Reanalysis Wind Speed Research Articles

Assessment of Wind Energy Potential in Kolaka Using Era5-land Reanalysis Data

This research aims to evaluate the potential for wind energy in Kolaka Regency using ERA5-Land reanalysis data from the output of the European Center for Medium-Range Weather Forecasts (ECMWF) model, which has a spatial resolution of 9 km with hourly temporal resolution for the period of 2001 to 2020. This research also compared the quality of ERA5-Land reanalysis wind speed data on data from nearby meteorological stations using statistical assessment methods. The analysis results reveal that the highest average wind speed is discovered at a height of 80 to 150 meters with a range of 4.99 to 5.93 m/s. The spatial distribution map of wind power density indicates that the energy potential at a height of 80 to 150 meters can reach 60 to 170 W/m2. The largest wind energy potential in Kolaka Regency can be generated in the DJF period (December-January-February) with the highest average monthly wind power density in January and the JJA period (June-July-August) with the highest average in July. In general, Kolaka Regency can be classified as an area with first-class wind energy potential, so it tends to be more suitable for the development of small-scale power plants. The suitable locations for developing small-scale wind power plants in Kolaka Regency are the southern Pomalaa coast and the Tanggetada coast because both locations have better wind energy sources than other areas.

The Importance of Contemporaneous Wind and pCO2 Measurements for Regional Air‐Sea CO2 Flux Estimates

AbstractFew observational platforms are able to sustain direct measurements of all the key variables needed in the bulk calculation of air‐sea carbon dioxide (CO2) exchange, a capability newly established for some Uncrewed Surface Vehicles (USVs). Western boundary currents are particularly challenging observational regions due to strong variability and dangerous sea states but are also known hot spots for CO2 uptake, making air‐sea exchange quantification in this region both difficult and important. Here, we present new observations collected by Saildrone USVs in the Gulf Stream during the winters of 2019 and 2022. We compared Saildrone data across co‐located vehicles and against the Pioneer Array moorings to validate the data quality. We explored how CO2 flux estimates differ when all variables needed to calculate fluxes from the bulk formulas are simultaneously measured on the same platform, relative to the situation where in situ observations must be combined with publicly‐available data products. We systematically replaced variables in the bulk formula with those often used for local and regional flux estimates. The analysis revealed that when using the ERA‐5 reanalysis wind speed in place of in situ observations, the ocean uptake of CO2 is underestimated by 8%; this underestimate grows to 9% if the NOAA Marine Boundary Layer atmospheric CO2 product and ERA‐5 significant wave height are also used in place of in situ observations. Overall our findings point to the importance of collecting contemporaneous observations of wind speed and ocean pCO2 to reduce biases in estimates of regional CO2 flux, especially during high wind events.

Three-Parameter Weibull for Offshore Wind Speed Distribution in Malaysia

It is usual practice to employ the probability density function in order to ascertain the wind energy potential. In this paper, the three-parameter Weibull model was suggested to distribute the low wind speed data at a site off the coast of Kijal, Terengganu. The maximum likelihood approach was used to determine the value of the distribution parameters. The Matrix Laboratory (MATLAB), better known for its user-friendly programming language, is used to carry out all of the analysis. The three-parameter Weibull was compared with the two-parameter Weibull and Rayleigh using the R-squared and the root mean square error (RMSE). These three distributions statistically model hourly ERA-5 reanalysis wind speeds. The three-parameter Weibull has a good performance, since it appears between the best R2 closest to 1 and RMSE the closest to 0. The R2 for the three-parameter Weibull is 0.9898, the two-parameter Weibull is 0.9874, and Rayleigh is 0.5153. While the RMSE Three-parameter Weibull is 0.0070, the two-parameter Weibull is 0.0082, and Rayleigh is 0.0677.

Machine Learning-Based Estimation of Tropical Cyclone Intensity from Advanced Technology Microwave Sounder Using a U-Net Algorithm

A U-Net algorithm was used to retrieve surface pressure and wind speed over the ocean within tropical cyclones (TCs) and their neighboring areas using NOAA-20 Advanced Technology Microwave Sounder (ATMS) reprocessed Sensor Data Record (SDR) brightness temperatures (TBs) and geolocation information. For TC locations, International Best Track Archive for Climate Stewardship (IBTrACS) data have been used over the North Atlantic Ocean and West Pacific Ocean between 2018 and 2021. The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) surface pressure and wind speed were employed as reference labels. Preliminary results demonstrated that the visualizations for wind speed and pressure matched the prediction and ERA5 location. The residual biases and standard deviations between the predicted and reference labels were about 0.15 m/s and 1.95 m/s, respectively, for wind speed and 0.48 hPa and 2.67 hPa, respectively, for surface pressure, after applying cloud screening for each ATMS pixel. This indicates that the U-Net model is effective for surface wind speed and surface pressure estimates over general ocean conditions.

Climate, trends and variability of extreme winds along the southwest coast of India

AbstractThe extreme wind climate and its trends and variability of the southwest coast of India, particularly the Kerala coast, are investigated using hourly reanalysis wind speeds for the period 1979–2021. The observed maximum wind speed and 99th percentile wind speed off Kerala coast are 21.0 and 11.3 m·s−1, respectively. The predominant direction of high wind speeds is in the SW–WNW sector, which accounts for 93.5% of the total high winds. Extreme wind speeds in this region are not due to tropical cyclones alone, but also due to seasonal high winds. Seasonally, the premonsoon and SW monsoon extreme winds are predominantly in the WSW–WNW directional sector, while postmonsoon strong winds are present in all directional sectors, but predominantly in the SSW–WNW sector, followed by the E–SSE sector. The 99th percentile wind speeds during premonsoon, SW monsoon and postmonsoon are 8.8, 12.3 and 8.9 m·s−1, respectively. A significant decreasing trend is observed in the 99th percentile wind speeds during 1979–2021, ranging from 3 to 6 cm·s−1·year−1 off Kerala coast. This is in contrast with global increasing extreme wind speeds, and it can be attributed to regional variability as well as weakening of the land–sea thermal gradients associated with rising surface temperature in the Arabian Sea in the light of global warming. In addition to the long‐term declining trend and monthly as well as seasonal features, distinct interannual variations are also noticed in the 99th percentile wind speeds. Analysis shows that the atmospheric teleconnections associated with El Niño–La Niña events and Indian Ocean Dipole have an important role in the variability of extreme wind speeds. The co‐occurrence of positive (negative) IOD with El Niño (La Niña) results in a sharp decline (increase) in extreme wind speeds. Significant decadal variations are also noticed in the 99th percentile wind speeds.

Evaluating ERA5 reanalysis predictions of low wind speed events around the UK

Low wind speed events represent one of the biggest challenges in fully de-carbonising the electricity system due to the growing proportion of wind energy in the UK energy mix. While reanalysis products are a useful tool to study the spatio-temporal characteristics of these occurrences, their performance and limitations should be understood prior to usage. In this study, hourly 10 m ERA5 reanalysis wind speed data were evaluated against in-situ wind speed measurements from 205 onshore and offshore observation stations around the UK. It was found that ERA5 has biases in mean wind speed of 0.166 m/s and −0.136 m/s for onshore and offshore domains respectively, and biases in hourly wind speed standard deviation of −0.354 m/s and −0.425 m/s for onshore and offshore domains respectively. Both errors are more pronounced in autumn and winter. These errors lead to an underestimation of the percentage frequency of short-duration low wind speed events. Furthermore, the findings suggest that the largest errors are from sites which are situated in coastal and mountainous regions where short-range topographical variability and local wind effects may not be resolved by ERA5. Despite these shortcomings, ERA5 nevertheless outperforms its global reanalysis counterparts in the UK domain and therefore, can provide valuable information in the context of low wind speed events prediction.

The properties of the global offshore wind turbine fleet

Offshore wind capacity increased massively in recent years. Sufficient wind potential must be available to realize the ambitious goals regarding further wind capacity expansion. Thus, the goal of this study is to quantify the meteorological, geographical, and technical properties of the current global offshore wind turbine fleet. The offshore wind turbine fleet is classified by twelve factors that belong to meteorological, geographical, and technical factor categories. A comprehensive wind turbine site dataset is evaluated, including 5473 sites in Europe and 3404 in Asia. The factors at the wind turbine sites are derived using publicly available datasets, including a reanalysis wind speed dataset, geodata, and technical properties. The results of this study are that the global median (1) wind speed is 8.7 m/s, (2) water depth is 17 m and (3) distance to shore is 27 km. The distance to the nearest wind turbine is 5.1 times the rotor diameter. The factors studied have a high regional variability. While Asian offshore wind turbines operate in shallower water closer to the shores than European, European wind turbine sites provide higher wind resources. The regional differences indicate that the wind potential and wind turbine siting criteria differ depending on the country. The factor values obtained are reference values for future offshore wind resource assessments and estimates of the wind potential from a regional to global scale.

Downscaling wind speed based on coupled environmental factors and machine learning

AbstractWind speed changes impact society and have important implications for climate change studies. Thus, high‐resolution and high‐quality wind speed datasets are necessary for environmental monitoring and ecosystem research. However, there is no complete set of high spatial and temporal resolution wind speed datasets for China. Additionally, it is extremely challenging to produce wind speed data at high spatial and temporal resolution for large‐scale regions with diverse climate types and complex topographies, such as China. In this study, we used multisource remote sensing images, obtained data on various environmental factors through the Google Earth Engine and Evapotranspiration (ET) Watch Cloud platforms, and combined machine learning algorithms to downscale the ERA5 reanalysis wind speed data, and finally obtained the daily wind speed datasets with 1 km spatial resolution for China in 2015. To verify the accuracy of the model and data products, we selected several metrics to evaluate in conjunction with the actual site observed data. The results show that the multifactor combination model of artificial neural network combining land surface temperature, sunshine durations and roughness factors outperforms a single‐factor combination model, and the results were in good agreement with the original data (R2 of 0.95 and RMSE of 0.40 m·s−1). The final wind speed data products were also in good agreement with the observed meteorological data (R2 range of 0.86–0.95 and RMSE range of 0.33–0.44 m·s−1); moreover, the accuracy and precision were greatly improved over the original data. This study provided a dataset that has potential applications in future climate change and ecosystem studies.

Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.

Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast

Abstract. The California Pacific coast is characterized by considerable wind resource and areas of dense population, propelling interest in offshore wind energy as the United States moves toward a sustainable and decarbonized energy future. Reanalysis models continue to serve the wind energy community in a multitude of ways, and the need for validation in locations where observations have been historically limited, such as offshore environments, is strong. The U.S. Department of Energy (DOE) owns two lidar buoys that collect wind speed observations across the wind turbine rotor layer along with meteorological and oceanographic data near the surface to characterize the wind resource. Lidar buoy data collected from recent deployments off the northern California coast near Humboldt County and the central California coast near Morro Bay allow for validation of commonly used reanalysis products. In this article, wind speeds from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP) are validated at heights within the wind turbine rotor layer ranging from 50 to 100 m. The validation results offer guidance on the performance and uncertainty associated with utilizing reanalyses for offshore wind resource characterization, providing the offshore wind energy community with information on the conditions that lead to reanalysis error. At both California coast locations, the reanalyses tend to underestimate the observed rotor-level wind resource. Occasions of large reanalysis error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production (> 10 m s−1), and mischaracterization of the diurnal wind speed cycle in summer months.

Statistical post-processing of reanalysis wind speeds at hub heights using a diagnostic wind model and neural networks

Abstract. The correct representation of wind speeds at hub height (e.g., 100 m above ground) is becoming more and more important with respect to the expansion of renewable energy. In this study, a post-processing of the wind speed of the regional reanalysis COSMO-REA6 in Central Europe is performed based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of 8 compared to COSMO-REA6 and considers different vertical atmospheric stabilities. In the second step, a statistical correction is performed using a neural network, as well as a generalized linear model based on different variables of the reanalysis. Although only a few measurements by masts or lidars are available at hub height, an improvement of the wind speed in the root-mean-squared error of almost 30 % can be achieved. A final comparison with radiosonde observations confirms the added value of combining the physical and statistical approaches in post-processing the wind speed.

Spaceborne GNSS-R Wind Speed Retrieval Using Machine Learning Methods

This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is divided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of individual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future.

Performance evaluation of NCEP/NCAR reanalysis blended with observation-based datasets for estimating reference evapotranspiration across Iran

Computing crop reference evapotranspiration (ETo) with the FAO Penman-Monteith method (PM-ETo) requires maximum and minimum air temperature, shortwave radiation, relative air humidity, and wind speed that are often unavailable in many places. The use of reanalysis data, which is common in climate studies, represents an alternative to observation data for the mentioned weather variables when are not available. This study focuses on the use of the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR)reanalysis blended with gridded data sets for computing monthly PM-ETo at 43 synoptic stations distributed across Iran. First, with a set of statistical indicators, the reanalysis weather variables required for computing ETo were compared with observation data, where a good match was obtained for solar radiation, maximum temperature, and minimum temperature. In contrast, at most of the studied stations, the reanalysis wind speed and relative humidity showed large errors when compared with observations. The PM-ETo computed with the blended reanalysis data were also compared with those obtained using observations. The results show that the PM-ETo computed from blended reanalysis compares relatively well with the ETo computedfrom observation at most of the studied stations. However, the spatial pattern of performance indicators reveal extremely poor results for the coastal locations of the Caspian Sea in the north and the Persian Gulf and Oman Sea in the south of Iran. Results suggest that, except for wind speed and relative humidity, the blended reanalysis products are suitable forestimatingETo in inland areas of Iran despitesome degree of overestimationat most of the studied stations.

Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine

In this work, a cost-effective wind resource method specifically developed for the ROSEO-BIWT (Building Integrated Wind Turbine) and other Building Integrated Wind Turbines is presented. It predicts the wind speed and direction at the roof of an previously selected building for the past 10 years using reanalysis data and wind measurements taken over a year. To do so, the reanalysis wind speed data is calibrated against the measurements using different kinds of quantile mapping, and the wind direction is predicted using random forest. A mock-up of a building and a BIWT were used in a wind tunnel to perform a small-scale experiment presented here. It showed that energy production is possible and even enhanced over a wide range of attack angles. The energy production estimations made with the best performing kind of calibration achieved an overall relative error of 6.77% across different scenarios.

Wind speed inversion and in-orbit assessment of the imaging altimeter on Tiangong-2 space station

Imaging altimeter (IALT) is a new type of radar altimeter system. In contrast to the conventional nadir-looking altimeters, such as HY-2A altimeter, Jason-1/2, and TOPEX/Poseidon, IALT observes the earth surface at low incident angles (2.5°–8°), so its swath is much wider and its spatial resolution is much higher than the previous altimeters. This paper presents a wind speed inversion method for the recently launched IALT onboard Tiangong-2 space station. Since the current calibration results of IALT do not agree well with the well-known wind geophysical model function at low incidence angles, a neural network is used to retrieve the ocean surface wind speed in this study. The wind speed inversion accuracy is evaluated by comparing with the ECMWF reanalysis wind speed, buoy wind speed, and in-situ ship measurements. The results show that the retrieved wind speed bias is about −0.21 m/s, and the root-mean-square (RMS) error is about 1.85 m/s. The wind speed accuracy of IALT meets the performance requirement.

How many offshore wind turbines does New England need?

AbstractThe proliferation of countries and regions with 100% clean or renewable energy targets necessitates an analysis to determine the number of generating units and storage needed to meet real‐time electricity demand on the electric grid. The coastal areas of New England have the capacity to produce a large percentage of the region's energy needs with offshore wind turbines. Here we model offshore wind turbine power production data using MERRA‐2 reanalysis and lidar wind speed data sets. We compare this power production to the New England hourly grid demand over the course of one year. 2,000 10 MW offshore wind turbines could satisfy New England's grid demand for about 37% of the year. When combined with 55 GWh of storage, 2,000 turbines could satisfy grid demand for about 72% of the year.

Using global reanalysis data to quantify and correct airflow distortion bias in shipborne wind speed measurements

Abstract. At sea, wind forcing is responsible for the formation and development of surface waves and represents an important source of near-surface turbulence. Therefore, processes related to near-surface turbulence and wave breaking, such as sea spray emission and air–sea gas exchange, are often parameterised with wind speed. Thus, shipborne wind speed measurements provide highly relevant observations. They can, however, be compromised by flow distortion due to the ship's structure and objects near the anemometer that modify the airflow, leading to a deflection of the apparent wind direction and positive or negative acceleration of the apparent wind speed. The resulting errors in the estimated true wind speed can be greatly magnified at low wind speeds. For some research ships, correction factors have been derived from computational fluid dynamic models or through direct comparison with wind speed measurements from buoys. These correction factors can, however, lose their validity due to changes in the structures near the anemometer and, thus, require frequent re-evaluation, which is costly in either computational power or ship time. Here, we evaluate if global atmospheric reanalysis data can be used to quantify the flow distortion bias in shipborne wind speed measurements. The method is tested on data from the Antarctic Circumnavigation Expedition onboard the R/V Akademik Tryoshnikov, which are compared to ERA-5 reanalysis wind speeds. We find that, depending on the relative wind direction, the relative wind speed and direction measurements are biased by −37 % to +22 % and -17∘ to +11∘ respectively. The resulting error in the true wind speed is +11.5 % on average but ranges from −4 % to +41 % (5th and 95th percentile). After applying the bias correction, the uncertainty in the true wind speed is reduced to ±5 % and depends mainly on the average accuracy of the ERA-5 data over the period of the experiment. The obvious drawback of this approach is the potential intrusion of model biases in the correction factors. We show that this problem can be somewhat mitigated when the error propagation in the true wind correction is accounted for and used to weight the observations. We discuss the potential caveats and limitations of this approach and conclude that it can be used to quantify flow distortion bias for ships that operate on a global scale. The method can also be valuable to verify computational fluid dynamic studies of airflow distortion on research vessels.

Integration of small-scale surface properties in a new high resolution global wind speed model

A new model for mapping the near-surface wind speed L-moments on a high spatial resolution scale (250 m × 250 m) is introduced (GloWiSMo). The target variables are the first five L-moments of 6146 globally distributed wind speed time series. ERA5 reanalysis wind speed available on a 0.25° × 0.25° grid was used as predictor representing the large-scale wind field. Eleven predictors derived from a land cover model and a digital elevation model were applied to integrate the influence of small-scale surface properties on the wind field. The model is based on a least-squares boosting approach which is a machine learning algorithm. The parameters of the Kappa and Wakeby distribution were estimated based on the modeled L-moments. By applying the power law, the near-surface wind speed distribution can be extrapolated to any hub height. Here, we selected a typical wind turbine hub height of 120 m to demonstrate the potential of GloWiSMo. It was found that the relevance of a predictor on the spatial variability of the wind resource changes with the size of the investigation area. While the roughness length is a decisive factor for the large-scale spatial variability of the wind resource, the relative elevation is an important factor for the small-scale spatial variability. Rigorous model evaluation was performed using a validation dataset containing 598 globally distributed wind speed time series. The coefficient of determination calculated for the first L-moment was found to be 0.83. Based on the evaluation results, we argue that the developed model enables accurate and spatially explicit wind resource estimates at a very high spatial resolution.

Assessing temporal variability of wind resources in China and the spatial correlation of wind power in the selected regions

In this study, the temporal variability of wind resources in China and the spatial correlation of wind power in the selected regions are analyzed based on reanalysis wind speed data from 1998 to 2017. The monthly and diurnal variation patterns of wind speed are investigated first. Then the temporal variation patterns of wind power are further studied based on 12 selected regions. The temporal variability of wind resources varies from one region to another, which implies that the variability of wind power can be mitigated by combining wind farms in different regions. So the spatial correlation of wind power between different regions is analyzed. The correlation coefficients generally range from −0.4 to 1.0. Meanwhile, the spatial correlation of wind power on different time scales is further investigated. The correlation coefficients increase with the time scale in most cases. As a result, the monthly and yearly variations of wind power may not be significantly mitigated by interconnecting wind power generated in different regions. However, interconnecting different wind farms can effectively mitigate the hourly fluctuation in wind power due to the less correlation on the hourly time scale. Finally, the influence of the proportion of the installed wind power capacities between combined regions on the mitigation e ffect is discussed.

An approach to homogenize daily peak wind gusts: An application to the Australian series

Daily Peak Wind Gust (DPWG) time series are important for the evaluation of wind‐related hazard risks to different socioeconomic and environmental sectors. Yet, wind time series analyses can be impacted by several artefacts, both temporally and spatially, which may introduce inhomogeneities that mislead the study of their decadal variability and trends. The aim of this study is to present a strategy in the homogenization of a challenging climate extreme such as the DPWG using 548 time series across Australia for 1941–2016. This automatic homogenization of DPWG is implemented in the recently developed Version 3.1 of the R package Climatol. This approach is an advance in homogenization of climate records as it identifies 353 break points based on monthly data, splits the daily series into homogeneous subperiods, and homogenizes them without needing the monthly corrections. The major advantages of this homogenization strategy are its ability to: (a) automatically homogenize a large number of DPWG series, including short‐term ones and without needing site metadata (e.g., the change in observational equipment in 2010/2011 was correctly identified); (b) use the closest reference series even not sharing a common period with candidate series or presenting missing data; and (c) supply homogenized series, correcting anomalous data (quality control by spatial coherence), and filling in all the missing data. The NCEP/NCAR reanalysis wind speed data were also trialled in aiding homogenization given the station density was very low during the early decades of the record; however, reanalysis data did not improve the homogenization. Application of this approach found a reduced range of DPWG trends based on site data, and an increased negative regional trend of this climate extreme, compared to raw data and homogenized data using NCEP/NCAR. The analysis produced the first homogenized DPWG dataset to assess and attribute long‐term variability of extreme winds across Australia.