that the SWS and gusts are decoupled for periods between 9 and 11 years with a marked seasonal dependence in its intensity. Finally, we detected discrepancies between the spectra shown by surface winds from observations and ERA5‐Land reanalysis, suggesting that simulated data do not accurately reproduce the variability of surface wind speeds. Plain Language Summary In the climate system, there are oscillations that are repeated with specific time periods over time; the best known example is the El Niño‐Southern Oscillation, which affects much of the world's climate. In this study, we apply a spectral analysis to: (a) characterize the spectrum of near‐surface mean wind speed and gusts in the Iberian Peninsula; (b) relate the oscillations of both wind variables with those of other parameters of the climate system; and, (c) identify sources of predictability. The most outstanding result is the high significant correlation between the mean wind speed and gusts with the stratospheric polar vortex with a time lag of about 2–3 months. This means that with an anomalously weak (strong) polar vortex we might expect strong (weak) surface winds 2–3 months later. This study has direct socioeconomic and environmental implications for for example, wind energy production, agriculture and hydrology, pollutant dispersion, among others, as it could predict interannual‐ to decadal‐scales wind speed behavior in the region. Key Points Surface winds correlate with the stratospheric polar vortex in periods close to 1 year, with a 2–3 months lag with respect to the anti‐phase Surface mean wind speed and gusts are decoupled for periods between 9 and 11 years, being greater in summer and disappearing in winter Spectral analysis showed differences between observed and ERA5‐Land reanalysis surface winds

In this paper, a new analytical approach is proposed to investigate the electrical and mechanical behavior of a low-power permanent magnet synchronous generator (PMSG) in the presence of a wind gust. The proposed model for the wind gust and wind power system was analytically investigated using the optimal auxiliary functions method (OAFM), which has proven to be a reliable tool. The reaction of the system to a wind gust was explicitly obtained, which is useful for stability analysis, protection issues, and risk assessment concerning the PMSG. A substantial reduction of computations in analytically analysing a complicated dynamical system is ensured by this new approach, through some auxiliary functions and convergence-control parameters.

This study presents rare measurements and analysis of a nocturnal thunderstorm downburst on the 213 m tall Cabauw tower in The Netherlands. The event occurred on 12 March 2008 between 02:00 and 03:00 UTC and was measured using four ultrasonic 10-Hz anemometers positioned at 3, 60, 100, and 180 m above ground level. 1-second gusts in the outflow exceeded 30 m s −1 at 60 m and above. This wind event was accompanied by an abrupt change of wind direction from southwest to west. While the shift in wind direction corresponded with the change of upwind surface roughness, the time series of turbulence intensity and other turbulence characteristics were not affected. The statistical properties of this event were compared against the largest European database of thunderstorm winds measured in the Mediterranean. The study also demonstrated that primary and secondary vortex structures—secondary vortex being rarely observed in actual downbursts—developed at the forward edge of the cold outflow. The estimated diameter of the downdraft was 1200 m at 70 m above ground. The measured velocity profiles and friction velocity were compared against theoretical predictions of the Monin-Obukhov Similarity Theory (MOST). MOST without stratification adjustment overestimated measured friction velocity twofold. Alternative values for surface roughness during the outflow were derived based on the measured friction velocity and MOST-based fit of measured velocity profiles. Ceilometer and radar measurements were supplementary data in this analysis.

Information on wind gusts is needed for assessment of wind-induced damage and risks to safety. The measurement of wind gust speed requires a high temporal resolution of the anemometer system, because the gust is defined as a short-duration (seconds) maximum of the fluctuating wind speed. Until the digitalization of wind measurements in the 1990s, the wind gust measurements suffered from limited recording and data processing resources. Therefore, the majority of continuous wind gust records date back at most only by 30 years. Although the response characteristics of anemometer systems are good enough today, the traditional measurement techniques at weather stations based on cup and sonic anemometers are limited to heights and regions where the supporting structures can reach. Therefore, existing measurements are mainly concentrated over densely-populated land areas, whereas from remote locations, such as the marine Arctic, wind gust information is available only from sparse coastal locations. Recent developments of wind gust measurement techniques based on turbulence measurements from research aircraft and from Doppler lidar can potentially provide new information from heights and locations unreachable by traditional measurement techniques. Moreover, fast-developing measurement methods based on Unmanned Aircraft Systems (UASs) may add to better coverage of wind gust measurements in the future. In this paper, we provide an overview of the history and the current status of anemometry from the perspective of wind gusts. Furthermore, a discussion on the potential future directions of wind gust measurement techniques is provided.

Investigating Amazonian intense wind gusts and their environments is essential to better understand the drivers and impacts of severe convection that can reshape forest structure, increase tree mortality, and threaten ecosystems and communities. This study presents the first multi‐decadal (2000–2024) assessment of intense convective wind gusts ≥15ms−1$\\left(\\ge 15\\,\\mathrm{m}\\,{\\mathrm{s}}^{-\\mathrm{1}}\\right)$across the entire Brazilian Amazon, using hourly observations from surface weather stations. Intense gusts occur frequently across the Amazon, particularly during the dry‐to‐wet transition months of September and October, peaking in the mid‐ to late afternoon. Thermodynamic factors favor intense gust generation during the dry and transition seasons, with environments characterized by higher downdraft convective available potential energy, steeper low‐level lapse rates, and higher lifting condensation levels, particularly in southern Amazon.