Turbulence kinetic energy dissipation rate: assessment of radar models from comparisons between 1.3 GHz wind profiler radar (WPR) and DataHawk UAV measurements

The WPR-LQ-7 is a UHF (1.3575 GHz) wind profiler radar used for routine measurements of the lower troposphere at Shigaraki MU Observatory (34.85 degrees N, 136.10 degrees E; Japan) at a vertical resolution of 100m and a time resolution of 10 min. Following studies carried out with the 46.5MHz middle and upper atmosphere (MU) radar (Luce et al., 2018), we tested models used to estimate the rate of turbulence kinetic energy (TKE) dissipation "from the Doppler spectral width in the altitude range similar to 0.7 to 4.0 km above sea level (a.s.l.). For this purpose, we compared LQ-7-derived epsilon using processed data available online (http://www.rish.kyoto-u.ac.jp/radar- group/blr/shigaraki/data/, last access: 24 July 2023) with direct estimates of epsilon (epsilon(U)) from DataHawk UAVs. The statistical results reveal the same trends as reported by Luce et al. (2018) with theMUradar, namely (1) the simple formulation based on dimensional analysis epsilon L-out = sigma(3)/L-out, with L-out similar to 70 m, provides the best statistical agreement with epsilon(U). (2) The model epsilon(N) predicting a sigma(2) N law ( N is Brunt-Vaisala frequency) for stably stratified conditions tends to overestimate for epsilon(U) less than or similar to 5 x 10(-4) m(2) s(-3) and to underestimate for epsilon(U) greater than or similar to 5 x 10(-4) m(2) s(-3). We also tested a model epsilon(S) predicting a sigma(2) S law (S is the vertical shear of horizontal wind) supposed to be valid for low Richardson numbers (Ri = N-2/S-2). From the case study of a turbulent layer produced by a Kelvin-Helmholtz (K-H) instability, we found that epsilon(S) and epsilon L-out are both very consistent with epsilon(U), while epsilon(N) underestimates epsilon(U) in the core of the turbulent layer where N is minimum. We also applied the Thorpe method from data collected from a nearly simultaneous radiosonde and tested an alternative interpretation of the Thorpe length in terms of the Corrsin length scale defined for weakly stratified turbulence. A statistical analysis showed that epsilon(S) also provides better statistical agreement with epsilon(U) and is much less biased than epsilon(N). Combining estimates of N and shear from DataHawk and radar data, respectively, a rough estimate of the Richardson number at a vertical resolution of 100m (Ri(100)) was obtained. We performed a statistical analysis on the Ri dependence of the models. The main outcome is that epsilon(S) compares well with epsilon(U) for low Ri(100) (Ri(100) less than or similar to 1), while epsilon(N) fails. epsilon(Lout) varies as epsilon(S) with Ri(100), so that epsilon(Lout) remains the best (and simplest) model in the absence of information on Ri. Also, sigma appears to vary as Ri(100)(-1/2) when Ri(100) greater than or similar to 0.4 and shows a degree of dependence on S-100 (vertical shear at a vertical resolution of 100 m) otherwise.