Adaptive Wind Gust and Associated Gust-factor Model for the Gust-producing Weather over the Northern South China Sea

Wind gusts are common environmental hazards that can damage buildings,bridges,aircraft,and cruise ships and interrupt electric power distribution,air traffic,waterway transport and port operations.Accurately predicting peak wind gusts in numerical models is essential for saving lives and preventing economic losses.This study investigates the climatology of peak wind gusts and their associated gust factors(GFs)using observations in the coastal and open ocean of the northern South China Sea(NSCS),where severe gust-producing weather occurs throughout the year.The stratified climatology demonstrates that the peak wind gust and GF vary with seasons and particularly with weather types.Based on the inversely proportional relationship between the GF and mean wind speed(MWS),a variety of GF models are constructed through least squares regression analysis.Peak gust speed(PGS)forecasts are obtained through the GF models by multiplying the GFs by observed wind speeds rather than forecasted wind speeds.The errors are thus entirely due to the representation of the GF models.The GF models are improved with weather-adaptive GFs,as evaluated by the stratified MWS.Nevertheless,these weather-adaptive GF models show negative bias for predicting stronger PGSs due to insufficient data representation of the extreme wind gusts.The evaluation of the above models provides insight into maximizing the performance of GF models.This study further proposes a stratified process for forecasting peak wind gusts for routine operations.

Characteristics of daily extreme wind gusts on the Qinghai-Tibet Plateau, China

Severe wind is a major natural hazard and a main driver of deserdficadon on the Qinghai-Tibet Plateau. Generally, studies of Qinghai-Tibet Plateau＇s wind climatology focus on mean wind speeds and its gust speeds have been seldom investigated. Here, we used observed daily maximum gust speeds from a 95- station network over a 5-year period （2008-2012） to analyze the characteristics of extreme wind speeds and directions by fitting Weibull and Gumbel distributions. The results indicated the spatial distribution of extreme wind speeds and their direction on the Qinghai-Tibet Plateau is highly variable, with its western portion prone to greater mean speeds of extreme wind gusts than its eastern portion. Maximum extreme wind speeds of 30.9, 33.0, and 32.2 m/s were recorded at three stations along the Qinghai Tibet Railway. Severe winds occurred mostly from November to April, caused primarily by the westerly jet stream. Terrain greatly enhances the wind speeds. Our spatial analysis of wind speed data showed that the wind speeds increased exponentially with an increasing altitude. We also assessed the local wind hazard by calculating the return periods of maximum wind gusts from the observational data based on the statistical extreme value distributions of these wind speeds. Further attention should be given to those stations where the yearly maximum daily extreme wind speed increased at a rate greater than that of mean value of daily extreme wind speeds. Severe extreme wind events in these regions of the plateau are likely to become more frequent. Consequently, building structural designers working in these areas should use updated extreme wind data rather than relying on past data alone.

Operational Safety Assessment of a High-Speed Train Exposed to the Strong Gust Wind

The operational safety characteristics of trains exposed to a strong wind have caused great concern in recent years.In the present paper,the effect of the strong gust wind on a high-speed train is investigated.A typical gust wind model for any wind angle,named“Chinese hat gust wind model”,was first constructed,and an algorithm for computing the aerodynamic loads was elaborated accordingly.A vehicle system dynamic model was then set up in order to investigate the vehicle system dynamic characteristics.The assessment of the operational safety has been conducted by means of characteristic wind curves(CWC).As some of the parameters of the wind-train system were difficult to measure,we also investigated the impact of the uncertain system parameters on the CWC.Results indicate that,the descending order of the operational safety index of the vehicle for each wind angle is 90°-60°-120°-30°-150°,and the worst condition for the operational safety occurs when the wind angle reaches around 90°.According to our findings,the gust factor and aerodynamic side force coefficient have great impact on the critical wind speed.Thus,these two parameters require special attention when considering the operational safety of a railway vehicle subjected to strong gust wind.

Structures and Characteristics of the Windy Atmospheric Boundary Layer in the South China Sea Region during Cold Surges

An observational analysis of the structures and characteristics of a windy atmospheric boundary layer during a cold air outbreak in the South China Sea region is reported in this paper. It is found that the main structures and characteristics are the same as during strong wind episodes with cold air outbreaks on land. The high frequency turbulent fluctuations （period 〈 1 min） are nearly random and isotropic with weak coherency, but the gusty wind disturbances （1 rain〈period 〈 10 min） are anisotropic with rather strong coherency. However, in the windy atmospheric boundary layer at sea, compared with that over land, there are some pronounced differences： （1） the average horizontal speed is almost independent of height, and the vertical velocity is positive in the lower marine atmospheric boundary layer; （2） the vertical flux of horizontal momentum is nearly independent of height in the low layer indicating the existence of a constant flux layer, unlike during strong wind over the land surface; （3） the kinetic energy and friction velocity of turbulent fluctuations are larger than those of gusty disturbances; （4） due to the independence of horizontal speed to height, the horizontal speed itself （not its vertical gradient used over the land surface） can be used as the key parameter to parameterize the turbulent and gusty characteristics with high accuracy.

Probabilistic Wind Gust Forecasting during the 2022 Beijing Winter Olympics

The probabilistic forecast of wind gusts poses a significant challenge during the post-processing of numerical model outputs.Comparative analysis of probabilistic forecasting methods plays a crucial role in enhancing forecast accuracy.Within the context of meteorological services for alpine skiing at the 2022 Beijing Winter Olympics,The ECMWF ensemble products were used to evaluate six post-processing methods.These methods include ensemble model output statistics(EMOS),backpropagation neural networks(BP),particle swarm optimization algorithms with backpropagation neural networks(PSO),truncated normal distributions,truncated logarithmic distributions,and generalized extreme value(GEV) distributions.The performance of these methods in predicting gust probabilities at five observation points along a ski track was compared.All six methods exhibited a substantial reduction in forecast errors compared to the original ECMWF products;however,the ability to correct the model forecast results varied significantly across different wind speed ranges.Specifically,the EMOS,truncated normal distribution,truncated logarithmic distribution,and GEV distribution demonstrated advantages in low wind-speed ranges,whereas the BP and PSO methods exhibit lower forecast errors for high wind-speed events.Furthermore,this study affirms the rationality of utilizing the statistical characteristics derived from ensemble forecasts as probabilistic forecast factors.The application of probability integral transform(PIT) and quantile–quantile(QQ) plots demonstrates that gust variations at the majority of observation sites conform to the GEV distribution,thereby indicating the potential for further enhanced forecast accuracy.The results also underscore the significant utility of the PSO hybrid model,which amalgamates particle swarm optimization with a BP neural network,in the probabilistic forecasting of strong winds within the field of meteorology.

Observation of a Straight-Line Wind Case Caused by a Gust Front and Its Associated Fine-Scale Structures

A straight-line wind case was observed in Tianjin on 13 June 2005,which was caused by a gust front from a squall line.Mesoscale analyses based on observations from in-situ surface stations,sounding,and in-situ radar as well as fine-scale analyses based on observation tower data were performed.The mesoscale characteristics of the gust front determined its shape and fine-scale internal structures.Based on the scale and wavelet analyses,the fine-scale structures within the gust front were distinguished from the classical mesoscale structures,and such fine-scale structures were associated with the distribution of straight-line wind zones.A series of cross-frontal fine-scale circulations at the lowest levels of the gust front was discovered,which caused a relatively weak wind zone within the frontal strong wind zone.The downdraft at the rear of the head region of the gust front was more intense than in the classical model,and similar to the microburst,a series of vertical vortices propagated from the rear region to the frontal region.In addition,strong tangential fine-scale instability was detected in the frontal region.Finally,a fine-scale gust front model with straight-line wind zones is presented.

Evaluation of the wind energy potential of two south west sites in Nigeria

Wind resource assessment is a crucial first step in gauging the potential of a site to produce energy from wind turbines. In this paper, the wind energy potential of Abeokuta （07°03＇N, 03°19＇E） and Ijebu-Ode （06°47＇N, 03°58＇E）, two south west sites in Nigeria were examined. Twenty years （1990-2010） of monthly mean wind data from a 10m height were subjected to two-parameter Weibull analysis and other statistical analyses. The results show that the average annual mean wind speed variation for Abeokuta ranges from 2.2 to 5.0 m/s. For Ijebu-Ode, it ranges from 2.0 to 5.0m/s. The wind power density variation based on the Weibull analysis ranges from 4.26 to 24.51 W/m2 for Abeokuta and from 8.54 to 76.46 W/m2 for Ijebu-Ode. Ijebu-Ode was found to be the better of the two sites in terms of annual variation of mean wind speed.