Vertical structure of the lower troposphere derived from MU radar, unmanned aerial vehicle, and balloon measurements during ShUREX 2015

Hashiguchi

et al. 2018

Earth Planets Space

Self Cite

We tested models commonly used for estimating turbulence kinetic energy dissipation rates ε from very high frequency stratosphere-troposphere radar data. These models relate the root-mean-square value σ of radial velocity fluctuations assessed from radar Doppler spectra to ε. For this purpose, we used data collected from the middle and upper atmosphere (MU) radar during the Shigaraki unmanned aerial vehicle (UAV)-radar experiment campaigns carried out at the Shigaraki MU Observatory, Japan, in June 2016 and 2017. On these occasions, UAVs equipped with fastresponse and low-noise Pitot tube sensors for turbulence measurements were operated in the immediate vicinity of the MU radar. Radar-derived dissipation rates ε estimated from the various models at a range resolution of 150 m from the altitude of 1.345 km up to the altitude of ~ 4.0 km, a (half width half power) beam aperture of 1.32° and a time resolution of 24.6 s, were compared to dissipation rates (ε U) directly obtained from relative wind speed spectra inferred from UAV measurements. Firstly, statistical analysis results revealed a very close relationship between enhancements of σ and ε U for ε U 10 −5 m 2 s −3 , indicating that both instruments detected the same turbulent events with ε U above this threshold. Secondly, ε U was found to be statistically proportional to σ 3 , whereas a σ 2 dependence is expected when the size of the largest turbulent eddies is smaller than the longitudinal and transverse dimensions of the radar sampling volume. The σ 3 dependence was found even after excluding convectively generated turbulence in the planetary boundary layer and below clouds. The best agreement between ε U and radar-derived ε was obtained with the simple formulation based on dimensional analysis ε = σ 3 /L c where L C ≈ 50-70 m. This empirical expression constitutes a simple way to estimate dissipation rates in the lower troposphere from MU radar data whatever the sources of turbulence be, in clear air or cloudy conditions, consistent with UAV estimates.

Section: Practical Methods From Radar Datamentioning

confidence: 99%

Turbulence kinetic energy dissipation rates estimated from concurrent UAV and MU radar measurements

Luce

Hashiguchi

et al. 2018

Earth Planets Space

Self Cite

“…Therefore, all three instruments detected the same prominent temperature and humidity gradients, down to decameter scales in stratified conditions. These gradients extended horizontally over a few kilometers at least and persisted for hours without significant changes [see Luce et al (2018) Therefore, Re b is, in strict terms, not the turbulence Reynolds number but more appropriately is a measure of the broadness of the ISR. For turbulence to be well established, R must have a value of at least 2, and therefore Re b must be above 35.…”

Section: Shurex 2016 Observationsmentioning

confidence: 99%

Mixing Coefficient in Stably Stratified Flows

Journal of Physical Oceanography

Luce²

2018

Self Cite

Turbulent mixing in the interior of the oceans is not as well understood as mixing in the oceanic boundary layers. Mixing in the generally stably stratified interior is primarily, although not exclusively, due to intermittent shear instabilities. Part of the energy extracted by the Reynolds stresses acting on the mean shear is expended in increasing the potential energy of the fluid column through a buoyancy flux, while most of it is dissipated. The mixing coefficient χm, the ratio of the buoyancy flux to the dissipation rate of turbulence kinetic energy ε, is an important parameter, since knowledge of χm enables turbulent diffusivities to be inferred. Theory indicates that χm must be a function of the gradient Richardson number. Yet, oceanic studies suggest that a value of around 0.2 for χm gives turbulent diffusivities that are in good agreement with those inferred from tracer studies. Studies by scientists working with atmospheric radars tend to reinforce these findings but are seldom referenced in oceanographic literature. The goal of this paper is to bring together oceanographic, atmospheric, and laboratory observations related to χm and to report on the values deduced from in situ data collected in the lower troposphere by unmanned aerial vehicles, equipped with turbulence sensors and flown in the vicinity of the Middle and Upper Atmosphere (MU) radar in Japan. These observations are consistent with past studies in the oceans, in that a value of around 0.16 for χm yields good agreement between ε derived from turbulent temperature fluctuations using this value and ε obtained directly from turbulence velocity fluctuations.

“…

The WPR-LQ-7 is a UHF (1.3575 GHz) wind profiler radar used for routine measurements of the lower troposphere 10 at Shigaraki MU observatory (34.85°N, 136.10°E, Japan) at a vertical resolution of 100 m and a time resolution of 10 min.Following studies carried out with the 46.5 MHz Middle and Upper atmosphere (MU) radar (Luce et al, 2018), we tested models used to estimate turbulence kinetic energy (TKE) dissipation rates 𝜀 from the Doppler spectral width in the altitude range ~0.7 to 4.0 km ASL. For this purpose, we compared LQ-7-derived 𝜀 by using processed data available on line (http://www.rish.kyoto-u.ac.jp/radar-group/blr/shigaraki/data/) with direct estimates of 𝜀 (𝜀 𝑈 ) from DataHawk UAVs.

…”

mentioning

confidence: 99%

“…Following studies carried out with the 46.5 MHz Middle and Upper atmosphere (MU) radar (Luce et al, 2018), we tested models used to estimate turbulence kinetic energy (TKE) dissipation rates 𝜀 from the Doppler spectral width in the altitude range ~0.7 to 4.0 km ASL. For this purpose, we compared LQ-7-derived 𝜀 by using processed data available on line (http://www.rish.kyoto-u.ac.jp/radar-group/blr/shigaraki/data/) with direct estimates of 𝜀 (𝜀 𝑈 ) from DataHawk UAVs.…”

mentioning

confidence: 99%

Turbulence Kinetic Energy dissipation rate: Assessment of radar models from comparisons between 1.3 GHz WPR and DataHawk UAV measurements

Luce

Hashiguchi

et al. 2023

Preprint

Abstract. The WPR-LQ-7 is a UHF (1.3575 GHz) wind profiler radar used for routine measurements of the lower troposphere at Shigaraki Middle and Upper (MU) observatory (34.85° N, 136.10° E, Japan) at a vertical resolution of 100 m and a time resolution of 10 min. Following studies carried out with the 46.5 MHz Middle and Upper atmosphere (MU) radar (Luce et al., 2018), we tested models used to estimate turbulence kinetic energy (TKE) dissipation rates ε from the Doppler spectral width in the altitude range ~0.7 to 4.0 km ASL. For this purpose, we compared LQ-7-derived ε by using processed data available on line (http://www.rish.kyoto-u.ac.jp/radar-group/blr/shigaraki/data/) with direct estimates of ε (εU) from DataHawk UAVs. The statistical results reveal the same trends as reported by Luce et al. (2018) with the MU radar, namely: (1) The simple formulation based on dimensional analysis εLout=σ3 / Lout, with Lout ~70 m, provides the best statistical agreement with εU. (2) The model εN predicting a σ2 N law (N is Brunt-Vaïsälä frequency) for stably stratified conditions tends to overestimate for εU < ~5 10−4 m2 s−3 and to underestimate for εU > ~5 10−4 m2 s−3. We also tested a model εS predicting a σ2 S law (S is the vertical shear of horizontal wind) supposed to be valid for low Richardson numbers (Ri = N2 ⁄ S2). From the case study of a turbulent layer produced by a Kelvin-Helmholtz instability, we found that εS and εLout are both very consistent with εU, while εN underestimates εU 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 scale defined for weakly stratified turbulence. A statistical analysis showed that εS also provides better statistical agreement with εU and is much less biased than εN. Combining estimates of N and shear from DataHawk and radar data, respectively, a rough estimate of the Richardson number at a vertical resolution of 100 m (Ri100) was obtained. We performed a statistical analysis on the Ri dependence of the models. The main outcome is that εS compares well with εU for low Ri100's (Ri100 < ~1) while εN fails. εLout varies as εS with Ri100 so that εLout remains the best (and simplest) model in the absence of information on Ri. Also, σ appears to vary as Ri100−1/2 when Ri100 > ~0.4 and shows a degree of dependence with S100 otherwise.