Daily Maximum Wind Speed Research Articles

Estimation of daily reference evapotranspiration using artificial neural network for Raipur region in Chhattisgarh

This study presents estimation of daily reference evapotranspiration using artificial neural network (ANN) for Raipur region of Chhattisgarh. An ANN is a flexible mathematical structure, which is capable of identifying complex nonlinear relationships between input and output data sets. To test artificial neural networks (ANN) for estimating reference evapotranspiration with limited climatic data, comparison has been made with Penman Montieth (PM) method. Data on daily maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), wind speed (WS) and sunshine hours (SSH) of 31 years were collected from the observatory of Department of Agrometeorology, IGKV, Raipur. Four different models have been developed based on input combination viz: M1 (Tmax, Tmin, RH (I), RH (II), WS and SSH), M2 (Tmax, RH (I), RH (II), WS and SSH), M3 (Tmax, WS and SSH) and M4 (Tmax and SSH). The analysis was carried out in MATLAB R2014b software. Performance evaluation of the models have been carried out by calculating mean absolute deviation (MAD), root mean square error (RMSE), absolute Prediction Error (APE), coefficient of correlation (CC), coefficient of efficiency (CE) and Index of Agreement (IOA). Upon comparison the Model M1R showed the highest CC, CE and IOA value as 99.7, 99.4 and 99.8 during training and highest CC, CE and IOA value as 99.7, 99.5 and 99.6 during testing period. The developed ANN models may therefore be adopted for estimating ET0 in the region with reasonable degree of accuracy.

Estimation of daily PM2.5 concentration and its relationship with meteorological conditions in Beijing

When investigating the impact of air pollution on health, particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) is considered more harmful than particulates of other sizes. Therefore, studies of PM2.5 have attracted more attention. Beijing, the capital of China, is notorious for its serious air pollution problem, an issue which has been of great concern to the residents, government, and related institutes for decades. However, in China, significantly less time has been devoted to observing PM2.5 than for PM10. Especially before 2013, the density of the PM2.5 ground observation network was relatively low, and the distribution of observation stations was uneven. One solution is to estimate PM2.5 concentrations from the existing data on PM10. In the present study, by analyzing the relationship between the concentrations of PM2.5 and PM10, and the meteorological conditions for each season in Beijing from 2008 to 2014, a U-shaped relationship was found between the daily maximum wind speed and the daily PM concentration, including both PM2.5 and PM10. That is, the relationship between wind speed and PM concentration is not a simple positive or negative correlation in these wind directions; their relationship has a complex effect, with higher PM at low and high wind than for moderate winds. Additionally, in contrast to previous studies, we found that the PM2.5/PM10 ratio is proportional to the mean relative humidity (MRH). According to this relationship, for each season we established a multiple nonlinear regression (MNLR) model to estimate the PM2.5 concentrations of the missing periods.

Development of a Fire Weather Index Using Meteorological Observations within the Northeast United States

AbstractA fire weather index (FWI) is developed using wildfire occurrence data and Automated Surface Observing System weather observations within a subregion of the northeastern United States (NEUS) from 1999 to 2008. Average values of several meteorological variables, including near-surface temperature, relative humidity, dewpoint, wind speed, and cumulative daily precipitation, are compared on observed wildfire days with their climatological average (“climatology”) using a bootstrap resampling approach. Average daily minimum relative humidity is significantly lower than climatology on wildfire occurrence days, and average daily maximum temperature and average daily maximum wind speed are slightly higher on wildfire occurrence days. Using the potentially important weather variables (relative humidity, temperature, and wind speed) as inputs, different formulations of a binomial logistic regression model are tested to assess the potential of these atmospheric variables for diagnosing the probability of wildfire occurrence. The FWI is defined using probabilistic output from the preferred binomial logistic regression configuration. Relative humidity and temperature are the only significant predictors in the binomial logistic regression. The binomial logistic regression model is reliable and has more probabilistic skill than climatology using an independent verification dataset. Using the binomial logistic regression output probabilities, an FWI is developed ranging from 0 (minimum potential) to 3 (high potential) and is verified independently for two separate subdomains within the NEUS. The climatology of the FWI reproduces observed fire occurrence probabilities between 1999 and 2008 over a subdomain of the NEUS.

The Paths of Extratropical Cyclones Associated with Wintertime High-Wind Events in the Northeastern United States

AbstractThis study analyzes the association between wintertime high-wind events (HWEs) in the northeastern United States and extratropical cyclones. Sustained wind maxima in the daily summary data from the National Climatic Data Center’s integrated surface database are analyzed for 1979–2012. For each station, a generalized Pareto distribution is fit to the upper tail of the daily maximum wind speed data, and probabilistic return levels at 1, 3, and 5 yr are derived. Wind events meeting the return-level criteria are termed HWEs. The HWEs occurring on the same day are grouped into simultaneous wind exceedance dates, termed multistation events. In a separate analysis, extratropical cyclones are tracked using ERA-Interim. The multistation events are associated with the extratropical cyclone tracks on the basis of cyclone proximity on the day of the event. The multistation wind events are found to be most often associated with cyclones traveling from southwest to northeast, originating west of the Appalachian Mountains. To quantify the relative frequency of the strong-wind-associated cyclones, the full set of northeastern cyclone tracks is separated on the basis of path, using a crosshairs algorithm designed for this region. The tracks separate into an evenly distributed set of four pathways approaching the northeastern United States: from due west, from the southwest, and from the southeast and storms starting off the coast north of the Carolinas. Using the frequency of the tracks in each of the pathways, it is shown that the storms associated with multistation wind events are most likely to approach the northeastern United States from the southwest.

Determination of extreme wind values using the Gumbel distribution

An investigation of what types of extreme wind values are the most suitable for the Gumbel distribution was conducted on Jeju Island, South Korea. Three measurements and reference sites that have different topographical conditions were selected to clarify the influence of the topographical conditions on extreme wind values. Because long-term wind data are required to estimate extreme wind speeds with a higher degree of confidence, the MCP (Measure-Correlate-Predict) technique was applied to generate ten-year wind data from one-year measurements. Extreme wind values were sampled from the ten-year wind data based on periodic maximum wind speeds and the wind speeds above a threshold, which were divided into three types each. The six types of extremes were examined to determine which type was more suitable for the Gumbel distribution under the assumption that the Gringorten formula is the best plotting position method for the Gumbel distribution. In addition, we predicted the appropriate wind turbine class for the measurement sites in compliance with IEC international standards. As a result, daily maximum wind speeds were the best extreme wind values for the Gumbel distribution in the six types irrespective of the topographical conditions. The site that has a higher average wind speed was estimated to have a higher extreme wind speed, indicating that a higher wind turbine class was needed at the site.

Differential effects of typhoons on ichthyotoxic Cochlodinium polykrikoides red tides in the South Sea of Korea during 2012–2014

The harmful dinoflagellate Cochlodinium polykrikoides is known to cause fish death by gill-clogging when its abundance exceeds approximately 1000 cellsml−1. Thus, red tides of this dinoflagellate have caused considerable loss in the aquaculture industry worldwide. Typhoons carrying strong winds and heavy rains may alter the process of red tide events. To investigate the effects of typhoons on C. polykrikoides red tides, daily variations in the abundance of C. polykrikoides, and wind speeds in three study areas in the South Sea of Korea were analyzed during the periods of C. polykrikoides red tides and the passage of 14 typhoons during 2012–2014. The typhoons differentially affected Cochlodinium red tides during the study period, and the daily maximum wind speed generated by the typhoon was critical. Four typhoons with daily maximum wind speeds of >14ms−1 eliminated Cochlodinium red tides, while three typhoons with daily maximum wind speed of 5–14ms−1 only lowered the abundance. However, other typhoons with daily maximum wind speeds of <5ms−1 had no marked effect on the Cochlodinium abundance. Therefore, typhoons may sometimes eliminate C. polykrikoides red tide events, or reduce cell abundances to a level that is not harmful to caged fish cultivated in aquaculture industries. Thus, typhoons should be considered when compiling red tide dynamics and fish-kill models.

Changes of dry-hot wind hazard for winter wheat in the typical agricultural areas of Huang-Huai-Hai Plain: A case of Shangqiu

Climate change is producing significant impacts on global agricultural production.Climatic variations affect crop production, and tend to be a key constraint of agricultural production, primarily on continuous increase of winter wheat yields worldwide. The great uncertainties in predicting the effects of climate change on wheat production are most likely due to less understanding of the responses of wheat production to extreme climatic factors, e.g.high temperatures, low humidity as well as high wind speed. Dry- hot wind hazard represents one of the main natural disasters for Chinese winter wheat production, especially in the HuangHuai- Hai Plain. However, there still exist great uncertainties of the effects of dry- hot wind hazard on winter wheat production, mainly due to the gaps of long- term observations.Therefore, we selected Shangqiu as the case study area to determine the occurrence regularity of dry-hot wind hazard on winter wheat production in the Huang-Huai-Hai Plain. We analyzed regional meteorological data with daily resolution in the later growth stage of winter wheat during the period 1963- 2012. In accordance with the meteorological industry standards of "Disaster Grade of Dry-hot Wind for Wheat" by the China Meteorological Administration, we synthetically analyzed the distribution of annual average days of dry-hot wind in winter wheat growing seasons and the associated responses to the climate change. Hence, the relationship between dry-hot wind times and winter wheat yields was examined. The results showed that the annual average days of light and severe dry-hot wind tended to decline in recent 50 years. Great inter- annual variations of light and severe dry- hot wind were observed. The significant interannual variations were related with the corresponding meteorological conditions of temperature, moisture and wind speed. The most serious damages of both light and severe dryhot wind occurred in the 1960 s, while less damages appeared in the 1980 s and the last decade,which could be also explained by the corresponding temperature, moisture and wind speed conditions. From 1963 to 2012, a climatic mutation point of daily maximum temperature was found in 1972(p0.05). The wind speed at 14:00 and the relative humidity at 14:00 were closely related to the hazard. A climatic mutation point of the wind speed at 14:00 was found around 1984, and climatic mutation of the relative humidity at 14:00 was found in 1981(p0.05). Daily maximum temperature and wind speed at 14:00 and the relative humidity at 14:00played a major role in the decreasing trend of dry- hot wind disaster, and the significant decrease of wind speed at 14:00 was a main factor in Shangqiu. Dry- hot wind hazard had a strong response to climate change. Yields of winter wheat were negatively correlated with annual average days of dry-hot wind in Shangqiu(p0.05). In actual practices, great attention should be paid to the defense of dry- hot wind for winter wheat production. Thus, the most effective measures have to be taken to enhance the resistance of winter wheat to dry-hot wind hazard through improving field microclimate condition.

Bayesian Nonparametric Calibration and Combination of Predictive Distributions

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan, R. and Gneiting, T. (2010) and Gneiting, T. and Ranjan, R. (2013), we use infinite beta mixtures for the calibration. The proposed Bayesian nonparametric approach takes advantage of the flexibility of Dirichlet process mixtures to achieve any continuous deformation of linearly combined predictive distributions. The inference procedure is based on Gibbs sampling and allows accounting for uncertainty in the number of mixture components, mixture weights, and calibration parameters. The weak posterior consistency of the Bayesian nonparametric calibration is provided under suitable conditions for unknown true density. We study the methodology in simulation examples with fat tails and multimodal densities and apply it to density forecasts of daily S&P returns and daily maximum wind speed at the Frankfurt airport.

Estimation of Daily Reference Evapotranspiration (ET0) in the North of Algeria Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) Models: A Comparative Study

The aim of this study is to model the daily reference evapotranspiration (ET0) in the Mediterranean region of Algiers, Algeria country, using Adaptive Neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) models. Various daily climatic data, i.e. daily mean relative humidity, sunshine duration, maximum, minimum and mean air temperature and wind speed obtained from Dar El Beida weather station, are used as inputs to the ANFIS and MLR models so as to estimate ET0. In order to find the optimal topology of the ANFIS, different architectures were trained and examined and the network with minimum Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and high Coefficient of Correlation (CC) has been selected as an optimal configuration. A comparison was conducted between the estimates provided by the ANFIS and by MLR. The results showed that ANFIS using the climatic data successfully estimated ET0 and the ANFIS simulated ET0 better than the MLR. Totally 2,193 daily samples were used for training the model, and 730 daily samples for testing and validation of the model. The developed ANFIS model for the ET0 modelling shows good performance with an MAE index in the range of 0.32–0.75, RMSE between 0.41 and 0.75 and the CC in the range of 0.80–0.96, which endows with high performance of predictive ANFIS system to make use for modelling daily reference evapotranspiration (ET0).

Comparison of non-homogeneous regression models for probabilistic wind speed forecasting

In weather forecasting, non-homogeneous regression (NR) is used to statistically post-process forecast ensembles in order to obtain calibrated predictive distributions. For wind speed forecasts, the regression model is given by a truncated normal (TN) distribution, where location and spread derive from the ensemble. This article proposes two alternative approaches which utilise the generalised extreme value (GEV) distribution. A direct alternative to the TN regression is to apply a predictive distribution from the GEV family, while a regime-switching approach based on the median of the forecast ensemble incorporates both distributions. In a case study on daily maximum wind speed over Germany with the forecast ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF), all three approaches significantly improve the calibration as well as the overall skill of the raw ensemble with the regime-switching approach showing the highest skill in the upper tail.

Wind storm loss estimations in the Canton of Vaud (Western Switzerland)

Abstract. A storm loss model that was first developed for Germany is applied to the much smaller geographic area of the canton of Vaud, in Western Switzerland. 24 major wind storms that struck the region during the period 1990–2010 are analysed, and outputs are compared to loss observations provided by an insurance company. Model inputs include population data and daily maximum wind speeds from weather stations. These measured wind speeds are regionalised in the canton of Vaud following different methods, using either basic interpolation techniques from Geographic Information Systems (GIS), or by using an existing extreme wind speed map of Switzerland whose values are used as thresholds. A third method considers the wind power, integrating wind speeds temporally over storm duration to calculate losses. Outputs show that the model leads to similar results for all methods, with Pearson's correlation and Spearman's rank coefficients of roughly 0.7. Bootstrap techniques are applied to test the model's robustness. Impacts of population growth and possible changes in storminess under conditions of climate change shifts are also examined for this region, emphasizing high shifts in economic losses related to small increases of input wind speeds.

The impact of differences in large-scale circulation output from climate models on the regional modeling of ozone and PM

Abstract. Climate change may have an impact on air quality (ozone, particulate matter) due to the strong dependency of air quality on meteorology. The effect is often studied using a global climate model (GCM) to produce meteorological fields that are subsequently used by chemical transport models. However, climate models themselves are subject to large uncertainties and fail to reproduce the present-day climate adequately. The present study illustrates the impact of these uncertainties on air quality. To this end, output from the SRES-A1B constraint transient runs with two GCMs, i.e. ECHAM5 and MIROC-hires, has been dynamically downscaled with the regional climate model RACMO2 and used to force a constant emission run with the chemistry transport model LOTOS-EUROS in a one-way coupled run covering the period 1970–2060. Results from the two climate simulations have been compared with a RACMO2-LOTOS-EUROS (RLE) simulation forced by the ERA-Interim reanalysis for the period 1989–2009. Both RLE_ECHAM and RLE_MIROC showed considerable deviations from RLE_ERA for daily maximum temperature, precipitation and wind speed. Moreover, sign and magnitude of these deviations depended on the region. The differences in average present-day concentrations between the simulations were equal to (RLE_MIROC) or even larger than (RLE_ECHAM) the differences in concentrations between present-day and future climate (2041–2060). The climate simulations agreed on a future increase in average summer ozone daily maximum concentrations of 5–10 μg m−3 in parts of Southern Europe and a smaller increase in Western and Central Europe. Annual average PM10 concentrations increased 0.5–1.0 μg m−3 in North-West Europe and the Po Valley, but these numbers are rather uncertain: overall, changes for PM10 were small, both positive and negative changes were found, and for many locations the two climate runs did not agree on the sign of the change. This illustrates that results from individual climate runs can at best indicate tendencies and should therefore be interpreted with great care.

Early warnings of extreme winds using the ECMWF Extreme Forecast Index

The European FP7 SafeWind Project aims at developing research towards a European vision of wind power forecasting, which requires advanced meteorological support concerning extreme wind events. This study is focused mainly on early warnings of extreme winds in the early medium-range. Three synoptic stations (airports) of North Germany (Bremen, Hamburg and Hannover) were considered for the construction of time series of daily maximum wind speeds. All daily wind extremes were found to be linked to very intense surface cyclonic circulation systems being advected mainly by southwest and northwest flow regimes. Overall, it becomes clear that the first indications of an extreme wind event might come from the ECMWF deterministic and/or probabilistic components capturing very intense weather systems (possible windstorms) in the medium term. For early warnings, all available EPS Extreme Forecast Index (EFI) formulations were used, by linking daily maximum wind speeds to EFI values for different forecast horizons. From all possible EFI schemes deployed for issuing early warnings, the highest skill was found for the Gust Factor formulation (EFI-10FGI). Using EFI-10FGI, the corresponding 99% threshold could provide an early warning for a considerable portion of the > 99% wind extremes, but not for all. By lowering this threshold the number of hits is increased until all extremes are captured (zero misses), although by doing so the number of false alarms increased significantly. Consequently, an optimal trade-off between hits and false alarms has to be made when setting different (critical) EFI thresholds. Copyright © 2012 Royal Meteorological Society

Applying stochastic small‐scale damage functions to German winter storms

Analyzing insurance‐loss data we derive stochastic storm‐damage functions for residential buildings. On district level we fit power‐law relations between daily loss and maximum wind speed, typically spanning more than 4 orders of magnitude. The estimated exponents for 439 German districts roughly range from 8 to 12. In addition, we find correlations among the parameters and socio‐demographic data, which we employ in a simplified parametrization of the damage function with just 3 independent parameters for each district. A Monte Carlo method is used to generate loss estimates and confidence bounds of daily and annual storm damages in Germany. Our approach reproduces the annual progression of winter storm losses and enables to estimate daily losses over a wide range of magnitudes.

Spatial Predictions of Extreme Wind Speeds over Switzerland Using Generalized Additive Models

AbstractThe purpose of this work is to present a methodology aimed at predicting extreme wind speeds over Switzerland. Generalized additive models are used to regionalize wind statistics for Swiss weather stations using a number of variables that describe the main physiographical features of the country. This procedure enables one to present the results for Switzerland in the form of a map that provides the 98th percentiles of daily maximum wind speeds (W98) at a 10-m anemometer height for cells with a 50-m grid interval. This investigation comprises three major steps. First, meteorological data recorded by the weather stations was gathered to build local wind statistics at each station. Then, data describing the topographic and landscape characteristics of the country were prepared using geographic information systems (GIS). Third, appropriate regression models were selected to make spatially explicit predictions of extreme wind speeds in Switzerland. The predictions undertaken in this study provide realistic values of the W98. The effects of topography on the results are particularly conspicuous. Wind speeds increase with altitude and are greatest on mountain peaks in the Alps, as would be intuitively expected. Relative errors between observations and model results calculated for the meteorological stations do not exceed 30%, and only 12 out of 70 stations exhibit errors that exceed 20%. The combination of GIS techniques and statistical models used to predict a highly uncertain variable, such as extreme wind speed, yields interesting results that can be extended to other fields, such as the assessment of storm damage on infrastructures.

Factors contributing to seasonal variations in wet deposition fluxes of trace elements at sites along Japan Sea coast

In this study, we measured the wet deposition fluxes of ten trace elements (As, Cd, Cr, Cu, Mn, Ni, Pb, Sb, V and Zn) from December 2002 to March 2006 at three sites along the Japan Sea coast, which have been strongly affected by the long-range transport of air pollutants from the Asian continent. Also, factors, contributing to their seasonal variations were investigated. At the northern and central sites, the monthly wet deposition fluxes of all or most trace elements greatly increased during the cold season (typically, November–April), along with their monthly average (volume-weighted) concentrations in the precipitation. The cold/warm season ratios for the average concentrations of trace elements in precipitation were within the range of 2.7–5.1 at the northern site and 1.8–5.9 at the central site, which were similar to the average scavenging ratios (= concentration in precipitation/concentration in air) at each site. However, there were small differences (0.47–1.2 at the northern site and 0.73–1.7 at the central site) in the ratios of average concentrations in air between the two seasons. These suggest that the increase in the wet deposition fluxes of trace elements during the cold season is due to increases in their scavenging ratios. On the other hand, the result for the southern site was different from those at the other sites. The number of days when the daily maximum wind speed exceeded 10 m s −1 at the meteorological observatories near the study sites increased markedly during the cold season at the northern and central sites, showing that strong winds usually blow during the cold season at those sites, but not at the southern site. Higher wind speed transports larger amounts of constituents into the cloud system, which can result in their increased concentrations in precipitation. Thus, high scavenging ratios of trace elements during the cold season may be caused by the increase in their amounts of discharge into the cloud system owing to high wind speed, suggesting that wind speed is an important factor in the seasonal variations in the wet deposition fluxes.

Day length and weather conditions profoundly affect physical activity levels in older functionally impaired people

Background:Regular physical activity is vital for maintaining the health and independence of older people. Few objective data exist on the effect of weather on physical activity levels in this group....

Development and application of an objective storm severity measure for the Northeast Atlantic region

An objective index for the estimation of storm severity has been developed, validated and applied to both reanalysis data and an AOGCM simulation for the Northeast Atlantic region. The index is based on the exceedance of local thresholds of the daily maximum wind speed. Positive trends for both the severity of storms during the historic ERA40 period (1960-2000), and under anthropogenic climate change (ACC) conditions (SRES A1B and A2) are identified. Additionally an increase in the spatial extent of storms is diagnosed, amounting up to about 10 % between present day and the scenario climate. Two types of the index are introduced: One area integrated measure for the investigation domain of 50°W to 20°E, 45°N to 70°N, called ASSI (Area Storm Severity Index), per day; and one event integrated index, called ESSI (Event Storm Severity Index), per storm event. ASSI and ESSI clearly identify extreme cyclone bombs under ACC conditions, which exceed the range found in observed (ERA40) and simulated data for the recent climate. ASSI and ESSI identify an increase in the severity of storms for the Northeast Atlantic and western Central Europe under ACC conditions. The index can easily be calculated for large data sets, and is thus well applicable to multi-model ensemble simulations in order to objectively estimate climate change signals and related measures of uncertainty. Reasons for the increase in severity could be seen mainly in the occurrence of higher wind speeds, and in larger areas affected by storms. These larger areas result from longer tracks combined with a common broadening along the path.

Seasonal changes in reproductive output of a year-round nesting population of the green turtle Chelonia mydas at Huyong Island, Thailand

AbstractFor the vast majority of reptiles, low temperature is a major constraint on reproduction. However, continuous reproduction is not always observed even in equatorial climates. In the present study, we examined the reproductive output of green turtles in a year-round nesting population on Huyong Island, Thailand. From 1996 to 2007, 121 females were identified. Because individual turtles nest several times within a season, we defined the nesting season of an individual as the month during which the first nesting activity was detected. We found a significant difference in clutch frequency among months. However, there were no significant relationships during the nesting period between clutch frequency and environmental parameters such as mean daily air temperature, precipitation and maximum wind speed. Although we could not identify the specific factors, our findings suggest that environmental factors may regulate seasonal variation in reproductive output even in the tropics.

Extremes of near-surface wind speed over Europe and their future changes as estimated from an ensemble of RCM simulations

In this study, we analyse the uncertainty of the effect of enhanced greenhouse gas conditions on windiness projected by an ensemble of regional model simulations driven by the same global control and climate change simulations. These global conditions, representative for 1961–1990 and 2071–2100, were prepared by the Hadley Centre based on the IPCC SRES/A2 scenario. The basic data sets consist of simulated daily maximum and daily mean wind speed fields (over land) from the PRUDENCE data archive at the Danish Meteorological Institute. The main focus is on the results from the standard 50 km-resolution runs of eight regional models. The best parameter for determining possible future changes in extreme wind speeds and possible change in the number of storm events is maximum daily wind speed. It turned out during this study that the method for calculating maximum daily wind speed differs among the regional models. A comparison of simulated winds with observations for the control period shows that models without gust parameterisation are not able to realistically capture high wind speeds. The two models with gust parametrization estimate an increase of up to 20% of the number of storm peak (defined as gusts ≥ 8 Bft in this paper) events over Central Europe in the future. In order to use a larger ensemble of models than just the two with gust parameterisation, we also look at the 99th percentile of daily mean wind speed. We divide Europe into eight sub-regions (e.g., British Isles, Iberian Peninsula, NE Europe) and investigate the inter-monthly variation of wind over these regions as well as differences between today’s climate and a possible future climate. Results show differences and similarities between the sub-regions in magnitude, spread, and seasonal tendencies. The model ensemble indicates a possible increase in future mean daily wind speed during winter months, and a decrease during autumn in areas influenced by North Atlantic extra-tropical cyclones.