# Research on Wind Field Characteristics Measured by Lidar in a U-Shaped Valley at a Bridge Site
## 4 Results and Discussion  
### Wind Speed  
#### 4.1.1 Wind Speed Distribution  
Wind speed spatio-temporal distribution and time history at a certain height are shown in Figure 7. It can be found that the wind speed varies from 0 m/s to 25 m/s. Although the spatio-temporal distribution of the wind speed at the same location of the virtual wind tower (VWT) is obviously nonuniform, its trend among different virtual towers is consistent to a certain extent, which indicates that wind speed distribution along the spanning direction of the bridge has a certain correlation.  
Time history of the wind speed is shown in Figure 8, with the height of the bridge deck taken as the starting point, from 0 m to 750 m and the interval is 150 m; thus, 6 specific heights are selected. The starting point on the time axis is selected as 0:0:0 at midnight every day, and the day is divided into 240 time periods with an interval of 6 min. Taking the third category as an example, the wind speed at different heights fluctuates significantly, and the degree of fluctuation does not decrease significantly with the increase in height, indicating that the influence of mountain valley terrain on the wind speed is more obvious than that of surface roughness.  
After preliminary research, it was found that during the measurement period, the datawith the wind speed effective ratio is not less than 0.6, occupied only 8%. On the one hand, when wind speed is low, the noise proportion of the original data is relatively high, and the reliability of the data is relatively low. On the other hand, the influence of small value wind on wind-resistance performance of the structure is negligible. Then, combined with the data characteristics, 57% of the data is excluded with a minimum wind speed not less than 7 m/s as the standard. Next, the effective wind speed at different virtual tower locations was counted, and it was found that the effective data at the midspan location accounted for 34%, much higher than other measuring positions. Due to the influence of valley topography, the effective data at the west bridge tower accounted for the lowest  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Weather** & **Wind Direction** & **Effective Ratio \(\beta\) Range** \\ \hline
19 December 2020 & Cloudy & South Wind Grade 2 & 0.382–0.919 \\
20 January 2021 & Light Rain & Southwest Wind Grade 2 & 0.134–0.726 \\
5 March 2021 & Light Rain & West Wind Grade 1 & 0.263–0.794 \\
25 March 2021 & Clear Day & Southwest Wind Grade 2 & 0.772–0.992 \\
10 April 2021 & Fine Day & Southwest Wind Grade 2 & 0.692–0.980 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Historical weather list.  
Figure 6: Data effective ratio.  
proportion, only 11%. Furthermore, the effective data at VWT-3 was taken as the research object and analyzed, as showed in Figure 9.  
Figure 7: Spatio-temporal distribution of wind speed, **(a)** VWT-1; **(b)** VWT-2; **(c)** VWT-3; **(d)** VWT-4; **(e)** VWT-5.  
In wind-resistance research of long-span bridges, the wind speed at the height of main girder deserves special attention [30] and is set as the reference wind speed, which is used to process the dimensionless wind speed.