Accurate fitting of coastal wind profiles is of great significance for objective forecasting of winds, fusion of multi-source wind speed observations, and wind energy assessment. Based on observations collected at 32 coastal wind towers, the variation of the power law exponent is analyzed and the impact of different power law exponent quantification methods on vertical wind speed extrapolation is assessed. The results show that the coastal power law exponent varies with observation height, season, and time of day, and the variation trend is spatially different. Further comparative analysis reveals that the power law exponent shows a consistent exponential decay with increasing wind speed. Compared with the fixed value quantification method based on least squares, using the power law exponent that is optimally fitted by an exponential function and fluctuates with wind speed for vertical wind speed extrapolation is meaningful and it can reduce the root mean square error of the extrapolation results by up to about 49%. However, due to the impacts of factors other than wind speed, the fitting accuracy of the power law exponent by wind speed and the effect of its application in fitting the coastal wind profile is unstable. Further research is needed to evaluate the possibility of cutting-edge technologies such as machine learning in the vertical fitting of wind fields.

Atmospheric reanalysis is a process that utilizes numerical weather prediction (NWP) models and data assimilation systems to integrate historical meteorological observations, thereby reconstructing datasets that represent past atmospheric states. This paper presents the China Meteorological Administration’s (CMA) latest global atmospheric reanalysis product, CRA1.5, which employs the Global Spectral Model (GSM) and the Gridpoint Statistical Interpolation (GSI) Data Assimilation system. CRA1.5 features a model spectral resolution of T_L1534 (equivalent to approximately 13 km in horizontal grid spacing), a product time interval of 1 h, a model top at 0.27 hPa, and a hybrid-4DEnVar scheme along with an Ensemble Kalman Filter for data assimilation. The temporal coverage of CRA1.5 extends from 1979 to near real-time. During the reanalysis production, we assimilated a substantial volume of reprocessed satellite data and extensive conventional observations. These observations include dense networks of conventional observations within China (comprising approximately 120 sounding stations and 2400 meteorological stations) as well as satellite observations from China’s Fengyun, Haiyang, and Yunyao satellite series. Compared to CRA-40, the root-mean-square error (RMSE) of geopotential height at 500 hPa is reduced by 22.46%, the RMSE of temperature at 200 hPa is reduced by 21.7%, and the RMSE of zonal wind at 850 hPa is reduced by 14.41%. CRA1.5 outperforms ERA5 in 100-m wind speed over China (RMSE: 3.21–3.36 m s⁻¹ vs. 3.46–3.59 m s⁻¹) and achieves comparable accuracy to ERA5 in atmospheric precipitable water (RMSE: 3.10 mm vs. 3.13 mm). CRA1.5’s 2-m temperature trend aligns closely with HadCRUT5, capturing global warming accurately. With higher spatiotemporal resolution (0.1° × 0.1°, hourly) and advanced assimilation, CRA1.5 provides a critical dataset for climate monitoring, NWP, and AI-based meteorological research. CRA1.5 has been implemented for real-time operation at the CMA. Updates are available up to 6 h after the analysis time, with a final update provided at a 3-day lag.