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ABSTRACT 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.

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

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

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.

Global warming is expected to have far‐reaching impacts on the frequency and intensity of extreme events, but the effects of anthropogenic warming on the emergence seasons of year‐maximum near‐surface wind speed (NSWS) remain poorly understood. We provide a comprehensive map of the changing emergence seasons of year‐maximum NSWS using Coupled Model Intercomparison Project Phase 6 projections. Our analysis reveals a rapid response of synoptic‐scale extreme NSWS to global warming, with consistent spatial patterns observed across various periods and warming scenarios. The most significant increase (∼16%) in the emergence season is projected to occur in December‐January‐February (DJF) over Mid‐high‐latitude Asia by the end of the 21st century. The study also anticipates changes in the emergence seasons of year‐maximum NSWS at a regional scale. These results deepen our understanding of the complex and interconnected nature of global climate change and underscore the need for concerted efforts in addressing this pressing challenge. Plain Language Summary Global warming is indisputably triggering changes in the world's weather systems, leading to more frequent and intense extreme weather events. However, it is unclear how anthropogenic warming affects the timing of the annual strongest near‐surface wind speed (NSWS). In this study, we used state‐of‐the‐art global climate models to create a comprehensive map illustrating these NSWS patterns of response to global warming. We discovered that these changes are consistent across various time periods (near to long term) and warming scenarios (low to high warming), revealing a robust relationship between extreme NSWS and global warming. The most significant change is observed during December‐January‐February in Mid‐high‐latitude Asia, with an increase of about 16% by the end of the 21st century. Our findings suggest that we can expect more year‐maximum NSWS occurs in different regions during specific seasons: December‐January‐February in North America and Asia, March‐April‐May in Africa, June‐July‐August in Asia and West Africa, and September‐October‐November in South America and Australia. These results offer valuable insights for guiding adaptation efforts even if ambitious climate actions manage to limit global warming at a lower level. Key Points Changing emergence seasons of the land year‐maximum near‐surface wind speed (NSWS) map is created There is a rapid response of emergence seasons of year‐maximum NSWS to anthropogenic warming The strongest increase (16%) in emergence season is projected to occur in December‐January‐February over Mid‐high‐latitude Asia

Accurate wind speed forecasts are essential for optimizing the efficiency of wind energy operations. Currently, there is limited research on nationwide assessment of predictive performance in multiple numerical weather prediction (NWP) models for wind speed at turbine hub height over China, especially concerning wind ramp events. Utilizing observed measurements from 262 wind farms, this study evaluated the performance of five NWP models in forecasting the mean state and spatiotemporal variations of wind speed as well as wind ramps. The results indicated that the European Center for Medium‐Range Weather Forecast Integrated Forecasting System (ECMWF–IFS) performed the best in forecasting climatological wind speed with a temporal correlation coefficient (TCC) of 0.74 and root mean square error (RMSE) of 2.34 m s−1. Although not widely utilized in China, the model from Meteo‐France (MF–ARPEGE) showed promising potential for wind energy forecasting with a TCC of 0.72 and RMSE of 2.45 m s−1. In terms of temporal variations of wind speed, all the models could reasonably predict the seasonal variations of wind speed, whereas only three NWP models were able to capture the characteristics of the observed diurnal variation. An error decomposition analysis further revealed that the primary source of predicted error for wind speed was the sequence error component (SEQU), indicating the model errors were mainly attributed from the temporal inconsistency between forecasts and observations. Furthermore, the occurrences of wind ramps were generally underestimated by NWP models, while this shortcoming can be partly overcome by improving the time resolution of NWP models. European Center for Medium‐Range Weather Forecast Integrated Forecasting System (ECMWF–IFS) and Meteo‐France (MF–ARPEGE) performed the best in forecasting the hub‐height wind speed, accounting for 80.4% and 89.9% of total 262 wind farms with the highest temporal correlation coefficient (TCC) and the lowest root mean square error (RMSE), respectively.

Decadal variations in near‐surface wind speed (NSWS) and their causes are poorly understood. We found that the decadal transition of winter NSWS in northern China (NC) was 10 years earlier than in southern China (SC), which could be linked to the changes in intensities of the eastward wave‐activity flux and Siberian High (SH) induced by the Warm Arctic‐Cold Eurasia (WACE) dipole pattern. From 1973 to 1990, the WACE pattern from positive to negative phases confined the eastward wave trains to high latitudes with a decreasing SH, inducing an NSWS reduction. From 1991 to 2000, the WACE strengthened from negative to positive phases, causing a decadal transition in NSWS first in NC. After 2000, accompanied by the strengthening of the positive WACE, the eastward wave trains propagated downstream to lower latitudes, the SH and the meridional pressure gradient enhanced. Therefore, the transition of decadal NSWS occurred in SC until 2000. Plain Language Summary Near‐surface wind speed (NSWS) is critical in exchanging energy, water, and momentum between the Earth's surface and the lower atmosphere. Previous studies have reported that the slowdown in NSWS and its reversal could be a manifestation of decadal variations in the climate system. However, the regional non‐synchronization of decadal variations in NSWS and the corresponding cause are poorly understood. This study reported a non‐synchronization of the decadal transition in winter NSWS between northern China (NC) and southern China (SC). The significant turning point of winter NSWS across NC was 10 years earlier than those across SC, and it was caused mainly by the zonal wind. The non‐synchronization of decadal variations in winter NSWS between NC and SC was linked to the Warm Arctic‐Cold Eurasia (WACE) atmospheric circulation pattern, and the Siberian High (SH) could serve as a bridge through which the WACE atmospheric circulation pattern influences the asynchronous transition of decadal NSWS between NC and SC. This study improves the understanding of decadal variations in NSWS across China. Key Points The decadal near‐surface wind speed (NSWS) transition in winter over northern China (NC) was 10 years earlier than in southern China (SC) The non‐synchronization of the decadal transition in NSWS between NC and SC was linked with the Warm Arctic‐Cold Eurasia pattern Intensities of wave‐activity flux and Siberian High influenced the non‐synchronization of decadal transition in NSWS between NC and SC

Reanalysis products have become a tool for wind energy users requiring information about the wind speed long-term variability. These users are sensitive to many aspects of the observational references they employ to estimate the wind resource, such as the mean wind, its seasonality and long-term trends. However, the assessment of the ability of atmospheric reanalyses to reproduce wind speed trends has not been undertaken yet. The wind speed trends have been estimated using the ERA-Interim reanalysis (ERA-I), the second version of the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) and the Japanese 55-year Reanalysis (JRA-55) for the period 1980-2015. These trends show a strong spatial and seasonal variability with an overall increase of the wind speed over the ocean and a tendency to a decline over land, although important disagreements between the different reanalyses have been found. In particular, the JRA-55 reanalysis produces more intense trends over land than ERA-I and MERRA-2. This can be linked to the negative bias affecting the JRA-55 near-surface wind speeds over land. In all the reanalyses high wind speeds tend to change faster than both low and average wind speeds. The agreement of the wind speed trends at 850 hPa with those found close to the surface suggests that the main driver of the wind speed trends are the changes in large-scale circulation.

Vortex-induced vibration (VIV) is a typical of large amplitude vibration for slender structure. Predicting the amplitude and wind speed range for VIV is vital and challenging in the wind-resistance design. The 4:1 rectangular cross-section cylinder is one of the representative structure for fundamental wind-induced vibration analysis. In this study, a novel wake-oscillator model tailored for predicting VIV in a 4:1 rectangular cylinder is proposed. Besides structural responses, this model can also conceptually reproduce the shedding behaviors of the vortices around the cylinder. For this purpose, two oscillators are employed and attached to the structure in the model. Oscillator 1, mounted on the rear face, simulates the swaying motion of the wake vortex. Oscillator 2, attached to the windward face, represents the variations in the main vortices generated from the leading edges. Governing functions of the two oscillators are derived according to the zero circulation assumption of the target region. The parameters are determined with accordance to both the rigid and aero-elastic model tests. The model’s validity is examined through the comparison of the predicted response amplitudes with experimental data. Results demonstrate that the model can effectively predict the cylinder’s responses across various Scruton numbers, using a single set of model parameters. This model is further applied to investigate the underlying mechanics of VIV excitation, focusing on the provided wind load and vibrating frequencies of Oscillators 1 and 2. These analyses helps to understand the structural VIV phenomenon.