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Long-span bridges in mountainous areas are greatly disturbed by wind, and the wind field at the mountain gorge bridge site is extremely complex. Therefore, it is of great engineering significance to accurately evaluate the wind field characteristics of this kind of terrain. In this paper, to enhance understanding of this kind of wind field, the wind field in a mountainous gorge is measured for a long time using wind radar, and the mean wind parameters are statistically analyzed. The results show that the mean wind parameters vary greatly under different wind directions, and the wind speed profile does not meet the power-law model. Therefore, a mixed model suitable for the wind speed profile in a mountainous gorge is proposed. Additionally, GEV distribution and Logistic distribution are found to be suitable for describing the distribution characteristics of wind speed and angle of attack, respectively. In addition, considering the correlation between wind parameters, this paper also constructs the joint probability model of wind speed and angle of attack at different heights by the Copula function. Thus, a combined wind parameters profile model is developed under different exceedance probabilities based on the inverse first-order reliability method (IFORM). This study can provide a reference for the construction of the joint probability model of wind parameters.

Version 5 (v05) of the thermospheric wind data from the Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) instrument on the Ionospheric Connection Explorer (ICON) mission has been recently released, which largely avoids local‐time dependent artificial baseline drifts that are found in previous versions of the ICON/MIGHTI wind data. This paper describes monthly climatologies of zonal‐mean winds and tides based on the v05 ICON/MIGHTI data under geomagnetically quiet conditions (Hp30 < 3o) during April 2020–March 2022. Green‐line winds in the lower thermosphere (90–110 km) and red‐line winds in the middle thermosphere (200–300 km) are analyzed, as these data cover both daytime and nighttime. The latitude and height structures of zonal‐mean winds and tides are presented for each month, and the results are compared with the widely used empirical model, Horizontal Wind Model 2014 (HWM14). The ICON/MIGHTI and HWM14 results are in general agreement, providing a validation of the v05 ICON/MIGHTI data. The agreement is especially good for the zonal‐mean winds. Amplitudes of lower thermospheric tides from ICON/MIGHTI tend to be larger than those from HWM14 as well as from an empirical model, Climatological Tidal Model of the Thermosphere (CTMT). This could be due to the influence of interannual variability of the tides. The amplitude structure of lower thermospheric tides in HWM14 does not match those from ICON/MIGHTI and CTMT in some months. Also, HWM14 underestimates the meridional‐wind amplitude of the migrating diurnal tide in the middle thermosphere. These results highlight the need for improved tidal representation in HWM14. Key Points Monthly climatologies of zonal‐mean winds and tides at 90–110 km and 200–300 km are determined using v05 Ionospheric Connection Explorer/Michelson Interferometer for Global High‐resolution Thermospheric Imaging (ICON/MIGHTI) observations ICON/MIGHTI and Horizontal Wind Model 2014 results are in general agreement, providing a validation of the Version 5 ICON/MIGHTI data The agreement is especially good for the zonal‐mean winds, while some discrepancies are found in tidal amplitudes

Since 1990s, the Ground-Based Velocity Track Display (GBVTD) – family of techniques, including the Generalized Velocity Track Display (GVTD), have been used to retrieve the kinematic structure of Tropical cyclones (TCs) using data from a single Doppler radar. This study builds on the GVTD formulation and proposes a single Doppler TC inner core mean wind retrieval technique, called GVTD-Mean Wind (GVTD-MW), that can improve the retrieved TC inner core structures and the understanding of TC intensity change. Results from analytical wind field experiments demonstrate that GVTD-MW can estimate mean wind speed and direction, with errors below 10% and 6°, respectively. GVTD-MW estimated mean winds are not sensitive to magnitude and direction of the along-beam mean wind component, vortex size, and uncertainty of the TC center. GVTD-MW was applied to Typhoon Nock-Ten (2004) and Typhoon Koinu (2023). For Nock-Ten, the significant cross-beam mean wind component enabled GVTD-MW to reduce the retrieval errors compared to GVTD. Consequently, the root mean square error (RMSE) for the entire volume decreased from 8.8 m s −1 (GVTD) to 7.6 m s −1 (GVTD-MW) when compared with radar observations. For Koinu, where the cross-beam mean winds were much smaller than those in Nock-Ten, GVTD-MW also improved the accuracy of storm structure retrieval. These findings demonstrate the characteristics and impacts of GVTD-MW in improving the TC structures from single-Doppler radar observations.

This paper discusses whether profiling LiDARs can replace meteorological tower-based wind speed measurement for wind energy applications without severely compromising accuracy. To this end, the accuracy of LiDAR is evaluated in a moderately complex terrain by comparing long-term wind data measured by a profiling LiDAR against those obtained from tower-mounted cup and sonic anemometers. The LiDAR-measured wind speeds show good agreement with those measured using the sonic anemometer, with the slope of regression line being 1.0 and R 2 > 0.99 . Furthermore, the turbulence intensity obtained from the LiDAR has better agreement with that from the sonic anemometer compared to the cup anemometer which showed the lowest turbulence intensities among the three devices. A comparison of the turbulence intensity obtained from the 90th percentile of the standard deviation distribution shows that the LiDAR-measured turbulence intensities are mostly larger (by 2% or less) than those measured by the sonic anemometer. The gust factors obtained from both devices roughly converged to 1.9, showing that LiDAR is able to measure peak wind speed with acceptable accuracy. The accuracy of the wind speed and power distributions measured using the profiling LiDAR are then evaluated by comparing them against the corresponding distributions obtained from the sonic anemometer. Furthermore, the annual capacity factor—for the NREL 5-MW wind turbine—from the LiDAR-measured wind speed is 2% higher than that obtained from the sonic anemometer-measured wind speed. Numerical simulations are performed using OpenFAST in order to compute fatigue loads for the wind speed and turbulence distributions for the LiDAR and the sonic anemometer measurements. It is found that the 20 years lifetime Damage Equivalent Loads (DELs) computed for the LiDAR wind speed were higher than those for the sonic anemometer wind speeds, by 2%–6% for the blade root bending moments and by 11%–13% for the tower base bending moments. This study shows that even with some shortcomings, profiling LiDARs can measure wind speeds and turbulence intensities with acceptable accuracy. Therefore, they can be used to analyze wind resource and wind power potential of prospective sites, and to evaluate whether those sites are suitable for wind energy development.

Surface wind speed (SWS) from meteorological observation, global atmospheric reanalysis, and geostrophic wind speed (GWS) calculated from surface pressure were used to study the stilling and recovery of SWS over China from 1960 to 2017. China experienced anemometer changes and automatic observation transitions in approximately 1969 and 2004, resulting in SWS inhomogeneity. Therefore, we divided the entire period into three sections to study the SWS trend, and found a near-zero annual trend in the SWS in China from 1960 to 1969, a significant decrease of −0.24 m s−1 decade−1 from 1970 to 2004, and a weak recovery from 2005 to 2017. By defining the 95th and 5th percentiles of daily mean wind speeds as strong and weak winds, respectively, we found that the SWS decrease was primarily caused by a strong wind decrease of −8% decade−1 from 1960 to 2017, but weak wind showed an insignificant decreasing trend of −2% decade−1. GWS decreased with a significant trend of −3% decade−1 before the 1990s; during the 1990s, GWS increased with a trend of 3% decade−1 whereas SWS continued to decrease with a trend of 10% decade−1. Consistent with SWS, GWS demonstrated a weak increase after the 2000s. After detrending, both SWS and GWS showed synchronous decadal variability, which is related to the intensity of Aleutian low pressure over the North Pacific. However, current reanalyses cannot reproduce the decadal variability and cannot capture the decreasing trend of SWS either.

Studies on urban ventilation indicate that urban ventilation performance is highly dependent on urban morphology. Some studies have linked local-scale urban ventilation performance with the local climate zone (LCZ) that is proposed for surface temperature studies. However, there is a lack of evidence-based studies showing LCZ ventilation performance and affirming the reliability of using the LCZ classification scheme to demonstrate local-scale urban ventilation performance. Therefore, this study aims to analyse LCZ ventilation performances in order to understand the suitability of using the LCZ classification scheme to indicate local-scale urban ventilation performance. This study was conducted in Shenyang, China, with wind information at 16 weather stations in 2018. The results indicate that the Shenyang weather station had an annual mean wind speed of 2.07 m/s, while the mean wind speed of the overall 16 stations was much lower, only 1.44 m/s in value. The mean wind speed at Shenyang weather station and the 16 stations varied with seasons, day and night and precipitation conditions. The spring diurnal mean wind was strong with the speeds of 3.56 m/s and 2.21 m/s at Shenyang weather station and the 16 stations, respectively. The wind speed (2.21 m/s at Shenyang weather station) under precipitation conditions was higher than that (1.75 m/s at Shenyang weather station) under no precipitation conditions. Downtown ventilation performance was weaker than the approaching wind background, where the relative mean wind speed in the downtown area was only 0.53, much less than 1.0. The downtown ventilation performance also varied with seasons, day and night and precipitation conditions, where spring diurnal downtown ventilation performance was the weakest and the winter nocturnal downtown ventilation performance was the strongest. Moreover, the annual mean wind speed of the 16 zones decreased from the sparse, open low-rise zones to the compact midrise zones, indicating the suitability of using LCZ classification scheme to indicate local-scale urban ventilation performance. The high spatial correlation coefficients under different seasons, day and night and precipitation conditions, ranging between 0.68 and 0.99, further affirmed that LCZ classification scheme is also suitable to indicate local-scale urban ventilation performance, despite without the consideration of street structure like precinct ventilation zone scheme.

Atmospheric winds are crucial to the transport of heat, moisture, momentum, and chemical species, facilitating Earth’s climate system interactions. Existing weather and climate studies rely heavily on the wind fields from reanalysis datasets. In this study, we analyze the uncertainty of instantaneous atmospheric winds in three reanalysis (ERA5, MERRA-2, and CFSv2) datasets. We show that the mean wind vector differences (WVDs) between the reanalysis datasets are about 3–6 m s −1 in the troposphere. The mean absolute wind direction differences can be more than 50°. Large WVDs greater than 5 m s −1 are found for 30%–50% of the time when the observed precipitation rate is larger than 0.1 mm h −1 over the eastern Pacific Ocean, Indian Ocean, Atlantic Ocean, and some mountain areas. The mean WVDs exhibit seasonal variations but no significant diurnal variations. The uncertainty of vertical wind shear has a correlation of 0.59 with the uncertainty of winds at 300 hPa. The magnitudes of vorticity and horizontal divergence uncertainties are on the order of 1 × 10 −5 s −1 , which is comparable to the mean values of vorticity and horizontal divergence. In comparison with some limited observations from field campaigns, the reanalysis datasets exhibit a mean WVD ranging from 2 to 4.5 m s −1 . Among the three reanalysis datasets, ERA5 shows the closest agreement with the observations while MERRA-2 has the largest discrepancy. The substantial uncertainty and errors of the reanalysis wind products highlight the critical need for new satellite missions dedicated to 3D wind measurements.

This is the first of two papers that document the creation of the New European Wind Atlas (NEWA). It describes the sensitivity analysis and evaluation procedures that formed the basis for choosing the final setup of the mesoscale model simulations of the wind atlas. The suitable combination of model setup and parameterizations, bound by practical constraints, was found for simulating the climatology of the wind field at turbine-relevant heights with the Weather Research and Forecasting (WRF) model. Initial WRF model sensitivity experiments compared the wind climate generated by using two commonly used planetary boundary layer schemes and were carried out over several regions in Europe. They confirmed that the most significant differences in annual mean wind speed at 100 m a.g.l. (above ground level) mostly coincide with areas of high surface roughness length and not with the location of the domains or maximum wind speed. Then an ensemble of more than 50 simulations with different setups for a single year was carried out for one domain covering northern Europe for which tall mast observations were available. We varied many different parameters across the simulations, e.g. model version, forcing data, various physical parameterizations, and the size of the model domain. These simulations showed that although virtually every parameter change affects the results in some way, significant changes in the wind climate in the boundary layer are mostly due to using different physical parameterizations, especially the planetary boundary layer scheme, the representation of the land surface, and the prescribed surface roughness length. Also, the setup of the simulations, such as the integration length and the domain size, can considerably influence the results. We assessed the degree of similarity between winds simulated by the WRF ensemble members and the observations using a suite of metrics, including the Earth Mover's Distance (EMD), a statistic that measures the distance between two probability distributions. The EMD was used to diagnose the performance of each ensemble member using the full wind speed and direction distribution, which is essential for wind resource assessment. We identified the most realistic ensemble members to determine the most suitable configuration to be used in the final production run, which is fully described and evaluated in the second part of this study .

The ERA5 reanalysis product has been compared with hourly near-surface wind speed and gust observations across Sweden for 2013–2017. ERA5 shows closer agreement than the previous ERA-Interim reanalysis with regard to both mean wind speed and gust measurements, although significant discrepancies are still found for inland and mountainous regions. Therefore, attempts have been made to improve formulations of the gust parametrization used in ERA5 by adding an elevation-dependency and by adjusting the convective gust contribution. Major improvements, especially over mountain regions, are achieved when the elevation differences among the stations are considered. Closer agreement between the observed and parametrized gusts is reached when the convective gust contribution is also tuned. The newly designed gust parametrization was also tested for Norway, which is characterized by more complex topography. Wind gusts from the selected Norwegian stations are more realistically simulated when both the elevation-dependency and the tuned convective contribution are implemented, although the parametrized gusts are still negatively biased. Such biases are not explained by the different in gust duration in recorded wind gusts between Sweden and Norway.

Wave and meteorological observations from free‐drifting buoys deployed during four field campaigns were collated to evaluate the impact of wind gustiness on wave evolution. Met‐ocean conditions included wind speeds up to 25 m/s and mixed seas and swell. Analysis of bulk characteristics indicates that wave heights in gusty winds are 20%–50% larger than wave heights in steady winds. Spectral characteristics, including the peak frequency, peak energy, and spectral tail shape, indicate more developed waves in gusty winds than steady winds for the same mean wind speed. Observed spectral differences are linked to wind‐wave growth through analysis of mean square slope, equilibrium friction velocity, and growth rate. The total energy input to the wave spectrum and the actively forced frequencies differ between gusty and steady winds. Results support hypotheses of a higher “effective” wind in gusty conditions and emphasize the need for robust formulation of gustiness in wave models.