Mixing Coefficient in Stably Stratified Flows

Kantha, Lakshmi; Luce, Hubert

doi:10.1175/jpo-d-18-0139.1

Cited by 13 publications

(17 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…C CT is traditionally called the mixing efficiency for CT (e.g., Oakey 1985), but it is actually a mixing coefficient (Gregg et al 2018;Kantha and Luce 2018). From Eqs.…”

Section: A2 Eddy Diffusivitymentioning

confidence: 99%

“…(68 and 67), it is determined that R f is the rate of conversion of turbulent energy produced (from various energy sources) to buoyancy energy needed to mix stratification layers, and C CT is simply the ratio of consumed buoyancy energy to energy dissipation by viscosity. Hereafter, C CT is called the mixing coefficient (Gregg et al 2018;Kantha and Luce 2018).…”

Section: A2 Eddy Diffusivitymentioning

confidence: 99%

“…Inoue et al (2007) used R eb for discriminating DDC from turbulence (R eb \ 20, CT is depressed, a nd DDC prevails, from Yamazaki, 1990). See Kantha and Luce (2018) for the significance of R eb .…”

Section: A2 Eddy Diffusivitymentioning

confidence: 99%

See 2 more Smart Citations

A note on estimating eddy diffusivity for oceanic double-diffusive convection

Nakano

Yoshida

2019

J Oceanogr

View full text Add to dashboard Cite

In this note, we provide an overview of the theoretical, numerical, and observational studies focused on oceanic eddy diffusivity, with an emphasis on double-diffusive convection (DDC). DDC, when calculated using the turbulent kinetic energy (TKE) equation, produces a negative diffusion of density. A second-moment closure model shows that DDC is effective within a narrow range. Other parameterizations can use in the actual sea, but improvements are still needed. Mixing coefficients referring to mixing efficiency are key factors when distinguishing DDC from conventional turbulence. Here, we show that measurements involving the gradient Richardson number, the buoyancy Reynolds number, and density ratio play a crucial role in determining eddy diffusivity in the presence of DDC. Therefore, deployment of a microstructure profiler together with either an acoustic Doppler current profiler (ADCP), lowered ADCP, or electromagnetic current meter is essential when measuring eddy diffusivity in the ocean's interior. Keywords Double-diffusive convection Á Mixing coefficient Á Mixing Á Turbulence Á Eddy diffusivity Á Kinetic energy dissipation rate Á Density ratio Á Gradient Richardson number Á Parameterization

show abstract

“…C CT is traditionally called the mixing efficiency for CT (e.g., Oakey 1985), but it is actually a mixing coefficient (Gregg et al 2018;Kantha and Luce 2018). From Eqs.…”

Section: A2 Eddy Diffusivitymentioning

confidence: 99%

Section: A2 Eddy Diffusivitymentioning

confidence: 99%

See 1 more Smart Citation

A note on estimating eddy diffusivity for oceanic double-diffusive convection

Nakano

Yoshida

2019

J Oceanogr

View full text Add to dashboard Cite

show abstract

“…High‐resolution estimates from radar indicated that the range of γ is from 0.06 to 0.3 in the upper troposphere and lower stratosphere (Dole et al, ). A recent observational study suggests that the mixing coefficient is around 0.16–0.2 in both the atmosphere and ocean (Kantha & Luce, ).…”

Section: Introductionmentioning

confidence: 99%

Estimate of Turbulent Energy Dissipation Rate From the VHF Radar and Radiosonde Observations in the Antarctic

et al. 2019

View full text Add to dashboard Cite

This study estimated the turbulent kinetic energy dissipation rates (TKEDRs) from 1‐year observations of the Program of the Antarctic Syowa Mesosphere‐Stratosphere‐Troposphere/Incoherent Scatter radar (PANSY radar) from October 2015 to September 2016 and compared the results with estimates from radiosonde measurements based on Thorpe's method. The radar‐based estimates showed that the TKEDR at Syowa Station was on the order of 10−5–10−3 m2/s3 in the altitude range of 1.5–19 km. Taking the proportional constant for Thorpe's method (the ratio of the Thorpe scale to Ozmidov scale) as unity, the radiosonde‐based measurements show values of TKEDR larger than radar‐based estimates by a factor of 2–5. The difference in the TKEDR between radiosonde‐ and radar‐based estimates is larger in the middle and upper troposphere than in the stratosphere. According to previous observational and numerical studies, Thorpe's method tends to overestimate the TKEDR for deep overturning layers. It is confirmed that the depth of the overturning layer is negatively correlated with the difference between radiosonde‐ and radar‐based estimates. The seasonal variation was also examined. An analysis using the distance from the local tropopause level showed that the local maximum in the TKEDR around the tropopause is particularly clear in austral summer. This is likely connected to the seasonality in the gravity wave activity in the Antarctic stratosphere.

show abstract

“…For locally homogeneous, stationary and isotropic turbulence in dry (unsaturated) air produced by shear instabilities in a stably stratified background, ε and C 2 T are theoretically interrelated. ε can be estimated from C 2 T using [20,21]:…”

Section: Introductionmentioning

confidence: 99%

Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data

et al. 2019

Self Cite

View full text Add to dashboard Cite

Turbulence parameters in the lower troposphere (up to ~4.5 km) are estimated from measurements of high-resolution and fast-response cold-wire temperature and Pitot tube velocity from sensors onboard DataHawk Unmanned Aerial Vehicles (UAVs) operated at the Shigaraki Middle and Upper atmosphere (MU) Observatory during two ShUREX (Shigaraki UAV Radar Experiment) campaigns in 2016 and 2017. The practical processing methods used for estimating turbulence kinetic energy dissipation rate ε and temperature structure function parameter C T 2 from one-dimensional wind and temperature frequency spectra are first described in detail. Both are based on the identification of inertial (−5/3) subranges in respective spectra. Using a formulation relating ε and C T 2 valid for Kolmogorov turbulence in steady state, the flux Richardson number R f and the mixing efficiency χ m are then estimated. The statistical analysis confirms the variability of R f and χ m around ~ 0.13 − 0.14 and ~ 0.16 − 0.17 , respectively, values close to the canonical values found from some earlier experimental and theoretical studies of both the atmosphere and the oceans. The relevance of the interpretation of the inertial subranges in terms of Kolmogorov turbulence is confirmed by assessing the consistency of additional parameters, the Ozmidov length scale L O , the buoyancy Reynolds number R e b , and the gradient Richardson number Ri. Finally, a case study is presented showing altitude differences between the peaks of N 2 , C T 2 and ε , suggesting turbulent stirring at the margin of a stable temperature gradient sheet. The possible contribution of this sheet and layer structure on clear air radar backscattering mechanisms is examined.

show abstract

Mixing Coefficient in Stably Stratified Flows

Cited by 13 publications

References 73 publications

A note on estimating eddy diffusivity for oceanic double-diffusive convection

A note on estimating eddy diffusivity for oceanic double-diffusive convection

Estimate of Turbulent Energy Dissipation Rate From the VHF Radar and Radiosonde Observations in the Antarctic

Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data

Contact Info

Product

Resources

About