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

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) O...

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Main Authors: Hubert Luce, Lakshmi Kantha, Hiroyuki Hashiguchi, Dale Lawrence
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Atmosphere
Subjects:
Online Access:https://www.mdpi.com/2073-4433/10/7/384
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language English
format Article
sources DOAJ
author Hubert Luce
Lakshmi Kantha
Hiroyuki Hashiguchi
Dale Lawrence
spellingShingle Hubert Luce
Lakshmi Kantha
Hiroyuki Hashiguchi
Dale Lawrence
Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data
Atmosphere
turbulence
energy dissipation rate
temperature structure function
eddy diffusivity
outer scale
mixing efficiency
Ozmidov length scale
Kolmogorov turbulence
author_facet Hubert Luce
Lakshmi Kantha
Hiroyuki Hashiguchi
Dale Lawrence
author_sort Hubert Luce
title Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data
title_short Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data
title_full Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data
title_fullStr Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data
title_full_unstemmed Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data
title_sort estimation of turbulence parameters in the lower troposphere from shurex (2016–2017) uav data
publisher MDPI AG
series Atmosphere
issn 2073-4433
publishDate 2019-07-01
description 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 <inline-formula> <math display="inline"> <semantics> <mi>&#949;</mi> </semantics> </math> </inline-formula> and temperature structure function parameter <inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mi>C</mi> <mi>T</mi> <mn>2</mn> </msubsup> </mrow> </semantics> </math> </inline-formula> from one-dimensional wind and temperature frequency spectra are first described in detail. Both are based on the identification of inertial (&#8722;5/3) subranges in respective spectra. Using a formulation relating <inline-formula> <math display="inline"> <semantics> <mi>&#949;</mi> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mi>C</mi> <mi>T</mi> <mn>2</mn> </msubsup> </mrow> </semantics> </math> </inline-formula> valid for Kolmogorov turbulence in steady state, the flux Richardson number <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>f</mi> </msub> </mrow> </semantics> </math> </inline-formula> and the mixing efficiency <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>&#967;</mi> <mi>m</mi> </msub> </mrow> </semantics> </math> </inline-formula> are then estimated. The statistical analysis confirms the variability of <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>f</mi> </msub> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>&#967;</mi> <mi>m</mi> </msub> </mrow> </semantics> </math> </inline-formula> around <inline-formula> <math display="inline"> <semantics> <mrow> <mo>~</mo> <mn>0.13</mn> <mo>&#8722;</mo> <mn>0.14</mn> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mo>~</mo> <mn>0.16</mn> <mo>&#8722;</mo> <mn>0.17</mn> </mrow> </semantics> </math> </inline-formula>, 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 <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>L</mi> <mi>O</mi> </msub> </mrow> </semantics> </math> </inline-formula>, the buoyancy Reynolds number <inline-formula> <math display="inline"> <semantics> <mrow> <mi>R</mi> <msub> <mi>e</mi> <mi>b</mi> </msub> </mrow> </semantics> </math> </inline-formula>, and the gradient Richardson number <i>Ri</i>. Finally, a case study is presented showing altitude differences between the peaks of <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>N</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mi>C</mi> <mi>T</mi> <mn>2</mn> </msubsup> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mi>&#949;</mi> </semantics> </math> </inline-formula>, 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.
topic turbulence
energy dissipation rate
temperature structure function
eddy diffusivity
outer scale
mixing efficiency
Ozmidov length scale
Kolmogorov turbulence
url https://www.mdpi.com/2073-4433/10/7/384
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spelling doaj-1a93f8008d914c479314039b0eeb0d992020-11-24T21:49:00ZengMDPI AGAtmosphere2073-44332019-07-0110738410.3390/atmos10070384atmos10070384Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV DataHubert Luce0Lakshmi Kantha1Hiroyuki Hashiguchi2Dale Lawrence3Université de Toulon, Aix-Marseille University, CNRS IRD, MIO, UM110, 83041 Toulon, FranceDepartment of Aerospace Engineering Sciences, University of Colorado, Boulder, CO 80309, USAResearch Institute for Sustainable Humanosphere, Kyoto University, Kyoto 611-0011, JapanDepartment of Aerospace Engineering Sciences, University of Colorado, Boulder, CO 80309, USATurbulence 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 <inline-formula> <math display="inline"> <semantics> <mi>&#949;</mi> </semantics> </math> </inline-formula> and temperature structure function parameter <inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mi>C</mi> <mi>T</mi> <mn>2</mn> </msubsup> </mrow> </semantics> </math> </inline-formula> from one-dimensional wind and temperature frequency spectra are first described in detail. Both are based on the identification of inertial (&#8722;5/3) subranges in respective spectra. Using a formulation relating <inline-formula> <math display="inline"> <semantics> <mi>&#949;</mi> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mi>C</mi> <mi>T</mi> <mn>2</mn> </msubsup> </mrow> </semantics> </math> </inline-formula> valid for Kolmogorov turbulence in steady state, the flux Richardson number <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>f</mi> </msub> </mrow> </semantics> </math> </inline-formula> and the mixing efficiency <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>&#967;</mi> <mi>m</mi> </msub> </mrow> </semantics> </math> </inline-formula> are then estimated. The statistical analysis confirms the variability of <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>f</mi> </msub> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>&#967;</mi> <mi>m</mi> </msub> </mrow> </semantics> </math> </inline-formula> around <inline-formula> <math display="inline"> <semantics> <mrow> <mo>~</mo> <mn>0.13</mn> <mo>&#8722;</mo> <mn>0.14</mn> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mo>~</mo> <mn>0.16</mn> <mo>&#8722;</mo> <mn>0.17</mn> </mrow> </semantics> </math> </inline-formula>, 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 <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>L</mi> <mi>O</mi> </msub> </mrow> </semantics> </math> </inline-formula>, the buoyancy Reynolds number <inline-formula> <math display="inline"> <semantics> <mrow> <mi>R</mi> <msub> <mi>e</mi> <mi>b</mi> </msub> </mrow> </semantics> </math> </inline-formula>, and the gradient Richardson number <i>Ri</i>. Finally, a case study is presented showing altitude differences between the peaks of <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>N</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <msubsup> <mi>C</mi> <mi>T</mi> <mn>2</mn> </msubsup> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mi>&#949;</mi> </semantics> </math> </inline-formula>, 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.https://www.mdpi.com/2073-4433/10/7/384turbulenceenergy dissipation ratetemperature structure functioneddy diffusivityouter scalemixing efficiencyOzmidov length scaleKolmogorov turbulence