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The accurate prediction of small-scale three-dimensional wind fields is of great practical significance for aviation safety, wind power generation, and related fields. This study proposes a novel method for predicting small-scale three-dimensional wind fields by combining the mesoscale Weather Research and Forecasting (WRF) model with Computational Fluid Dynamics (CFD). The method consists of three components: the WRF module, the hybrid neural network prediction module, and the CFD module. First, mesoscale meteorological fields are simulated using the WRF module to establish a historical inflow boundary dataset for the CFD domain. Next, deep separable convolutions are incorporated, and convolutional long short-term memory (ConvLSTM) is combined with a deep separable convolution-gated recurrent unit (DSConvGRU) to construct a hybrid neural network prediction module named ConvLSTM-DSConvGRU. This module is employed for predicting inflow boundary data. Finally, the predicted inflow boundary conditions drive the CFD module to predict small-scale three-dimensional wind fields. The effectiveness of the WRF and CFD downscaling coupling method was validated using observed data from meteorological stations within the simulated domain, along with statistical indicators of errors. Additionally, a comparative evaluation was conducted between the proposed hybrid network model and the four commonly used spatiotemporal prediction models to assess its prediction performance. The results demonstrate that our proposed wind field prediction method achieves accurate simulation and short-term prediction of small-scale three-dimensional wind fields, and the hybrid network model exhibits comprehensive advantages in terms of model complexity and prediction accuracy. [ABSTRACT FROM AUTHOR]

The meteorological causes of wind deflection and tower collapse of a 500 kV transmission lines are analyzed. The wind speed distribution characteristics in the fault area of the line are obtained by using the observed wind speed of an automatic weather station and the three-dimensional wind field based on radar inversion. The results show that the maximum wind speed and the extreme wind speed observed by automatic weather stations around the two accidents are small. The three-dimensional wind field of radar inversion better indicates the fault's location and time. Compared with strong convective weather which can cause the wind deflection， the strong convective weather which can cause the tower collapse has a higher echo height and stronger updraft. The core height of its reflectivity factor drops more than 3.5 km within 6 minutes， causing greater ground damage， but its locality is strong and the influence range is small. The results show that the current design wind speed of the transmission line is safe under the strong convective weather that causes wind deflection， but adverse terrains that can accelerate the wind speed must be avoided. It is suggested to take “cascaded collapse prevention” as the main idea for the microburst which can cause the tower collapse. In addition， under the influence of strong convective weather， the gust coefficient calculated by the observed wind speed is significantly larger than the value in the relevant specifications.

In 2019–2020, an array weather radar (AWR) network consisting of seven X-band phased-array radars (PARs), with a detection spatial resolution of 30 m and a temporal resolution of 30 s, was built in the city of Foshan in China's Guangdong Province. The detection time deviation in the same space is within 5 s. Through variational data assimilation, the three-dimensional wind field inside the storm can be obtained. This study selected instances of hail, thunderstorms, strong winds, and short-duration heavy precipitation in 2020 to conduct a detailed analysis. The results show the following: (1) The fine detection ability enables phased-array radars to detect the complete evolution process of convective storms, including development, strengthening, and weakening, providing a useful reference for judging the future variation trends of convective storms. (2) Through evolutionary analysis of the three-dimensional wind field, the dynamic mechanisms of storm strengthening and weakening could be obtained, which could serve as a reference to predict the development of storms. The gust wind index and convection index calculated based on the three-dimensional wind field could provide advanced warning for nowcasting. When the gust wind index was greater than 263, the probability of gale-force wind (above 17.0 m/s) was determined to be high. Moreover, the warning could be provided 10–20 min in advance. A convection index greater than 35 and the presence of concentrated contour lines were found to be conducive to the strengthening and formation of a convection, and the warning could be provided 20 min in advance. These results show that the application of PAR can provide important technical support for nowcasting severe convective weather. [ABSTRACT FROM AUTHOR]

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The accurate prediction of small-scale three-dimensional wind fields is of great practical significance for aviation safety, wind power generation, and related fields. This study proposes a novel method for predicting small-scale three-dimensional wind fields by combining the mesoscale Weather Research and Forecasting (WRF) model with Computational Fluid Dynamics (CFD). The method consists of three components: the WRF module, the hybrid neural network prediction module, and the CFD module. First, mesoscale meteorological fields are simulated using the WRF module to establish a historical inflow boundary dataset for the CFD domain. Next, deep separable convolutions are incorporated, and convolutional long short-term memory (ConvLSTM) is combined with a deep separable convolution-gated recurrent unit (DSConvGRU) to construct a hybrid neural network prediction module named ConvLSTM-DSConvGRU. This module is employed for predicting inflow boundary data. Finally, the predicted inflow boundary conditions drive the CFD module to predict small-scale three-dimensional wind fields. The effectiveness of the WRF and CFD downscaling coupling method was validated using observed data from meteorological stations within the simulated domain, along with statistical indicators of errors. Additionally, a comparative evaluation was conducted between the proposed hybrid network model and the four commonly used spatiotemporal prediction models to assess its prediction performance. The results demonstrate that our proposed wind field prediction method achieves accurate simulation and short-term prediction of small-scale three-dimensional wind fields, and the hybrid network model exhibits comprehensive advantages in terms of model complexity and prediction accuracy.

To achieve a comprehensive understanding of a three-dimensional (3D) wind field and the speed-up phenomenon in a downburst wind flow over a 3D hilly terrain, a succession of laboratory tests utilizing 12 hill models with cosine-squared cross-section was conducted using a physical downburst simulator with a jet diameter of 0.6 m. By placing the models in the strong horizontal wind region and the strong vertical wind region, the corresponding wind profiles for both the horizontal and vertical velocities were measured. It was found that the wind flowed predominantly over the crest of the hill in the case of low hills, whereas wind flow around the hill body became increasingly pronounced as the hill height increased. In addition, the speed-up region, where the horizontal wind velocity exceeds the impinging jet velocity, was identified, and found to move from the crest to the two sides of the hill as the hill height increased. Accordingly, the most significant topographic multipliers of all locations on the hill might appear at the crest, the hill foot, or elsewhere, depending largely on the hill height. Among all cases, the maximum topographic multiplier was 1.12, and occurred at the ridge, while the ratio of hill height to jet height was 5/12. Additionally, empirical equations are presented to facilitate the determination of wind loads induced by a downburst flow over an isolated hill. [ABSTRACT FROM AUTHOR]

Based on S-band weather radar and X-band phased-array radar data, we retrieved and studied the three-dimensional wind field of a convective storm during the heavy precipitation on June 6, 2020 in Foshan, Guangdong Province. The results are summarized as follows: (1) The S-band radar data was unable to retrieve the characteristics of the low-level horizontal wind field inside the convective storm because its 0.5 ° volume coverage was partly blocked and its spatial resolution was lower compared with that of the X-band phased-array radar. The retrieved wind field was relatively smooth due to large elevation interval and a lack of simultaneous scan. (2) With more elevation and more simultaneous scan, the X-band phased-array radar data produced high spatial resolution wind field that described the fine structure of the convective storm. Three-dimensional variation wind field retrieved by the X-band phasedarray radar displayed strong southerlies cyclonic flow into the center from the low level and south part of the convective storm. Meanwhile, a convergence line located at the middle level of storm. (3) Updrafts and downdrafts co-existed at the mature stage of the convective storm. Strong updrafts stretched to the top of the storm, and then diverged to the south part and north part of the storm. Downdrafts dominated the north part of the convective storm, while at the south part they reached the precipitation center and rear-flank. A small vortex emerged from the rear-flank downdrafts and southerly inflows between the old and new cells. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of dynamic detection of wind field in the three-dimensional display space, system software is carried out on the actual scene and corresponding airborne radar observation information data, and the particle swarm algorithm fuzzy logic algorithm is introduced into the wind field dynamic simulation process in three-dimensional display space, to analyze the error of the filtering result in detail, to process the hurricane Lily Doppler radar measurement data with the optimal adaptive filtering according to the error data. The three-dimensional wind field synchronous measurement data obtained by filtering was compared with three-dimensional wind field synchronous measurement data of the GPS dropsonde in this experiment, the sea surface wind field measurement data of the multi-band microwave radiometer, and the wind field data at aircraft altitude. [ABSTRACT FROM AUTHOR]

To achieve a comprehensive understanding of a three-dimensional (3D) wind field and the speed-up phenomenon in a downburst wind flow over a 3D hilly terrain, a succession of laboratory tests utilizing 12 hill models with cosine-squared cross-section was conducted using a physical downburst simulator with a jet diameter of 0.6 m. By placing the models in the strong horizontal wind region and the strong vertical wind region, the corresponding wind profiles for both the horizontal and vertical velocities were measured. It was found that the wind flowed predominantly over the crest of the hill in the case of low hills, whereas wind flow around the hill body became increasingly pronounced as the hill height increased. In addition, the speed-up region, where the horizontal wind velocity exceeds the impinging jet velocity, was identified, and found to move from the crest to the two sides of the hill as the hill height increased. Accordingly, the most significant topographic multipliers of all locations on the hill might appear at the crest, the hill foot, or elsewhere, depending largely on the hill height. Among all cases, the maximum topographic multiplier was 1.12, and occurred at the ridge, while the ratio of hill height to jet height was 5/12. Additionally, empirical equations are presented to facilitate the determination of wind loads induced by a downburst flow over an isolated hill.

Falling-sphere sounding remains an important method for in situ determination in the middle atmosphere and is the only determination method within the altitude range of 60–100 km. Traditional single-falling-sphere sounding indicates only the atmospheric density and horizontal wind but not the vertical wind; the fundamental reason is that the equation set for retrieving atmospheric parameters is underdetermined. For tractability, previous studies assumed the vertical wind, which is much smaller than the horizontal wind, to be small or zero. Obtaining vertical wind profiles necessitates making the equations positive definite or overdetermined. An overdetermined equation set consisting of six equations, by which the optimal solution of density and three-dimensional wind can be obtained, can be established by the double-falling-sphere method. Hence, a simulation experiment is designed to retrieve the atmospheric density and three-dimensional wind field by double falling spheres. In the inversion results of the simulation experiment, the retrieved density is consistent with the constructed atmospheric density in magnitude; the density deviation rate does not generally exceed 20% (less than 5% below 60 km). The atmospheric density retrieved by the double-falling-sphere method is more accurate at low altitudes than the single-falling-sphere method. The vertical wind below 50 km and horizontal wind retrieved by double-falling-sphere method is highly consistent with the constructed average wind field. Additionally, the wind field deviation formula is deduced. These results establish the fact that the double-falling-sphere method is effective in detecting atmospheric density and three-dimensional wind.

Falling-sphere sounding remains an important method for in situ determination in the middle atmosphere and is the only determination method within the altitude range of 60–100 km. Traditional single-falling-sphere sounding indicates only the atmospheric density and horizontal wind but not the vertical wind; the fundamental reason is that the equation set for retrieving atmospheric parameters is underdetermined. For tractability, previous studies assumed the vertical wind, which is much smaller than the horizontal wind, to be small or zero. Obtaining vertical wind profiles necessitates making the equations positive definite or overdetermined. An overdetermined equation set consisting of six equations, by which the optimal solution of density and three-dimensional wind can be obtained, can be established by the double-falling-sphere method. Hence, a simulation experiment is designed to retrieve the atmospheric density and three-dimensional wind field by double falling spheres. In the inversion results of the simulation experiment, the retrieved density is consistent with the constructed atmospheric density in magnitude; the density deviation rate does not generally exceed 20% (less than 5% below 60 km). The atmospheric density retrieved by the double-falling-sphere method is more accurate at low altitudes than the single-falling-sphere method. The vertical wind below 50 km and horizontal wind retrieved by double-falling-sphere method is highly consistent with the constructed average wind field. Additionally, the wind field deviation formula is deduced. These results establish the fact that the double-falling-sphere method is effective in detecting atmospheric density and three-dimensional wind. [ABSTRACT FROM AUTHOR]