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Strong winds have evident impacts on the environment and the society. It can affect the dispersion of air pollutants, land erosion, and damage buildings, representing a severe hazard to people and properties. However, the changes and variabilities of extreme winds are still largely unknown, especially in global high-elevation regions, e.g., the Tibetan Plateau. This study analyses for the first-time changes in extreme wind speed over the Tibetan Plateau using homogenized near-surface daily maximum wind speed observations for 1973–2020. Results show that the daily maximum wind speed has significantly decreased in most stations during 1973–2020, with the largest decline in magnitude observed in spring. The frequency of daily maximum wind speed exceeding the 95% percentile shows a similar slowdown pattern. The detected decline is linked to large-scale atmospheric circulation, particularly to changes in the patterns of westerly and monsoon, which explain 35%~57% of daily maximum wind speed anomaly variations. Furthermore, this study reveals that changes in (a) geostrophic wind, (b) the instability of atmospheric thermal stratification, (c) vertical wind shear, and (d) Tibetan Plateau low vortex also contributes to the observed decreasing trends of daily maximum wind speed.

The radius of maximum wind (RMW) defines the location of the maximum winds in a tropical cyclone and is critical to understanding intensity change as well as hazard impacts. A comparison between the Hurricane Analysis and Forecast System (HAFS) models and two statistical models based off the National Hurricane Center official forecast is conducted relative to a new baseline climatology to better understand whether models have skill in forecasting the RMW of North Atlantic tropical cyclones. On average, the HAFS models are less skillful than the climatology and persistence baseline and two statistically derived RMW estimates. The performance of the HAFS models is dependent on intensity with better skill for stronger tropical cyclones compared to weaker tropical cyclones. To further improve guidance of tropical cyclone hazards, more work needs to be done to improve forecasts of tropical cyclone structure. Plain Language Summary The radius of maximum wind (RMW) is a key structural parameter of tropical cyclones that describes how far the strongest winds are from the storm's center. The RMW is closely tied to significant hazards such as wind, storm surge, and rainfall. However, little forecast guidance is provided for the RMW resulting in forecasters using climatological estimates to help communicate hazard risk. In order to better forecast the RMW, we need to understand the performance of the few guidance techniques available. We compare RMW forecasts from the Hurricane Analysis and Forecast System (HAFS) to two statistical models and a climatological estimate. Forecasts of the RMW from HAFS are not competitive with statistical derivations of the RMW with marginally better to comparable skill for stronger tropical cyclones. The results indicate that there is a strong need for future improvements to better predict tropical cyclone structure in addition to track and intensity. Key Points Forecasting the radius of maximum wind (RMW) is important for forecasting tropical cyclone hazards A RMW climatology and persistence model is created to determine forecast skill Statistical RMW forecasts are skillful and outperform dynamical model guidance

Accurate wind profiles and radius of maximum wind (Rmax${R}_{\\max }$ ) are critical for reconstructing realistic tropical cyclones (TCs) to predict extreme ocean conditions, including storm surges and waves. Leveraging the expanding Synthetic Aperture Radar (SAR) database, we conducted the first comprehensive evaluation of commonly used TC wind profile models, demonstrating that a theory‐based model captures a broad range of TCs, outperforming semi‐empirical and empirical models. We then propose inferring Rmax${R}_{\\max }$with the theory‐based model, with quadrant‐maximum wind radii as inputs to account for TC asymmetry. Evaluated against SAR‐derived Rmax${R}_{\\max }$ , our best estimates are obtained using the wind radius closest to Rmax${R}_{\\max }$(64‐kt), consistent with the wind profile model's performance. These estimates outperform best‐track and semi‐empirical methods across all ocean basins, and we show that accounting for TC asymmetry significantly improves Rmax${R}_{\\max }$estimates. Finally, we highlight the challenge of representing the substantial variability observed in TC wind profile shapes.

Understanding the diurnal variation of horizontal wind in the atmospheric boundary layer is important for weather and climate research and wind energy applications. Here we analyze the hourly data from 91 wind profiler radar sites in China and observe that the power spectral density of horizontal wind in lower troposphere approximately follows the −5/3 power law in the mesoscale range over the ocean and coastal areas. However, in inland areas, the slopes of the power spectra are significantly greater than −5/3. We characterize the temporal and spatial variations of maximum wind speed and low level jets and find that the thermal wind effect may partially contribute to the high percentage of low‐level jets observed in the southeastern coast of China and Hainan Island. While the ERA5 reanalysis reproduces wind spectrum well for time scales >1 day, its spectrum diverges significantly from that of profiler data at shorter time scales. Plain Language Summary Understanding how the horizontal wind changes throughout the day in the lower part of the atmosphere is important for studying weather and climate and using wind energy. In this study, we looked at data from 91 radar sites in China that measure wind every hour. We found that the pattern of how the wind changes follows a specific mathematical relationship, called a power law, where the wind decreases in a particular way with decreasing spatial and temporal scales. This pattern holds true mostly over the ocean and coastal areas, but in areas further inland, the wind behaves a bit differently. We also studied how the low‐level jet (LLJ), which are fast winds at low altitudes, change over time and space. We discovered that the difference in temperature across the region contributes to the occurrence of these LLJs in coastal areas in southeast China and Hainan Island. Finally, we compare our findings with the ERA5 reanalysis, which demonstrates excellent agreement in reproducing the wind spectrum for time scales greater than 1 day. However, the spectrum derived from the ERA5 reanalysis diverges significantly from the profiler data at shorter time scales. Key Points Wind spectrum density from wind profiler radars over China shows a less negative slope over inland sites than the −5/3 power law over ocean Temporal and spatial variation of maximum wind speed in the lower troposphere and low level jets are characterized Wind spectrum from ERA5 reanalysis is realistic (deficient) for time scales >1 day (<1 day) compared with profiler data

The Tibetan Plateau (TP) affects its surrounding regions through thermal and dynamic processes. Hydrological cycles in TP are experiencing dramatic changes under the warming climate. As one of the most important circulations, the Indian summer monsoon (ISM) affects precipitation changes and therefore has a major effect on TP water resources. In this paper, the widths of ISM are defined as extents perpendicular to the moisture trajectories. They were calculated using ground-based measurements and verified with reanalysis datasets. The directions of maximum summer precipitation decreases ( Dir min ) were selected using the cluster proxy of directions of daily maximum winds ( W d ) and the slope aspects derived from digital elevation models (DEMs) at various resolutions. The major findings were that (1) similar southwestern Dir min were found based on the W d and the slope aspects derived from DEMs with resolutions of 100 and 200 km; (2) Reanalysis data from China Meteorological Forcing Dataset verified that a high percentage (~ 90%) of cells showing precipitation decreases also had slope aspects (derived from 100- and 200-km resolution DEMs) in the range of 112.5°–247.5°; (3) The possible widths of ISM trajectories in TP were approximately 100–200 km.

ABSTRACT Guangxi, located along China's southern coast, is prone to typhoons and features complex terrain, making wind speed forecasting challenging. Accurate prediction of near‐surface maximum wind speed is crucial for improving wind energy utilization and supporting carbon neutrality goals. This study proposes a novel prediction model using the eXtreme Gradient Boosting (XGBoost) algorithm integrated with a Bayesian Optimization Algorithm (BOA) and based on the k‐nearest neighbor mutual information feature selection algorithm (KNN‐MIFSA). Data from 93 meteorological stations in Guangxi (2016–2021) with a 3‐h temporal resolution were used. The model incorporates dynamic and thermal factors, including high‐altitude and surface variables, to predict maximum wind speed. Two key improvements were made in the prediction modeling: (1) KNN‐MIFSA was employed to select highly correlated features and eliminate redundant variables, and (2) BOA was used to optimize XGBoost parameters, enhancing model generalizability. The improved model was tested for 6 prediction lead times (12–72 h) from 2020 to 2021. Results show that, after adjusting parameters and processing factors, the new model reduced the mean absolute error (MAE) by 18.9%–30.06% and the root mean square error (RMSE) by 40.18%–65.83% compared to the original XGBoost model. For maximum wind speeds above level 6, MAE and RMSE of the new model were reduced by up to 40.41% and 30.92%, respectively, across lead times (12–72 h). The model demonstrates consistent performance and significantly improved accuracy, offering a promising approach for wind speed prediction in regions with complex terrain.

The parametric typhoon model is a powerful typhoon prediction and reproduction tool with advantages in accuracy, and computational speed. To simulate typhoons’ horizontal features, the longitude and latitude of the typhoon center, central pressure, radius of maximum wind speed (Rmax), and background states (such as surface air pressure and wind speed) are required. When a typhoon approaches or is predicted to affect Korea, the Korea Meteorological Agency (KMA) notifies the above-mentioned parameters, except for the Rmax and background state. The contribution of background wind and pressure is not very significant; however, Rmax is essential for calculating typhoon winds. Therefore, the optimized Rmax for the typhoons over the past five years was estimated at each time step compared with the in situ wind observation record. After that, a fifth-order polynomial fitting was performed between the estimated Rmax and the radius of strong wind (RSW; >15 m/s) provided by the KMA. Finally, the Rmax was calculated from the RSW via the empirical equation, and the horizontal fields of typhoon Hinnamnor (2211) were reproduced using a parametric model. Furthermore, the ocean storm surge height was adequately simulated in the surge model.

The Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model is the operational storm surge model of the National Hurricane Center (NHC). Previous studies have found that the SLOSH model estimates storm surges with an accuracy of ±20%. In this study, through hindcasts of historical storms, we assess the accuracy of the SLOSH model for four coastal regions in the Northeastern United States. We investigate the potential to improve this accuracy through modification of the wind field representation. We modify the surface background wind field, the parametric wind profile, and the maximum wind speed based on empirical, physical, and observational data. We find that on average the SLOSH model underestimates maximum storm surge heights by 22%. The modifications to the surface background wind field and the parametric wind profile have minor impacts; however, the effect of the modification to maximum wind speed is significant—it increases the variance in the SLOSH model estimates of maximum storm surges, but improves its accuracy overall. We recommend that observed values of maximum wind speed be used in SLOSH model simulations when possible.

Wind observations at multiple levels (40–200 m) have been conducted over a five-year time period (2016–2020) on Jeju Island of South Korea. This study aims to understand the vertical and temporal characteristics of the lower atmosphere. Jeju Island is a region located at mid-latitude and is affected by seasonal wind. The maximum wind speed occurs in the relatively lower altitudes during daytime and is delayed in the relatively higher altitude after sunset in a diurnal cycle. In the summer season, the altitudes appear earlier than in other seasons via the dominant solar radiation effect during daytime, and the altitude after sunset increases up to 160 m. However, the maximum wind speed in the winter season occurs irregularly among altitudes, and it is lower than that in the summer season. This can be attributed to the increase in the mean wind speed in the diurnal cycle caused by the strong northwestern wind in the winter season. These results imply that the relationship between near-surface and higher altitudes is primarily affected by solar radiation and seasonal winds. These results are expected to contribute to site selection criteria for wind farms.

This study examines the complex behaviour of tropical cyclones, specifically focusing on accurately estimating the Radius of Maximum Wind (RMW) and its implications for climate and oceanic processes. We use RMW data provided by the India Meteorological Department (IMD) to evaluate three existing methods for estimating RMW and introduce a new method based on machine learning techniques. The findings underscore the critical role of the RMW in shaping the intensity and impact of tropical cyclones, emphasizing the need for refined estimation techniques to bolster forecasting and preparedness measures. We use four cases to test our method. The results of our method show the root square mean error is 10.63 nautical miles and the error percentage of 17.00 % , which are lower than other methods. Therefore, the proposed ensemble machine learning model, designed to estimate RMW, showcases considerable promise in attaining precise RMW estimation.