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Satellite synthetic aperture radar (SAR) provides ocean surface wind fields at 10 m above sea level. The objective is to investigate the capability of SAR satellite StriX observations for mapping offshore wind farm wakes. The focus is on the conditions under which an apparent wind speed‐up is generated, measured in 48% of the 67 images available. The results compare well to Sentinel‐1 observations, showing a 34% wind speed‐up rate during several years based on 1171 images. Three wind speed‐up cases have been studied in detail using the mesoscale Weather, Research, and Forecasting (WRF) model with two wind farm parameterizations. At 10 m above sea level, the SAR‐based observations and WRF model compare for most cases, though only when turbulent kinetic energy (TKE) is included in the wind farm parameterization. The TKE mixes higher momentum downward in a stable atmosphere, causing surface wind speed‐up near the surface.

Global reanalysis products are important tools across disciplines to study past meteorological changes and are especially useful for wind energy resource evaluations. Studies of observed wind speed show that land surface wind speed declined globally since the 1960s (known as global terrestrial stilling) but reversed with a turning point around 2010. Whether the declining trend and the turning point have been captured by reanalysis products remains unknown so far. To fill this research gap, a systematic assessment of climatological winds and trends in five reanalysis products (ERA5, ERA-Interim, MERRA-2, JRA-55, and CFSv2) was conducted by comparing gridcell time series of 10-m wind speed with observational data from 1439 in situ meteorological stations for the period 1989–2018. Overall, ERA5 is the closest to the observations according to the evaluation of climatological winds. However, substantial discrepancies were found between observations and simulated wind speeds. No reanalysis product showed similar change to that of the global observations, although some showed regional agreement. This discrepancy between observed and reanalysis land surface wind speed indicates the need for prudence when using reanalysis products for the evaluation and prediction of winds. The possible reasons for the inconsistent wind speed trends between reanalysis products and observations are analyzed. The results show that wind energy production should select different products for different regions to minimize the discrepancy with observations.

Seasonal variations in near‐surface wind speed (NSWS) significantly impact wind energy production, yet the role of the El Niño–Southern Oscillation (ENSO) in shaping these variations remains insufficiently explored. This study investigates ENSO‐driven seasonal NSWS variability across China and their implications for wind power density (WPD). We demonstrate that ENSO exerts strongly season‐dependent impacts on WPD in subregions, spanning the ENSO‐developing summer to the decaying summer. During these active ENSO phases, 15%–50% of stations within that regions exhibit WPD anomalies exceeding ±10% relative to their seasonal climatology in response to a 1°C sea surface temperature change in the central‐eastern Pacific during the ENSO peak winter. Furthermore, 850 hPa wind speeds show coherent variations with NSWS, indicating a strong dynamic connection between the lower troposphere and surface. These findings deepen our understanding of ENSO's influence in driving seasonal wind resources, providing actionable insights for regional wind energy management and strategic resource planning. Plain Language Summary Understanding variations in near‐surface wind speed (NSWS) is crucial for optimizing wind energy exploitation, particularly as China expands its renewable energy capacity to meet rising demand. This study examines the impact of the El Niño–Southern Oscillation (ENSO), a recurring climate phenomenon characterized by fluctuations in Pacific Ocean surface temperatures, on the seasonal variability of NSWS and wind power density (WPD) across China. Using station observations and reanalysis data sets, we show that ENSO significantly modulates NSWS and WPD in Northeast, North, Southwest, and South China. For instance, a typical El Niño event, with a 1°C increase in winter sea surface temperature in the central‐eastern Pacific, can cause WPD variations exceeding ±10% of the seasonal average at up to half of the monitoring stations in these regions. Additionally, our analysis reveals a strong link between ENSO‐driven wind changes in the lower troposphere and at the surface, highlighting a coupled response. These insights suggest ENSO's role in modulating wind energy potential, aiding seasonal wind resource forecasting. Such understanding is essential as China continues expanding its wind energy infrastructure and adapting to climate‐driven wind variations during ENSO events. Key Points El Niño–Southern Oscillation has significant influences seasonal variations in near‐surface wind speed (NSWS) across Northeast, North, Southwest, and South China A 1 K winter Niño 3.4 warming induces wind power density changes exceeding ±10% at 15%–50% of monitoring stations Coherent variations in 850 hPa wind speeds and NSWS indicate a tight dynamic coupling between the lower troposphere and the near‐surface levels

Previous research has extensively explored the “stilling” and “reversal” phenomena in annual near‐surface wind speed (NSWS). However, the variations in the strengths of these phenomena between different months remain unclear. Here the monthly changes in observed NSWS from 769 stations across China during 1979–2020 were analyzed. The analysis reveals a consistent decline in NSWS that ceased around 2011, followed by an increasing trend detected in all months except March, where a distinct hiatus is observed. The hiatus in March NSWS is primarily attributed to a significant reduction in NSWS over North and Northwest China. This reduction can be linked to the southward shift of the East Asian subtropical jet (EASJ), which resulted in a decreased meridional temperature gradient and weakened transient eddy activities across northern China. These findings emphasize the importance of considering changes in the EASJ to gain a comprehensive understanding of NSWS changes at regional scale. Plain Language Summary Understanding how near‐surface wind has changed and identifying the factors driving these changes are crucial. This can help in developing adaptation strategies to increase society's resilience to possible future climate, such as understanding the future revenues of electricity production from wind farms. By analyzing wind observations from 769 stations across China since 1979, we confirmed a general decrease (stilling) that ceased around 2011, followed by a general significant increasing tendency (reversal) in all months but March. Indeed, March's wind series after 2011 showed a pause (i.e., hiatus) from the 1979–2011 slowdown. This hiatus was mainly caused by the general wind reduction across northern China since 2011, which differs from the wind increase observed in other regions. The slowdown in March from 2011 to 2020 is related to the southward shift of East Asian subtropical jet streams, which are fast‐flowing, narrow, and meandering air currents in the upper atmosphere. Jet streams play an important role in shaping both upper and lower air circulation and influence surface wind by transporting high and low‐pressure systems. Key Points March near‐surface wind speed (NSWS) over China experienced a hiatus after 2011, distinct from other months The observed hiatus in March NSWS was primarily caused by a significant reduction in NSWS over North and Northwest China A southward shift of the East Asian subtropical jet may have contributed to the detected hiatus

We utilize observations from the Parker Solar Probe (PSP) to study the radial evolution of the solar wind in the inner heliosphere. We analyze electron velocity distribution functions observed by the Solar Wind Electrons, Alphas, and Protons suite to estimate the coronal electron temperature and the local electric potential in the solar wind. From the latter value and the local flow speed, we compute the asymptotic solar wind speed. We group the PSP observations by asymptotic speed, and characterize the radial evolution of the wind speed, electron temperature, and electric potential within each group. In agreement with previous work, we find that the electron temperature (both local and coronal) and the electric potential are anticorrelated with wind speed. This implies that the electron thermal pressure and the associated electric field can provide more net acceleration in the slow wind than in the fast wind. We then utilize the inferred coronal temperature and the extrapolated electric + gravitational potential to show that both electric field driven exospheric models and the equivalent thermally driven hydrodynamic models can explain the entire observed speed of the slowest solar wind streams. On the other hand, neither class of model can explain the observed speed of the faster solar wind streams, which thus require additional acceleration mechanisms.

As near‐surface wind speed (NSWS) largely controls wind power generation, robust projection is vital to wind energy planning and broader sustainability goals. However, the predictive skill of climate models for NSWS remains uncertain. Analysis of Coupled Model Intercomparison Project Phase 6 simulations shows that the models reproduce the mean NSWS over China reasonably well but substantially underestimate the observed long‐term trend and variability. Given these large biases, bias correction is essential for obtaining more reliable NSWS projections. We correct this bias using an Empirical Orthogonal Function approach to isolate the dominant spatial modes of NSWS variability. The corrected projections exhibit amplified future trends and increased variability of NSWS over China compared with the original model output, with the strongest changes emerging under higher greenhouse gas emissions. Our results indicated that unadjusted models may understate the magnitude of future NSWS changes. This study provides a more reliable reference for future evolution of NSWS.

Near‐surface wind speed (NSWS) over China shows multiple time‐scale changes at a centennial scale, but the contributions of internal variability (IV), anthropogenic forcing (ANT), and natural forcing (NAT) to those changes remain unknown. This study investigated the contributions of IV, ANT, and NAT to NSWS changes at a centennial scale. Results show that the NSWS changes were attributed mainly to IV. IV not only modulated the interannual changes in NSWS but also determined the interdecadal transition in NSWS. The relative contributions of IV to the interannual and decadal NSWS exceeded 75.0%. ANT contributed particularly to the long‐term reduction in NSWS; especially, it has contributed 55.0% of the reduction in NSWS since 1957, serving as the major contributor to the reduction in NSWS. NAT had a small‐to‐negligible effect on China's NSWS throughout the study period. This study enhances our understanding of NSWS changes at different time scales. Plain Language Summary Near‐surface wind speed (NSWS) is crucial because it can influence energy, water, and air move between the Earth's surface and the atmosphere, which can also affect weather and climate systems like dust storms, evaporation rates, and the water cycle. In the past decades, interannual and interdecadal changes in NSWS, as well as the long‐term trend of NSWS have been analyzed; however, the causes behind these changes are not clear. Our research focuses on understanding these changes over nearly a century. We discover that internal variability (IV) is a primary factor driving these changes in NSWS, especially in terms of its fluctuations and shifts over decades. In addition to IV, anthropogenic forcing also plays a crucial role, particularly for the decrease of NSWS since 1957. On the other hand, natural forcing seem to have a minimal or almost no impact on NSWS changes in China during the study period. This study not only enhances our understanding of NSWS changes over multiple time scales but also provides essential information for policymakers to develop climate strategies and adaptation measures. Key Points Internal variability determines the interannual and interdecadal changes in near‐surface wind speed (NSWS) across China Anthropogenic forcing is responsible for the slowdown in NSWS since 1957, its contribution reaches 55.0% Natural forcing has a small‐to‐negligible influence on the changes in NSWS

Technology for anticipating wind speed can improve the safety and stability of power networks with heavy wind penetration. Due to the unpredictability and instability of the wind, it is challenging to accurately forecast wind power and speed. Several approaches have been developed to improve this accuracy based on processing time series data. This work proposes a method for predicting wind speed with high accuracy based on a novel weighted ensemble model. The weight values in the proposed model are optimized using an adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) algorithm. The original GWO algorithm is redesigned to emulate the dynamic group-based cooperative to address the difficulty of establishing the balance between exploration and exploitation. Quick bowing movements and a white breast, which distinguish the dipper throated birds hunting method, are employed to improve the proposed algorithm exploration capability. The proposed ADGWDTO algorithm optimizes the hyperparameters of the multi-layer perceptron (MLP), K-nearest regressor (KNR), and Long Short-Term Memory (LSTM) regression models. A dataset from Kaggle entitled Global Energy Forecasting Competition 2012 is employed to assess the proposed algorithm. The findings confirm that the proposed ADGWDTO algorithm outperforms the literature’s state-of-the-art wind speed forecasting algorithms. The proposed binary ADGWDTO algorithm achieved average fitness of 0.9209 with a standard deviation fitness of 0.7432 for feature selection, and the proposed weighted optimized ensemble model (Ensemble using ADGWDTO) achieved a root mean square error of 0.0035 compared to state-of-the-art algorithms. The proposed algorithm’s stability and robustness are confirmed by statistical analysis of several tests, such as one-way analysis of variance (ANOVA) and Wilcoxon’s rank-sum.

In this paper, we present a novel approach for forecasting weather variables that are not currently available in many state‐of‐the‐art AI models. A variable not found in most models is the 100‐m wind speed, which is commonly used in the energy sector to predict the power generated by wind turbines. We trained a convolutional neural network model on 12 years of ERA5 data to instantaneously predict the 100‐m wind speed based on a subset of variables found in the ECMWF‐AIFS forecast. We evaluated our model with 2020 ERA5 data and achieved an average 100‐m wind speed RMSE of 0.18 m/s, outperforming the wind profile power law method with an RMSE of 0.63 m/s. Using the AIFS output as input to our trained model, we generated 10‐day 100‐m wind speed forecasts without requiring autoregressive steps, significantly reducing computational costs. We compared our predictions with the ECMWF‐IFS forecast using the ECMWF analysis as ‘ground truth’ and showed greater accuracy at longer lead times. Additionally, we produced power generation forecasts for onshore and offshore wind farms across the United Kingdom, with improvements over the IFS after a lead time of 3 days. We also showed that our model exhibits spatial and temporal coherence between local predictions and discussed the common limitation of over‐smoothing in the predictions of AI models. This paper introduces a novel approach to forecast the 100 m wind speed, a key variable in wind power generation forecasting often missing from AI models. Using a convolutional neural network trained on ERA5 data, we leverage the ECMWF‐AIFS outputs to produce an improved power generation forecast, outperforming traditional methods and the ECMWF‐IFS after day 3 lead times.

Wind farms are enormous and complex control systems. It is challenging and valuable to control and optimize wind farms. Their applications are widely used in various industries. Artificial intelligent algorithms are effective methods for optimization problems due to their distinctive characteristics. They have been successfully applied to wind farms. In this paper, several issues in wind farms are presented. Applications of artificial intelligent algorithms in wind farm controllers, Mach number, wind speed prediction, wind power prediction and other problems of wind farms are reviewed. Two future research directions are pointed out to develop artificial intelligent algorithms for wind farm control systems and wind speed and power prediction.