# Influence of Atmospheric Flow Structure on Optical Turbulence Characteristics
## 4 Discussion  
In the present article, we studied the relationship between variations in the strength of small-scale (optical) turbulence along the line of sight of a telescope and meso-scale atmospheric disturbances. The Fried parameter was chosen as a measure characterizing the strength of small-scale (optical) turbulence. As the Fried parameter increased, the strength of turbulence and its effect on the wavefront decreased. In order to identify meso-scale atmospheric disturbances, we used the WRF model with a resolution of 500 m in the third (internal) domain. This spatial resolution was chosen due to the fact that atmospheric fluctuations with a linear size of 500 m are smaller than a spatial scale corresponding to the micrometeorological maximum in the energy spectrum of atmospheric turbulence. The WRF model was based on the use of the turbulent diffusion coefficients (for wind speed \(K_{V}\) and air temperature \(K_{T}\)). We should note that the amplitudes and height profiles of diffusion coefficients can differ significantly under conditions of stable atmospheric stratification. These differences are associated with the features of the formation and suppression of turbulent fluctuations in wind speed and air temperature, as well as with the variations in the ratio between turbulent kinetic energy and turbulent potential energy. In general, the structure of small-scale turbulence in a stably stratified atmospheric boundary layer at night significantly differs from daytime turbulence. Measurement data show that daytime turbulence is more intense than nighttime turbulence. The energy spectrum of turbulent fluctuations is significantly deformed in stably stratified atmospheric layers. Figure 11 shows the dimensionless energy spectra of angle-of-arrival fluctuations caused by turbulence at a height of 2 m above surface. The low-frequency fluctuations in these spectra were suppressed. \begin{table}
\begin{tabular}{c c c c} \hline
**Time** & \(V_{\text{m}}\)**, m/s** & \(E_{n}\) & \(\int_{f_{1}}^{f_{2}}E(f)df/Int\) \\ \hline
8 August & & & \\
06 h 00 min & 2.7 & 0.38 & 0.51 \\
13 h 12 min & 3.3 & 1.00 & 1.00 \\
14 h 09 min & 2.9 & 0.93 & 0.85 \\
14 h 51 min & 2.6 & 0.91 & 0.78 \\
15 h 54 min & 2.3 & 0.77 & 0.65 \\
17 h 09 min & 2.1 & 0.85 & 0.76 \\
9 August & & & \\
00 h 00 min & 1.7 & 0.34 & 0.29 \\ \hline \end{tabular}
\end{table} 
Table 2: Statistics of the wind field. The WRF model provides a few schemes to describe the Earth's surface. In this study, we used the RUC scheme. This scheme solves equations of energy balance and moisture on the surface by considering six layers of soil (and phase changes of water in the soil during cold periods) [34]. [PERSON] and [PERSON] [35] showed that a high spatial resolution provides more accurate estimates of turbulent flows of latent and sensible heat from the underlying surface. We believe that a sufficiently high horizontal resolution would lead to accurate simulations of surface net shortwave radiation and reliable estimates of air temperature in the lower layers of the atmosphere. Using the WRF model, the spatial distributions of wind speeds over the observatory, Lake Baikal and the surrounding region were obtained. Analysis of these distributions showed that a meso-scale vortex structure was formed above the observatory. The energy spectrum of motions within this vortex structure is described by the dependence of the power spectral density on frequency to a power close to minus 3. Such a spectrum is associated with the suppression of low-frequency turbulence. In order to identify features in the vertical structure of wind speed, we obtain vertical wind speed profiles using the WRF model. Figure 12a,b shows vertical profiles of wind speeds over the observatory and Lake Baikal, respectively. The vertical wind speed profiles have a classical shape.