Wind shear is one of the crucial parameters in wind resource assessment and also serves as a vital parameter and basis for determining wind turbines’ selection and hub height. Existing studies have only focused on typical underlying surface areas, but a relatively limited comprehensive analysis of wind shear characteristics in different complex environments remains. This study analyzes the daily and monthly variations in wind shear index (α) at the station scale based on the observations from 754 wind measurement towers across land surfaces in China. The distribution and empirical values of wind shear in different wind regions and underlying surface types are also investigated. The research findings indicate that the wind shear index derived from fitting the complete annual average wind speeds at multiple height levels of meteorological towers can accurately characterize the stratification state of the atmospheric boundary layer. The variation pattern of solar radiation influences the daily α value in typical regions. In mountainous and desert areas, the monthly variation tends to be higher in autumn and winter and lower in spring and summer. However, its monthly variation shows relatively smaller fluctuations in plain regions. The comprehensive α value over land regions in China is 0.135. The α values for I, II, III, and IV wind fields are 0.111, 0.163, 0.1, and 0.153, respectively. Its values for mountainous, plains, grassland, and desert regions are 0.12, 0.273, 0.123, and 0.104, respectively. By conducting statistical analysis on α values across different wind regions, guidance is provided for extrapolating surface wind speeds to hub-height wind speeds. This serves as a reference for wind energy resource assessment, wind turbine selection, and hub height determination in the atmospheric boundary layer of China.

Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy’s future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (~2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m·s⁻¹. The overall wind speed distribution range is significant, meeting the requirements for wind farm development.

Improving the accuracy of the evaluation of the performance of wind farms in large wind power bases located in complex terrain under the actual atmosphere is crucial to the sustainable development of wind power. To this end, this study combined the Weather Research and Forecasting (WRF) model with the Wind Farm Parameterization (WFP) method to investigate the wake characteristics and operational performance of large onshore wind farms in the complex terrain of Jiuquan City, Gansu Province, China. The research results showed that after verification, the systematic error of the WRF simulations was less than 3%. The WRF model and the WFP scheme simulated a significant warming phenomenon within the wind power base area, while a cooling effect was observed outside. The analysis of the wake effects indicated that the impact of Phase I construction on Phase II construction of the wind power base was minimal. During the operation of the entire wind power base, the wind speed within the wind farm decreased by approximately 10%, and the influence range of the predominant wind direction extended over a hundred kilometers downwind. The research conclusions provide a powerful scientific basis for optimizing design and operation, improving efficiency, minimizing the negative impacts on adjacent wind turbines, and ensuring the sustainable development of wind energy through dynamic planning and scientific assessment.

With growing global demand for clean energy, offshore wind resource assessment is crucial. This study comprehensively utilizes multi-source Synthetic Aperture Radar (SAR) satellite data, including European Sentinel-1 and Chinese Gaofen-3 satellites, along with in-situ wind mast measurements, to systematically assess the wind energy resources in the offshore area of Zhuanghe, Liaoning Province, China, from 2017 to 2022. Sea surface wind fields were retrieved through data preprocessing, polarization conversion, geophysical model inversion, and multi-source data fusion. By coupling a vertical extrapolation model that accounts for sea state effects, the 10‑m wind speeds were extrapolated to turbine hub heights of 40 m, 80 m, and 100 m, enabling the calculation of key wind energy parameters such as wind speed and wind power density. Results indicate that at 100 m height, the annual average wind speed is 6.40 m·s−1 and the annual average wind power density is 312.8 W·m−2, corresponding to wind class 2. Wind speeds are higher in spring and winter and lower in summer and autumn, with prevailing northerly winds. Compared with ERA5 reanalysis data, SAR data offer advantages including high spatial resolution, rich historical records, and superior local accuracy, making them suitable for detailed offshore wind resource assessment. This research fills the gap in high‑precision wind energy evaluation under complex meteorological and data‑scarce conditions in the region, provides reliable data support for local offshore wind farm planning and development, and offers transferable experience for the application of SAR technology in similar coastal areas in China.