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Though tropical cyclone (TC) models have been routinely evaluated against track and intensity observations, little work has been performed to validate modeled TC wind fields over land. In this paper, we present a simple framework for evaluating simulated low‐level inland winds with in‐situ observations and existing TC structure theory. The Automated Surface Observing Systems, Florida Coastal Monitoring Program, and best track data are used to generate a theory‐predicted wind profile that reasonably represents the observed radial distribution of TC wind speeds. We quantitatively and qualitatively evaluated the modeled inland TC wind fields, and described the model performance with a set of simple indicators. The framework was used to examine the performance of a high‐resolution two‐way nested Geophysical Fluid Dynamics Laboratory model on recent U.S. landfalling TCs. Results demonstrate the capacity of using this framework to assess the modeled TC low‐level wind field in the absence of dense inland observations. Plain Language Summary Some of the biggest human impacts of tropical cyclone (TC) winds come after the TC makes landfall. A skillful prediction of the radial distribution of winds is essential for forecasting TC‐induced inland hazards. However, the forecast skill of numerical hurricane models on inland TC wind fields has rarely been evaluated since it is challenging to collect wind observations during landfall, and the network of regular weather observations is too spread out to capture the strongest winds associated with a TC. This inhibits the improvement of forecast models and limits our understanding of the TC's inland evolution. Our work combines available inland in‐situ wind observations over the southeastern U.S. with existing TC structure theory, and presents a new “optimal” estimate of the post‐landfall winds. Our framework is found to be useful for evaluating the post‐landfall TC winds in hurricane forecast models. In addition, the new evaluation technique can intuitively demonstrate how well the model simulates TC intensity and structure. Key Points We introduce a new framework for evaluating modeled inland tropical cyclone (TC) wind fields with observation‐based, theory‐predicted wind profiles The theory‐predicted wind profile well represents the observed radial distribution of inland TC wind speeds We propose simple indicators to summarize the model performance on inland wind field predictions

This study was designed to investigate the nonlinear load behavior and fatigue life of distributed small wind turbines in natural turbulent wind fields through an in-situ field experiment. A test platform was deployed in a representative wind resource area in Hohhot, Inner Mongolia, using a self-developed 5 kW variable-pitch wind turbine prototype to perform synchronous high-frequency monitoring of wind speed, direction, and blade root loads. The aerodynamic loads were estimated via blade element theory, and rain-flow counting was used to identify the stress cycle history at the blade root, followed by fatigue life prediction using S-N curves and Miner’s linear damage model. A comparative study was conducted on two cases with comparable mean wind speeds but contrasting turbulence intensities. The analysis revealed that increased turbulence intensity significantly amplifies stress fluctuations, raising fatigue damage by 45.7% and reducing blade life by about 31.7%. The results further indicate that adjusting the pitch angle can effectively reduce excessive power output and peak structural loads, confirming the practical control performance of the newly developed pitch actuator. The research offers both measured data and theoretical insights for improving structural reliability and predicting fatigue life of small-scale wind turbines.

Fine modeling and fast prediction of regional wind field in the middle and upper atmosphere has always been a difficult problem. We designed a neural operator method to solve this problem. We combine the idea of data assimilation with deep learning method to design a regional wind field operator suitable for near space. The annual Root mean square error of the zonal wind and meridional wind of the operator model at the height of 30 km are 0.903 and 0.881, respectively, which is three times that of ConvLSTM. Moreover, we validate the sparse spatio‐temporal modeling method of regional wind field operator at 20/30/40/50 km altitude. The result shows that the model is mesh‐free, and can get high‐precision modeling of different spatio‐temporal resolutions, multiple regions and arbitrary positions at one time, which lays an foundation for fine regional modeling and rapid utilization of near space. Plain Language Summary The complex variation mechanism of regional wind fields in near space leads to the difficulty of high‐precision modeling and fast prediction, which seriously affects the design and flight of near space vehicles. In this study, a regional wind field neural operator method has been proposed, which can achieve the fine modeling of the regional wind field in the middle and upper atmosphere. The new method is highly flexible, and can get high‐precision modeling and rapid prediction in different spatial‐temporal resolutions, multiple regions and arbitrary positions. Key Points The neural operator is first used to study high‐precision spatio‐temporal modeling and rapid prediction of regional wind fields in near space The Root mean square error accuracy of regional wind field operator model is three times that of ConvLSTM The novel method is suitable for sparse spatio‐temporal modeling at any location with different data resolutions

The wind direction is closely linked to the power performance and structural loads of wind turbines. Conventional nacelle‐mounted vanes or sonic anemometers face errors associated with airflow distortions caused by turbine blades. Nacelle‐mounted lidar systems offer line‐of‐sight speed measurements from multiple positions ahead of the rotor and rely on wind field reconstruction methods to predict the wind direction. This work considers three methods: the matrix inverse, the velocity azimuth display, and the physics‐informed neural network (PINN)–based methods. The first two are industrialized techniques that assume homogeneous flow. For flat terrain and offshore sites, the inhomogeneity of the mean flow is influenced by time‐averaging windows and turbine wakes. To illustrate the limitations and potential bias of wind direction estimates with homogeneous flow assumptions, we first present the bias using site measurement data. We then formulate a theoretical bias for a typical two‐beam lidar system. Next, we use openly available large eddy simulation data to evaluate the minute‐averaged wind direction estimates produced by the three methods. The first two methods are found to be unreliable, with maximum errors reaching close to 25° in the unwaked scenario and exceeding 30° in the waked case. As for the PINN‐based method, the errors remain within 10° across unwaked, waked, nonyawed, and yawed scenarios, even when only a 2D nonlinear convection equation is used as the physical constraint.

Atmospheric circulations bring moisture from above the ocean to the high mountains of the western Tibet Plateau (TP), thus wind variability is of great importance to the water cycle centered on the western TP. This study therefore examines the leading modes of the wind fields over the western TP. We use the multivariate empirical orthogonal function (MV-EOF) analysis method to detect the dominant wind patterns above the western TP. This method extracts the leading modes of combined meridional and zonal wind variability at 200-hPa in the region of 22° N–50° N, 50° E–92° E. We find the first leading mode of the combined zonal and meridional wind field in the annual mean and in most seasons (spring, summer and autumn) over the western TP shows high similarity to the Western Tibetan Vortex (WTV), a large-scale atmospheric vortex-like pattern recently recognized over the western TP. In winter, the WTV, however, is closer to the second leading mode. We estimate the sensitivity of our results by changing the domain of the MV-EOF analysis region surrounding the western. We find the influence of the WTV is less sensitive to analysis location around the western TP. In short, the WTV generally represents the first leading mode of the wind field in most seasons over the western TP. This study augments our knowledge on wind variability over the western TP.

The Qinghai–Tibet Plateau is characterized by highly complex terrain, and civil aviation serves as a primary mode of transportation for regional mobility. A comprehensive understanding of wind field characteristics within the terminal areas of plateau mountain airports, as well as the formation mechanisms of wind shear during different flight phases, is of considerable importance for flight risk assessment, improvement of transport efficiency, and refined meteorological support services. However, studies focusing on wind field structures within the terminal areas of plateau mountain airports remain limited. In this study, dry-season observations from Coherent Doppler Wind Lidars at two critical locations in the terminal area of Lhasa Airport are analyzed. A comparative analysis is conducted on the vertical structure, diurnal variation, and the characteristics of turbulence and wind shear under different terrain conditions. The results show that above the valley height, both sites are dominated by stable westerly winds. Below the valley height, the wind field is strongly influenced by terrain complexity. At the Lhasa Airport site (LS), the valley is regular in shape and has a stable orientation. The prevailing wind direction is aligned with the valley, and easterly winds dominate the entire valley, especially in the middle and lower layers. In contrast, the Qushui site (QS) is located at the confluence of two valleys, where the terrain is more open and complex. The prevailing wind shifts clockwise with height, from northeasterly in the lower layers to easterly aloft. The wind direction is less concentrated than at LS. In terms of diurnal variation, a stable easterly layer forms within the valley at LS in the morning. A transition layer of about 200–300 m exists between this layer and the westerlies aloft. Within the transition layer, wind speed is relatively weak and wind direction stability is low. At QS, morning winds are weaker and more variable within the valley. Wind direction stability increases with height. In the afternoon, both sites are influenced by the downward transport of westerly momentum. However, the effect is more pronounced at QS, where low-level wind speed is higher and wind direction is more stable. Turbulence at both sites peaks between 14:00 and 17:00 and is mainly driven by thermally induced updrafts. Turbulence intensity at QS is stronger, with a vertical extent exceeding 1500 m, indicating a stronger response to thermal forcing. Wind shear at both sites mainly occurs between 12:00 and 18:00, with peak frequency from 13:00 to 17:00. This period is consistent with peak turbulence activity. Wind shear at LS occurs more frequently and lasts longer. At QS, momentum transport from above 1500 m enhances wind shear occurrence at 800–1000 m. The causes of wind shear differ under different prevailing wind conditions. Under prevailing westerlies, wind shear is mainly caused by rapid changes in wind direction with height. Under prevailing easterlies, it is primarily associated with an enhanced vertical gradient of wind speed. These results reveal the significant influence of complex terrain on low-level wind structures and causes of wind shear. The findings provide a scientific basis for operational decision-making at plateau mountain airports.

The Holland model is a common and efficient parametric model for constructing the typhoon wind field and involves two critical parameters: the radius of maximum wind (RMW) and the Holland B parameter, which are sensitive to the azimuth of a typhoon prior to landfall. However, their azimuth dependencies cannot be well addressed in the original mathematical expression. This study developed a framework for improving the Holland model by considering the azimuth-dependent RMW and Holland B parameter for typhoons along the Zhejiang coast prior to landfall. The Weather Research and Forecasting (WRF)–Advanced Research WRF (ARW) model was applied to construct the precise database of wind fields for six historical typhoons that passed through the Zhejiang coast. The azimuth-dependent functions were proposed to describe the asymmetry of the RMW and Holland B parameter, of which the coefficients were obtained by regression analyses of the precise database of historical typhoons. The improved azimuth-dependent model was then illustrated to assess the coastal hazards along Zhejiang Coast, including wind speeds with 50- and 100-year return periods and storm surges. The asymmetries of the RMW and Holland B parameter were expressed as functions of the heading direction and latitude of the typhoon center, respectively. The improved model showed good agreement with the observations during Typhoon Winnie (9711), Prapiroon (0012), and Khanun (0515). Including azimuth dependency in typhoon wind fields can provide more specific and reliable information in estimating coastal hazards under typhoons.

Taiwan, frequently impacted by typhoons, faces significant risks to offshore wind turbines from extreme winds. Accurate prediction of typhoon boundary layer characteristics is crucial for offshore wind energy development. This study employed a coupled Weather Research and Forecasting (WRF) model and large-eddy simulations (LES) to investigate the effects of atmospheric parameters (e.g., temperature and pressure) on typhoon characteristics during a historical event (Typhoon Nesat, 2017). High-resolution wind fields near an offshore wind farm were generated and validated by on-site meteorological mast data, with the evaluated peak mean wind speed of 31.8 m/s, only 0.3 m/s higher than the observed value. The results showed that on-site wind speeds were highly sensitive to deviations in the WRF-simulated typhoon track. In addition, the LES-generated turbulence underestimated the actual conditions, especially in the period following the primary extreme wind impact. Moreover, direct assimilation of mesoscale wind data into the microscale model overstated turbulence in the typhoon wake, with turbulent kinetic energy 2–4 times higher than those with indirect assimilation. Overall, however, the proposed WRF-LES model provides a powerful approach for simulating typhoon with moderate accuracy. The high-resolution wind fields generated can be utilized for wind turbine simulations, benefiting wind energy development in Taiwan.

Heavy precipitation, triggered by the residual circulation from Typhoon Fitow (2013), struck Shanghai with a spatial inhomogeneity distribution. Using multi-physical diagnostic analysis based on automatic weather stations (AWS) observations, the fifth-generation ECMWF re-analyses (ERA5), FY satellite products, and typhoon track data, this study investigates the relationship between the surface wind field and precipitation in Shanghai. The main results are as follows: (1) The Shanghai Central Area (SCA) decelerated the eastward progression of the northerly wind associated with the typhoon’s residual circulation. The low-wind area and wind deflection around the SCA enhanced the wind field convergences in its surrounding regions. (2) The slow-moving convective systems prolonged the duration of rainfall, while the wind deflection around the SCA further strengthened surface wind convergence conditions. (3) Intense surface Q vector convergence persisted between the northerly wind from the residual circulation and the northeasterly maritime wind. Precipitation attenuated rapidly within 2 h following the weakening of the Q vector convergence, suggesting that this parameter served as a reliable indicator for the development of the studied precipitation event. Additionally, surface water vapor advection was channeled by wind deflection around the SCA, leading to water vapor accumulation in both upwind and downwind areas of the SCA.

The simulation of near-surface typhoon wind fields is crucial for high-precision typhoon hazard assessments and thus of great significance for disaster forecasting, loss risk assessment and emergency management. The terrain correction method for simulating regional large-scale wind fields has a single correction method that cannot satisfy the requirements of refined risk assessment. This paper aims to use the advantage with regard to accuracy of the fluid dynamics mechanism model (CFD, computational fluid dynamics) in small-scale wind speed simulations and obtain a terrain correction method suitable for simulating regional large-scale wind fields by extracting the spatial variation of the wind speed over complex terrain. Specifically, typical mountains with different cross-sectional shapes and slopes are used to characterize the undulating terrain, and the CFD model is used to simulate and analyze the wind speed changes on the upwind and leeward slopes, at the mountain top, and in the downwind area under different initial wind speeds. The wind speed at these locations has a good quantitative relationship with the initial wind speed. Combined with the typical building wind load codes in China, the wind speed correction algorithm suitable for large-scale complex terrain is supplemented and improved. This paper presents the simulation results of three typhoons, and taking Typhoon No. 0713 as an example, a near-surface typhoon wind field simulation is performed. Compared with that of other models, the accuracy of the terrain-corrected simulation results by the method provided in this article is increased by 8.8–16.89%. Such CFD-based typhoon disaster near-surface wind fields can more accurately reflect the spatial distribution and intensity of typhoon wind hazards over large-scale complex terrain and can provide technical support for the loss risk prediction and assessment of forest resources, mountain forestry economies, crops and other vulnerable bodies during typhoon disasters.