# Research on Wind Field Characteristics Measured by Lidar in a U-Shaped Valley at a Bridge Site
## 1 Introduction  
Many long-span bridges have been built in mountainous valley areas, for example, Royal Gorge Bridge, (1929, 384 m), Canon City, Colorado, USA; Aizhai Bridge (2012, 1176 m), Hunan Province, China; Longjiang Bridge (2016, 1196 m), Yunnan Province, China; and Yachihre Bridge (2016, 800 m), Guizhou Province, China; and more will be built in the future. The requirement for long-span bridges in mountainous valley areas also enhances the performance standard of long-span bridges. Among them, the influence of wind on the design, construction, and operation of long-span bridges cannot be ignored, and is even one of the control factors. At the same time, the distribution of wind parameters in mountainous valley terrain is different from that in open and flat terrain, and is extremely complex, as shown in Figure 1. From the perspective of wind speed, the wind speed profile in flat and open areas conforms to the law of power index or logarithm. Although much research has been conducted in this field, the spatio-temporal distribution of wind speed in mountainous valley areas has its own unique patterns, which are generally analyzed on a case-by-case basis and are difficult to be described by a unified mathematical model. In addition, due to the impact of topographic relief and elevation changes, the gradient wind height in mountain valleys may be higher than the former. At present, the study of wind parameters in mountainous valley areas is one of the hot topics in wind engineering research. The research on mountainous valley wind parameters by scholars mainly applies the methods of wind tunnel terrain model test, numerical simulation, and field measurement. Wind tunnel terrain model test is widely used, but there is room for improvement in accuracy because of characteristic flow simulation and scaling model making [1; 2; 3]. With the development of computer technology and computational fluid dynamic (CFD), numerical simulation methods have been applied more and more widely, and their reliability has also been verified [4; 5; 6]. However, further research is needed in calculating domain size and setting boundary conditions, such as surface roughness. In spite of the time consuming, huge investment and limited measured positions, field measurement can obtain the most direct and reliable wind field data, and it is often used as the verification standard for the analysis results of the first two methods [7; 8; 9; 10; 11; 12; 13]. From the perspective of measuring equipment performance, field measurement can be divided into direct measurement and indirect measurement. Direct measurements, for example, tall mast balloons with anemometers have been important in defining the profiles of atmospheric parameters of the atmosphere boundary layer [11; 12; 13; 14; 15; 16; 17]. The ability to observe atmospheric variables with a minimum of assumptions has been widely used, despite the development costs and associated limitations on sample size. However, limited by the performance of direct measurement instruments, only limited monitoring positions can be set up, and the location of instruments is restricted by terrain and other conditions; therefore, direct instruments are generally installed on the bridge deck of pylons. This method is difficult to use to obtain wide-range wind parameters at bridge sites, such as the wind profile at the bridge midspan located at deep mountainous valley. Especially in the design state, when the bridge has not been built yet, it is impossible to conduct large-scale direct observation in a mountainous valley. With the development of testing technology, especially the application of remote monitoring technology, indirect observation is mostly made by remote sensors, such as radar, sodar, and lidar, which greatly reduce the cost of field measurement and allow a large range of massive data to be obtained.