Accurate predictions of TKE dissipation rate are important for many engineering and environmental applications, such as wildfire propagation, transport of pollution or other particulates suspended in the atmosphere, design of high-rise buildings, aviation meteorology, and wind energy for which ε is a crucial parameter to predict hub-height wind speed.
More recently, remote sensing, and specifically light detection and ranging (LiDAR), has become a compelling measurement technique to investigate TKE dissipation rate for atmospheric flows [8] by leveraging its capability of probing simultaneously multiple locations throughout the ABL, and the relatively high-frequency resolution to probe adequately the inertial subrange, or at least partially its lower-frequency range [9].
TKE dissipation rate can be estimated from measurements of the streamwise velocity collected through different techniques. Direct measurements of ε require high spatio-temporal resolution, such as using hot-wire anemometry and invoking Taylor’s hypothesis for frozen turbulence, or particle image velocimetry. In case these highly resolved measurements are not available, the TKE dissipation rate can be indirectly estimated by leveraging the second- or third-order structure functions of the streamwise velocity, or its power spectral density [10].
In this study, the TKE dissipation rate is estimated using streamwise velocity measurements collected with a scanning Doppler LiDAR deployed at the Galveston Island State Park, Texas, USA. The LiDAR is deployed at a distance of about 100 m from the shoreline and the measurements are generally collected over the water surface, thus they are representative of the marine atmospheric surface layer in coastal region. The LiDAR is set up to perform feedback fixed-point measurements with a sampling frequency of 2 Hz, range gate of 18 m, and elevation angle of 3.5°, which leads to a vertical resolution of about 1 m. Vertical profiles of wind speed and direction are periodically measured with the Doppler beam swinging (DBS) technique. Specifically, the wind direction estimated at the 10-m height is leveraged to set up the azimuth angle of the following fixed-point data collection, which is performed for durations ranging from 25 to 75 minutes. This scanning strategy allows for accurate measurements of the streamwise wind velocity component, which are needed for the calculation of the power spectra and structure functions used to estimate ε. The TKE dissipation rate is calculated using methods involving the second-order structure function and the power spectral density of the streamwise velocity measured from the scanning Doppler LiDAR. Identifying the inertial subrange from the LiDAR turbulence measurements is a critical aspect of this process, as it ensures the accuracy of ϵ estimations. The inertial subrange is the range of scales in which turbulence is fully developed and the energy cascade follows the Kolmogorov scaling laws, making it crucial for reliable dissipation rate calculations.
The results of this study indicate that ϵ estimated from both methods have a good level of agreement. Moreover, colder offshore winds, typically associated with unstable atmospheric conditions, lead to higher ϵ values. In contrast, milder onshore winds, which are associated with more stable atmospheric conditions, exhibit lower ϵ values. These findings underscore the significant impact of atmospheric stability on turbulence characteristics. Furthermore, the study identifies clear trends in ϵ related to wave age and reference wind velocity, providing valuable insights into the interaction between turbulence and wave dynamics.
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