Objective In the atmospheric boundary layer, studying the formation and development of turbulence is of significant interest to numerical weather prediction, atmospheric dynamics, mechanical structure safety of wind power equipment, and aviation safety. The refractive index structure constant Cn2 and turbulent energy dissipation rate ε are commonly used parameters for studying the intensity of atmospheric turbulence. Traditional observation methods rely on meteorological elements, such as temperature, pressure, humidity, and wind field obtained from radiosondes. Micro-temperature sensors, ultrasonic anemometers, and wind profile radars, combine with corresponding methods to obtain atmospheric turbulence parameters. A new observation method based on lidar observation of atmospheric turbulence parameters has been widely studied. Compared with traditional observation methods, lidar can detect vertical profiles round the clock and high temporal-spatial resolution. In this study, we present the estimation of Cn2 and ε based on the coherent Doppler wind lidar observation data with second-level time resolution, combined with the temperature and pressure diurnal variation model. Besides, the vertical change profile of Cn2 round the clock is presented and compared with the vertical velocity variance σa2. We believe that the proposed methods and estimation results can be useful for studying the characteristics of turbulence parameters in the boundary layer, the energy exchange of the land-atmosphere system, and transportation and diffusion of pollutants in coastal urban areas.
Methods First, the wind speed data observed by lidar with a signal-to-noise ratio of less than 8 dB and observation height below 60 m are eliminated. The meridional wind υ and zonal wind ν are calculated using the wind speed data after quality control. Then, the US standard atmosphere model is used to simulate changes in temperature and pressure at different altitudes. The GOT01_0 temperature diurnal change model is used to simulate the diurnal temperature change. The potential temperature is calculated based on the empirical relationship between temperature, pressure, and potential temperature. Finally, the refractive index structure constant Cn2 is estimated using the method proposed by Tatarski. By estimating the velocity structure function, the turbulent energy dissipation rate is estimated using the outer scale L0 and the vertical velocity standard deviation σa. The turbulent energy dissipation rate ε and the vertical velocity variance σa2 representing the vertical component of the turbulent kinetic energy were compared with Cn2 to characterize that it is feasible to estimate Cn2 based on the temperature and pressure model. Besides, the influence of each variable required to calculate Cn2 on the calculation result is investigated based on the theory of error analysis to guide for further in-depth research.
Results and Discussions The magnitude of Cn2 is between 10 -16 and 10 -13m -2/3. It is mainly concentrated on 10 -16m -2/3, and the refractive index structure in a dry and clean atmosphere in the order of 10 -18 to 10 -13m -2/3 is consistent. As the height increases, Cn2 gradually decreases, indicating that the turbulence intensity decreases as the height increases. The strong turbulence is mainly concentrated below 1 km, and the turbulence intensity is weaker at 1 to 1.5 km, but this trend is not absolute; however, the strong turbulence can reach 1.5 to 2 km (Fig.3). Except for October 18, the other observation days showed a sudden increase in Cn2 between October 17 and 21, and the height rose from 500 to 900 m. It may be due to strong turbulent uplift caused by changes in ground thermal radiation after temperature attenuation and may also be related to the conversion of sea and land wind. The daily variation of Cn2 near the ground presents an obvious “Mexican hat” shape, i.e., during the day, Cn2 is higher than that at night, which is also reflected in 360 m. As the height increases, the hat-shaped diurnal variation gradually disappears, and the diurnal variation of Cn2 at 720 m presents random fluctuations (Fig.5). σa2 and Cn2 have a strong correlation, and the correlation coefficient is above 0.7, indicating the consistency of turbulence in the vertical and horizontal directions. Besides, the correlation decreases when the surface temperature at night decreases. There is a certain correlation between ε and Cn2, when the turbulence is fully mixed and developed during the day; the correlation coefficient is above 0.5. The dynamic range of Cn2 estimated by simulated temperature and pressure is smaller than that estimated by GMAO meteorological data of CALIPSO data (Fig.8), which is mainly caused by the fact that the standard atmospheric profile cannot reflect the randomness of instantaneous temperature change.
Conclusions In this study, the wind profile observation data of the coherent Doppler wind lidar in the Yangmeikeng area of Shenzhen are used, combined with the standard atmosphere model, the diurnal temperature model, and the data from the ground weather station. The atmospheric refractive index structure constant Cn2 and turbulent energy dissipation rate ε in the atmospheric boundary layer under clear weather are estimated. The characteristics of temporal and spatial changes are analyzed according to its time-height vertical profile. Besides, we investigated the specific effects of changes in various parameters on Cn2. The high correlation between Cn2 and σa2 estimated based on the horizontal wind showed that the turbulent motion is isotropic. The estimation based on temperature and pressure models is feasible in the absence of measured temperature and pressure profiles. Compared with the calculation results of GMAO standard meteorological data, the estimated Cn2 has a smaller dynamic range, which is due to the absence of instantaneous changes in the simulated temperature profile. To further accurately investigate the changes in turbulence parameters in the Shenzhen area, long-term joint observations with various equipment must guide the study of atmospheric pollutant transport and diffusion and weather changes in the Shenzhen area.
