Changes In Wind Speed Research Articles

Potential Impacts of Climate Change on Renewable Energy and Storage Requirements for Grid Reliability and Resource Adequacy

Abstract This paper provides a study of the potential impacts of climate change on intermittent renewable energy resources and storage requirements for grid reliability and resource adequacy. Climate change models and available regional data were first evaluated to determine uncertainty and potential changes in solar irradiance, temperature, and wind speed within a specific U.S. southwest service area as a case study. These changes were then implemented in solar and wind energy models to determine impacts on renewable energy resources. Results for the extreme climate change scenario show that the projected wind power may decrease by ∼13% due to projected decreases in wind speed. Projected solar power may decrease by ∼4% due to decreases in irradiance and increases in temperature. Uncertainty in these climate-induced changes in wind and solar resources was accommodated in probabilistic models assuming uniform distributions in the annual reductions in solar and wind resources. Uncertainty in battery storage performance was also evaluated based on increased temperature, capacity fade, and degradation in round-trip efficiency. The hourly energy balance among electrical load, generation, and storage was calculated throughout the year. The annual loss of load expectation (LOLE) was found to increase from ∼0 days/year to a median value of ∼2 days/year due to potential reductions in renewable energy resources caused by climate change and decreased battery performance. Significantly increased battery storage was required to reduce the LOLE to desired values of 0.2 days/year.

A Harvester with a Helix S-Type Vertical Axis to Capture Random Breeze Energy Efficiently.

Breeze energy is a widely distributed renewable energy source in the natural world, but its efficient exploitation is very difficult. The conventional harvester with fixed arm length (HFA) has a relatively high start-up wind speed owing to its high and constant rotational inertia. Therefore, this paper proposes a harvester with a helix s-type vertical axis (HSVA) for achieving random energy capture in the natural breeze environment. The HSVA is constructed with two semi-circular buckets driven by the difference of the drag exerted, and the wind energy is transferred into mechanical energy. Firstly, as the wind speed changes, the HSVA harvester can match the random breeze to obtain highly efficient power. Compared with the HFA harvester, the power coefficient is significantly improved from 0.15 to 0.2 without additional equipment. Furthermore, it has more time for energy attenuation as the wind speeds dropped from strong to moderate. Moreover, the starting torque is also better than that of HFA harvester. Experiments showed that the HSVA harvester can improve power performance on the grounds of the wind speed ranging in 0.8-10.1 m/s, and that the star-up wind speed is 0.8 m/s and output peak power can reach 17.1 mW. In comparison with the HFA harvester, the HSVA harvester can obtain higher efficient power, requires lower startup speed and keeps energy longer under the same time. Additionally, as a distributed energy source, the HSVA harvester can provide a self-generating power supply to electronic sensors for monitoring the surrounding environment.

Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis

Accurate wind power forecasting plays a crucial role in the planning of unit commitments, maintenance scheduling, and maximizing profits for power traders. Uncertainty and changes in wind speeds pose challenges to the integration of wind power into the power system. Therefore, the reliable prediction of wind power output is a complex task with significant implications for the efficient operation of electricity grids. Developing effective and precise wind power prediction systems is essential for the cost-efficient operation and maintenance of modern wind turbines. This article focuses on the development of a very-short-term forecasting model using machine learning algorithms. The forecasting model is evaluated using LightGBM, random forest, CatBoost, and XGBoost machine learning algorithms with 16 selected parameters from the wind energy system. The performance of the machine learning-based wind energy forecasting is assessed using metrics such as mean absolute error (MAE), mean-squared error (MSE), root-mean-squared error (RMSE), and R-squared. The results indicate that the random forest algorithm performs well during training, while the CatBoost algorithm demonstrates superior performance, with an RMSE of 13.84 for the test set, as determined by 10-fold cross-validation.

Response of the tropical Pacific Ocean to wind changes related to global warming from simulations with an ocean general circulation model

A suite of numerical experiments is implemented with an ocean general circulation model (OGCM) to ex-amine the roles of wind stress and wind speed for oceanic changes in the tropical Pacific under global warming. In particular, we turned off the changes of wind stress and/or wind speed in the model to identify the effects of wind-driven ocean circulation and air-sea latent heat flux (i.e., its portion through the wind speed influence on the efficiency of latent heat flux). Results show that 1) the wind stress change appears to be a key forcing mechanism for weakening the tropical surface currents as well as for the oceanic changes in the equatorial thermocline, while it only contributes secondarily to the sea surface temperature (SST) pattern formation in the tropics; 2) the wind speed change is the leading cause for the minimum warming over the southeast subtropics and for a stronger surface warming in the northern hemisphere than in the southern hemisphere; and 3) the enhanced surface warming along the equator is mainly due to the effect of warming in the absence of wind stress and wind speed changes, and this effect also plays a significant role for changing the equatorial thermocline.

Analysis of Wind Farms under Different Yaw Angles and Wind Speeds

Wind farm optimization is pivotal in maximizing energy output, reducing costs, and minimizing environmental impact. This study comprehensively explores wind farm behavior under varying wind conditions and yaw angles to achieve these objectives. The primary motivation is to optimize wind farm performance and efficiency through proper yaw adjustment in response to wind speed changes. A computational investigation using a three-by-three wind turbine array was conducted, employing large eddy simulation (LES) to evaluate wind farm performance. Nine LES cases were considered, incorporating three wind speeds (7.3 ms−1, 10.4 ms−1, and 4.3 ms−1) and three yaw angles (30°, 20°, and 0°), with nearly constant turbulence intensity (TI) at 12.0%. The impact of wind speed and yaw angles on wake characteristics and power outputs were analyzed. The findings reveal that wind speed has limited influence on wake characteristics and power outputs, except for lower wind speeds at a yaw angle of 20 degrees. These results contribute to understanding wind farm performance optimization, aiding in developing strategies to enhance energy extraction while minimizing costs and environmental implications.

Study the impact of transient state on the doubly fed induction generator for various wind speeds

Recently, renewable resources such as wind, hydro, and tidal have experienced a rapid development. Electricity production, based on wind, has been concentrated on a large scale. Additionally, a doubly fed induction generator has been used in wind farms on a large scale. This machine is influenced by the multiple transient states that are happening in the grid. Many researchers studied the effect of voltage-dip on DFIG performance; none of them studied the effect of voltage-dip sharing with wind-speed changing for both sub- and hyper-synchronous modes. In this paper, DFIG behavior is investigated under a transient state which is represented by 3-phase voltage-dip, in both operation modes (sub-synchronous & hyper-synchronous) with various values of the wind speed. Based on MATLAB Simulink, the various DFIG parameters are extracted to determine the relation between voltage-dip, variable wind speed, and DFIG performance. Results show that the parameters that are affected were rotor-current, rotor-voltage, and DC-Link voltage, while stator-current and stator-flux are not affected. It is also shown that DC-Link voltage values are smaller in the hyper-synchronous mode compared with sub-synchronous one.

Evaluating offshore wind power potential in the context of climate change and technological advancement: Insights from Republic of Korea

Offshore wind power is constantly being invested and expanded around the world. National plans for wind power generation must be evaluated in light of climate change and technological advancement since wind speed changes at sea level due to climate change along with continuous technological development significantly affect the wind power generation potential. Republic of Korea has set ambitious plans to develop and expand large-scale offshore wind power generation and with the aim of accurately predicting domestic wind power generation potential, the current study analyzed the patterns and consequences of changes in wind speed due to climate change. Furthermore, the contribution of developing a 15 MW large-scale wind power generator to the national target for offshore wind power generation was evaluated. Changes in wind speed under RCP 4.5 and RCP 8.5 scenarios were examined for 10-year interval changes and for seasonal variabilities from 2010 to 2100. Results indicate that the wind speed would continuously decrease in the RCP 8.5 scenario, suggesting that failing to reduce greenhouse gas emission would eventually lead to loss of wind resources and resultant decline in wind power generation. Analyses also revealed that installing 3 MW wind turbines (utilization rate of 22%) or 15 MW wind turbines (utilization rate of 29%) can respectively achieve 40% or 55% of the national electricity production target for 2030. Assuming an aggressive deployment in high wind speed areas, it was found feasible to achieve up to 64% of the 2030 target, and even 100% but with a high utilization rate of 42%.

Urbanization accelerates urban warming by changing wind speed: Evidence from China based on 2421 meteorological stations from 1978 to 2017

Understanding the process and mechanism underlying urbanization's effect on urban warming is essential to future urban development planning. This study aims to measure the impact of urbanization on urban warming and dynamically analyze its meteorological drivers (wind speed and precipitation). According to remote sensing based impervious surface data, 2421 meteorological stations in China in 1978–2017 were dynamically classified into urban and rural stations. Then, correlation analysis was used to explore the effect and mechanism of urbanization on meteorological differences between urban and rural areas. The positive effect of urbanization on urban warming was 0.02 °C (0.26 °C) per decade during the period of 1988–2017 (2008–2017), and the contribution of urbanization to urban warming increased from 58.74% to 61.21% between the two periods. In 59.7% of cities, urbanization drove urban warming by changing wind speed. In 80.95% of cities, when wind speed decreased, the temperature difference between urban and rural areas increased. When wind speed was higher, the positive contribution of urbanization to urban warming was more significant. Considering the urban ventilation environment during urban planning and renewal will improve the local climate and reduce climate risks.

Hardware-in-the-Loop Scheme of Linear Controllers Tuned through Genetic Algorithms for BLDC Motor Used in Electric Scooter under Variable Operation Conditions

Outrunner brushless DC motors (BLDC) are a type of permanent magnet synchronous motor (PMSM) widely used in electric micro-mobility vehicles, such as scooters, electric bicycles, wheelchairs, and segways, among others. Those vehicles have many operational constraints because they are driven directly by the user with light protective wearing. Therefore, to improve control strategies to make the drive safer, it is essential to model the traction system over a wide range of operating conditions in a street environment. In this work, we developed an electro-mechanical model based on the Hardware-in-the-Loop (HIL) structure for a two-wheeler electric scooter, using the BLDC motor to explore its response and to test linear controllers for speed and torque management under variable operating conditions. The proposed model includes motor parameters, power electronics component characteristics, mechanical structure, and external operating conditions. Meanwhile the linear controllers will be adjusted or tuned though a heuristic approach based on Genetic Algorithms (GAs) to optimize the system’s response. The HIL scheme will be able to simulate a wide range of conditions such as user weight, slopes, wind speed changes, and combined conditions. The designed model can be used to improve the design of the controller and estimate mechanical and electrical loads. Finally, the results of the controller tests show how the proposed cascade scheme, tuned through the GA, improves the system behavior and reduces the mean square error with respect to a classical tuning approach between 20% and 60%.

Optimization of Permanent Magnet Synchronous Generator Output Power in Wind Power Plants with ANN Back Propagation

The focus of this research is optimizing a wind power plant using a Permanent Magnet Synchronous Generator (PMSG). The backpropagation method of the artificial neural network system was chosen to optimize the output power of the wind power generator. Based on the simulation results, the backpropagation algorithm of the artificial neural network obtains the output power based on the input variable in the form of changing wind speed. The results show that the best value is learning rate = 0.5, error = 0.0001, max. epoch= 100000, neuron hidden layer = 5. The Mean Square Error (MSE) value obtained is 0.1026 reaching the goal at epoch 14845. The reverse training regretion reaches 0.99917. The optimization results are close to the specified error, which is 0.0001, while what is obtained is 0.0145. The power generated by the wind speed is 10.7 m/s before being optimized using the back propagation neural network method worth 321 watts, while the optimized power results are 409 watts. The difference in the average target power obtained is 88 watts compared to the power of the Artificial Neural Network (ANN).

Minor impacts of rain on methane flux from hemiboreal, boreal, and subarctic lakes

Methane (CH4) emissions (FCH4) from northern freshwater lakes are not only significant but also highly variable in time and one driver variable suggested to be important is precipitation. Rain can have various, potentially large effects on FCH4 across multiple time frames, and verifying the impact of rain on lake FCH4 is key to understand both contemporary flux regulation, and to predict future FCH4 related to possible changes in frequency and intensity of rainfall from climate change. The main objective of this study was to assess the short-term impact of typically occurring rain events with different intensity on FCH4 from various lake types located in hemiboreal, boreal, and subarctic Sweden. In spite of high time resolution automated flux measurements across different depth zones and covering numerous commonly types of rain events in northern areas, in general, no strong impact on FCH4 during and within 24 h after the rainfall could be observed. Only in deeper lake areas and during longer rain events FCH4 was weakly related to rain (R2 = 0.29, p < 0.05), where a minor FCH4 decrease during the rain was identified, suggesting that direct rainwater input, during greater rainfall, may decrease FCH4 by dilution of surface water CH4.Overall, this study indicates that typical rain events in the studied regions have minor direct short-term effects on FCH4 from northern lakes and do not enhance FCH4 from shallow and deeper parts of lakes during and up to 24-h after the rainfall. Instead, other factors such as wind speed, water temperature and pressure changes were more strongly correlated with lake FCH4.

Research on Coordinated Control Strategy of DFIG-ES System Based on Fuzzy Control

As the penetration rate of wind power systems is rising, which causes the overall system’s inertia to decline, the power system’s capacity to regulate frequency will be negatively affected. Therefore, this paper investigates the inertia control of doubly fed induction generation, and an energy storage system is installed in the wind farm to respond to the frequency deviation. First, a fuzzy control-based virtual inertia adaptive control strategy is presented. The goal of dynamic adjustment of the virtual inertia coefficient is realized by taking into account the uncertain factors of wind speed and frequency change rate. A recovery strategy based on the energy storage system’s level of charge is employed to prevent overcharging and over-discharging of the battery. Then, a weight factor based on frequency deviation is introduced to combine the droop output of the energy storage system with the virtual inertia output of the doubly fed induction generation, and the joint output mode of the wind storage system is determined in each stage of primary frequency regulation. Finally, the simulation verification is performed using the wind storage system simulation model created by MATLAB. The comparison results with other control methods prove that the proposed method is effective.

Spatiotemporal distributions and oceanic emissions of short-lived halocarbons in the East China Sea

Coastal waters are important sources of volatile halocarbons, which are important in atmospheric chemistry. Here, in May (spring) and October (autumn) 2020, we studied the surface, bottom, and sediment-pore seawater concentrations, atmospheric mixing ratios, and sea-to-air fluxes of the three primary short-lived atmospheric halocarbons (CH3I, CH2Br2, and CHBr3) in the East China Sea (ECS). The highest concentrations of the three short-lived halocarbons occurred in coastal waters, such as the Changjiang estuary and Zhejiang coastal waters, reflecting the influence of excessive anthropogenic inputs on the distributions of these gases. Interestingly, the aqueous levels of these gases seemed to be lower compared to previous measurements in this oceanic region, probably due to reduced contributions from local anthropogenic emission sources. The concentrations of CH3I, CH2Br2, and CHBr3 in pore water were significantly higher than those in bottom water, suggesting that sediment could be a source of these short-lived halocarbons. Additionally, the atmospheric mixing ratios of these gases occasionally increased in coastal areas. An air-mass back trajectory analysis showed this was due to continental anthropogenic sources and emissions from enriched waters. The atmospheric mixing ratios of these halocarbons exhibited significant seasonal variability, with significant correlations among atmospheric CH3I, CH2Br2, and CHBr3 in spring, but not in autumn. The sea-to-air fluxes of CH3I, CH2Br2, and CHBr3 indicated that the ECS is a source of these gases. Seasonal differences in CH3I and CH2Br2 fluxes were driven by changes in wind speed and sea surface temperature, while CHBr3 flux changes were associated with changes in its surface seawater concentration.

Skimming Flow and Sand Transport Within and Above Ammophila (Marram) Grass on a Foredune

AbstractAn investigation of wind flow and sand transport over a low to high density Ammophila dominated foredune on the Łeba barrier, Poland, utilizing multiple anemometers and sand traps is presented. The study provides the first high resolution data on modified saltation and suspended sand transport over a foredune under conditions of both canopy and skimming flow. Topographic acceleration of flow and topographic steering over the foredune were observed during oblique incident above threshold flow conditions. The concentration of sand in aeolian transport decreases with height to a minimum that is reached at different elevations above the ground, depending on Ammophila grass density. Of the three Ammophila (marram) grass densities investigated, nearly half (46%) of the total sand‐transport rate occurred above the lower density Ammophila grass, whereas up to 70% of the total transport occurred above the higher density marram grass. This likely results from both elastic collisions of sand grains with grass leaves over the Ammophila canopy and the different positions of the canopy rebounding surface, which is sensitive to even slight changes in wind speed. In addition, as plant density increases, there is less flow penetration into the canopy, vegetative forcing (similar to topographic forcing) of vertical flow is greater, and sand in both modified saltation and suspension is forced higher above the canopy in skimming flow. The maximum sand transport above the vegetation canopy increased in elevation from 32 to 48 to 53 cm as the vegetation density increased from low to moderate to high, respectively. The study also demonstrates that in skimming flow, relatively more sand is transported at higher elevations above a canopy surface than above a bare sand surface.

Impact of early heat anomalies on urban tree cooling efficiency: Evidence from spring heatwave events in India

India’s heat waves have recently led to huge losses to the natural ecosystem and human society, and are projected to occur earlier and more frequently in the future. Frequent heat waves around the world in recent years highlight the emerging need for heat mitigation in sustainable urban development. Urban greening has been widely recognized as an effective nature-based solution to mitigate thermal stress. However, knowledge of how recent early heat anomalies affect the growth of urban trees and their cooling efficiency (CE) remains unclear. To inform this issue, here we compared the trees’ growth status and CE in 2022 spring heatwave period to that of the same period in 2019–2021 in New Delhi and Haryana, India as a natural experiment, to investigate the relationship between early heat anomalies and urban trees’ CE. Results showed that (1) the 2022 warm spring increased the greenness (reflected by Normalized Difference Vegetation Index-NDVI) of urban trees and advanced urban trees’ growing stage, but more warming effects of urban trees with higher coverage were observed, which is related to the water use. (2) Interannual pattern of the urban trees’ CE in 2022 was different from that of 2019–2021, which is attributed to the changing meteorological variables, i.e., air temperature, precipitation and wind speed from global model Climate Forecast System (CFS), especially the air temperature, and the biophysical responses of trees, using evapotranspiration (ET) as a proxy. We advocate planting more trees in areas with low tree cover percentage, equitable water usage within cities, and more effective thermal mitigation measures are expected to play a sustainable and significant role at the forefront of climate change’s worst impacts. Meanwhile, local trait database and field observation data are essential for the selection of urban trees adaptable and climatically useful in extreme heat events in Indian cities.

Innovations in Passive Downdraft Cooling Performance Evaluation Methods: Design and Construction of a Novel Environmental Test Chamber

Energy demand for active mechanical space cooling is projected to double by 2050. Wider adoption of passive cooling systems can help reduce demand. However, familiarity with these systems remains low, and innovation in the field is constrained due to a lack of cost-effective, accessible performance evaluation methods. This paper reports the design, construction, and commissioning of an affordable, self-contained environmental test chamber. The novel chamber replicates a range of outdoor conditions common in hot, dry regions, making possible year-round testing of reduced-scale prototypes. Data from calibration testing are reported, showing no significant difference in evaporative efficiency when a reduced-scale prototype tested in the chamber is compared with datasets from prior full-scale testing. Analyzing the results using an independent sample two-tailed t-test with a 95% confidence interval found a p-value of 0.75. While measured outlet air velocities for reduced-scale and full-scale prototypes differed to some extent (root mean square error of 0.45 m/s), results were nevertheless deemed comparable due to errors introduced by the rapid change in wind speeds and directions at full scale. Future chamber modifications will correct misalignments between data collected from the two scales and prevent observed increases in the chamber’s relative humidity levels during testing.

Analysis of the 20-Year Variability of Ocean Wave Hazards in the Northwest Pacific

In the Northwest Pacific (NWP), where a unique monsoon climate exists and where both typhoons and extratropical storms occur frequently, hazardous waves pose a significant risk to maritime safety. To analyze the 20-year variability of hazardous waves in this region, this study utilized hourly reanalysis data from the ECMWF ERA5 dataset covering the period from 2001–2020, alongside the wave risk assessment method. The ERA5 data exhibits better consistency, in both the temporal and spatial dimensions, than satellite data. Although hazardous wind seas occur more frequently than hazardous swells, swells make hazardous waves travel further. Notably, the extreme wave height (EWH) shows an increasing trend in high- and low-latitude areas of the NWP. The change in meridional wind speeds is the primary reason for the change in the total wind speed in the NWP. Notably, the maximum annual increase rate of 0.013 m/year for EWH exists in the region of the Japanese Archipelago. This study elucidated the distributions of wave height intensity and wave risk levels, noting that the EWHs of the 50-year and 100-year return periods can reach 20.92 m and 23.07 m, respectively.

ADMM-based joint rescheduling method for high-speed railway timetabling and platforming in case of uncertain perturbation

The operation of high-speed trains is susceptible to perturbations such as strong winds and heavy snowfall, causing them to deviate from operating schedules. A joint mixed-integer linear programming (MILP) model based on the event-activity network is proposed to reschedule the train timetable and platform assignment jointly. Trains traveling in different directions are simultaneously considered in this model, and they can occupy the available reverse platforms in stations. Furthermore, to strengthen the robustness of the rescheduling scheme in case of uncertain perturbations, such as variable temporary speed restrictions caused by changing wind speed, the uncertainty is modeled using scenario-based chance constraints, which are then converted into deterministic constraints. Model predictive control (MPC) divides the entire time horizon into several stages. In each stage, the proposed model is decomposed according to directions. The constraints for trains traveling in different directions are dualized by the Alternating Direction Method of Multipliers (ADMM), which makes the sub-problems decoupled and solvable in parallel in each iteration of ADMM, promoting real-time performance. The real timetable and line data of Beijing–Shanghai HSR are utilized to investigate the effect of the proposed method. Compared to the commonly used strategies, it can significantly reduce delays and the number of affected events. The proposed method also shows relatively high robustness compared to the method in the case of predefined perturbations. It causes fewer conflicts and fewer needs for rescheduling when facing the changing perturbation and gets rescheduling results with higher quality.

Impacts of Quasi-Biennial Oscillation and El Niño–Southern Oscillation on Stratospheric Isentropic Mixing Process

The present study investigates the influences of stratospheric quasi-biennial oscillation (QBO) and El Niño–Southern Oscillation (ENSO) on the intensity of stratospheric isentropic mixing based on ERA-Interim and MERRA-2 reanalysis products. It is found that isentropic mixing in the stratosphere is modulated by QBO and ENSO. An analysis of the QBO basis function in the multiple regression model reveals that isentropic mixing in the lower stratosphere is suppressed in the equatorial region in the WQBO phase, while the mixing enhances in the subtropical and mid-latitude regions. This result is not consistent with the Holton–Tan mechanism. However, isentropic mixing in the mid-latitudes becomes stronger in the middle stratosphere in the EQBO phase, which agrees well with the Holton–Tan effect. Composite analysis indicates that QBO-induced changes in the direction and speed of the stratospheric zonal wind can affect wave propagation and wave breaking. In the WQBO phase, zonal wind weakens, and a planetary wave is anomalously converging near 30°N, which leads to an increase in isentropic mixing; on the contrary, wind speed becomes large, and the upward propagation of planetary wave divergence, which lead to the isentropic mixing, becomes weak near 60°N. In the EQBO phase, the wind is relatively weak around 60°N, and the isentropic mixing is strong. Multiple regression analysis reveals the ENSO impact on the intensity of isentropic mixing, which shows weak mixing in the middle and high latitudes and strong mixing in the low latitudes of the lower stratosphere in the El Niño years. In the middle stratosphere, isentropic mixing enhances in the mid-latitude region due to intensified upward propagation of planetary waves but weakens in the polar region. Composite analysis reveals a clear relationship between the mixing strength zones of the El Niño and La Niña years with the position of the polar jet and changes in zonal wind speed.

Evaluation of Blended Wind Products and Their Implications for Offshore Wind Power Estimation

The Cross-Calibrated Multi-Platform (CCMP) wind analysis is a satellite-based blended wind product produced using a two-dimensional variational method. The current version available publicly is Version 2 (CCMP2.0), which includes buoy winds in addition to satellite winds. Version 3 of the product (CCMP3.0) is being produced with several improvements in analysis algorithms, without including buoy winds. Here, we compare CCMP3.0 with a special version of CCMP2.0 that did not include buoy winds, so both versions are independent of buoy measurements. We evaluate them using wind data from buoys around the coasts of the United States and discuss the implications for the wind power industry and offshore wind farms. CCMP2.0 uses ERA-Interim 10 m winds as the background to fill observational gaps. CCMP3.0 uses ERA5 10 m neutral winds as the background. Because ERA5 winds are biased towards lower values at higher wind conditions, CCMP3.0 corrected this bias by matching ERA5 wind speeds with satellite scatterometer wind speeds using a histogram matching method. Our evaluation indicates that CCMP3.0 has better agreement with the independent buoy winds, primarily for higher winds (&gt;10 m/s). This is reflected by the higher correlation and lower root-mean-squared differences of CCMP3.0 versus buoy winds, especially for higher wind conditions. For the U.S. coastal region (within 200 km), the mean wind speed of CCMP3.0 is enhanced by 1–2%, and the wind speed standard deviation is enhanced by around 3–5%. These changes in wind speed and its standard deviation from CCMP2.0 to CCMP3.0 cause an 8–12% increase in wind power density. The wind power density along the U.S. coastal region is also correlated with various climate indices depending on locations, providing a useful approach for predicting wind power on subseasonal to interannual timescales.