Articles | Volume 16, issue 15
https://doi.org/10.5194/amt-16-3561-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-3561-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Turbulence kinetic energy dissipation rate: assessment of radar models from comparisons between 1.3 GHz wind profiler radar (WPR) and DataHawk UAV measurements
Research Institute for Sustainable Humanosphere, Kyoto University,
Kyoto, 611-0011, Japan
Lakshmi Kantha
Smead Aerospace Engineering Sciences, University of Colorado, Boulder, CO, USA
Hiroyuki Hashiguchi
Research Institute for Sustainable Humanosphere, Kyoto University,
Kyoto, 611-0011, Japan
Dale Lawrence
Smead Aerospace Engineering Sciences, University of Colorado, Boulder, CO, USA
Abhiram Doddi
Smead Aerospace Engineering Sciences, University of Colorado, Boulder, CO, USA
Tyler Mixa
GATS Inc., Boulder, CO, USA
Masanori Yabuki
Research Institute for Sustainable Humanosphere, Kyoto University,
Kyoto, 611-0011, Japan
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Cited articles
Baumert, H. and Peters H.: Second-moment closures and length scales for
weakly stratified turbulent shear flows, J. Geophys. Res., 105, 6453–6468,
2000.
Basu, S. and Holtslag, A. A. M.: Turbulent Prandtl number and
characteristic length scales in stably stratified flows: steady-state
analytical solutions, Environ. Fluid Mech., 21, 1273–1302,
https://doi.org/10.1007/s10652-021-09820-7, 2021.
Basu, S. and Holtslag, A. A. M.: Revisiting and revising Tatarskii's
formulation for the temperature structure parameter in
atmospheric flows, Environ. Fluid Mech., 22, 1107–1119,
https://doi.org/10.1007/s10652-022-09880-3, 2022.
Basu, S., He, P., and De Marco, A. W.: Parameterizing the energy dissipation
rate in stably stratified flows, Bound.-Lay. Meteorol., 178, 167–184,
https://doi.org/10.1007/s10546-020-00559-0, 2021.
Bertin, F., Barat, J., and Wilson, R.: Energy dissipation rates, eddy
diffusivity, and the Prandtl number: An in situ experimental approach and
its consequences on radar estimate of turbulent parameters, Radio Sci., 32,
791–804, https://doi.org/10.1029/96RS03691,1997.
Chen, Z., Tian, Y., and Lü, D.: Turbulence parameters in the
troposphere-lower stratosphere observed by Beijing MST radar, Remote
Sens.-Basel, 14, 947, https://doi.org/10.3390/rs14040947, 2022.
Clayson, C. A. and Kantha, L.: On turbulence and mixing in the free
atmosphere inferred from high-resolution soundings, J. Atmos. Ocean. Tech.,
25, 833–852, https://doi.org/10.1175/2007JTECHA992.1, 2008.
Cohn, S. A.: Radar measurements of turbulent eddy dissipation rate in the
troposphere: a comparison of techniques, J. Atmos. Ocean. Tech., 12, 85–95,
https://doi.org/10.1175/1520-0426(1995)012>0085:RMOTED>2.0.CO;2, 1995.
Dehghan, A. and Hocking, W. K.: Instrumental errors in spectral-width
turbulence measurements by radars, J. Atmos. Sol.-Terr Phy., 73, 1052–1068,
https://doi.org/10.1016/j.jastp.2010.11.011, 2011.
Dehghan, A., Hocking, W. K., and Srinivasan, R.: Comparisons between
multiple in-situ aircraft turbulence measurements and radar in the
troposphere, J. Atmos. Sol.-Terr Phy., 118, 64–77,
https://doi.org/10.1016/j.jastp.2013.10.009, 2014.
Delage, D., Roca, R., Bertin, F., Delcourt, J., Crémieu, A., Masseboeuf,
M., and Ney, R.: A consistency check of three radar methods for monitoring
eddy diffusion and energy dissipation rates through the tropopause, Radio
Sci., 32, 757–767, https://doi.org/10.1029/96RS03543, 1997.
Dole, J., Wilson, R., Dalaudier, F., and Sidi, C.: Energetics of small scale turbulence in the lower stratosphere from high resolution radar measurements, Ann. Geophys., 19, 945–952, https://doi.org/10.5194/angeo-19-945-2001, 2001.
Doviak, R. J. and Zrnić, D. S.: Doppler radar and weather observations,
Academic Press, San Diego, ISBN 012221420X, 9780122214202, 1984.
Frisch, A. S. and Clifford, S. F.: A study of convection capped by a stable
layer using Doppler radar and acoustic echo sounders, J. Atmos. Sci., 31,
1622–1628, https://doi.org/10.1175/1520-0469(1974)031<1622:ASOCCB>2.0.CO;2, 1974.
Fritts, D., Wang, L., Geller, M. A., Werne, J., and Balsley, B. B.:
Numerical modeling of multiscale dynamics at a high Reynolds number:
Instabilities, turbulence, and an assessment of Ozmidov and Thorpe scales,
J. Atmos. Sci., 73, 555–577, https://doi.org/10.1175/JAS-D-14-0343.1, 2016.
Fukao, S., Yamanaka, M. D., Ao, N., Hocking, W. K., Sato, T., Yamamoto, M.,
Nakamura, T., Tsuda, T., and Kato, S.: Seasonal variability of vertical eddy
diffusivity in the middle atmosphere. 1. Three-year observations by the
middle and upper atmosphere radar, J. Geophys. Res.-Atmos., 99, 18973–18987,
https://doi.org/10.1029/94JD00911, 1994.
Garanaik, A. and Venayagamoorthy, S. K.: On the inference of the state of
turbulence and mixing efficiency in stably stratified flows, J. Fluid Mech.,
867, 323–333, https://doi.org/10.1017/jfm.2019.142, 2019.
Gerz, T., Schumann, U., and Elghobashi, S. E.: Direct numerical simulation
of stratified homogeneous turbulent shear flows, J. Fluid Mech., 200,
563–594, https://doi.org/10.1017/S0022112089000765, 1989.
Hocking, W. K.: On the extraction of atmospheric turbulence parameters from
radar backscatter Doppler spectra. I. Theory, J. Atmos. Terr. Phy., 45,
89–102, https://doi.org/10.1016/S0021-9169(83)80013-0, 1983.
Hocking, W. K.: Measurement of turbulent energy dissipation rates in the
middle atmosphere by radar techniques: a review, Radio Sci., 20, 1403–1422,
https://doi.org/10.1029/RS020i006p01403, 1985.
Hocking, W. K.: Observations and measurements of turbulence in the middle
atmosphere with a VHF radar, J. Atmos. Terr. Phy., 48, 655–670,
https://doi.org/10.1016/0021-9169(86)90015-2, 1986.
Hocking, W. K.: The dynamical parameters of turbulence theory as they apply
to middle atmosphere studies, Earth Planets Space, 51, 525–541,
https://doi.org/10.1186/BF03353213, 1999.
Hocking, W. K. and Hamza, A. M.: A quantitative measure of the degree of
anisotropy of turbulence in terms of atmospheric parameters, with particular
relevance to radar studies, J. Atmos. Sol-Terr. Phy., 59, 1011–1020,
https://doi.org/10.1016/S1364-6826(96)00074-0, 1997.
Hocking, W. K., Röttger, J., Palmer, R. D., Sato, T., and Chilson P. B.:
Atmospheric radar, Cambridge University Press, ISBN 9781107147461, 2016.
Hunt, J. C. R., Stretch, D. D., and Britter, R. E.: Length scales in
stably stratified turbulent flows and their use in turbulence models, in: Stably stratified flow and dense gas dispersion, edited by: Puttock, J. S., Clarendon Press, Oxford, 285–321, 1988.
Imai, K., Nakagawa, T., and Hashiguchi, H.: Development of tropospheric wind
profiler radar with Luneberg lens antenna (WPR LQ-7), Electric Wire &
Cable, Energy, 64, 38–42, 2007.
Ivey, G. N. and Imberger, J.: On the nature of turbulence in a stratified
fluid. Part I. The energetics of mixing, J. Phys. Oceanogr., 21, 650–659,
https://doi.org/10.1175/1520-0485(1991)021<0650:OTNOTI>2.0.CO;2, 1991.
Jacoby-Koaly, S., Campistron, B., Bernard, S., Bénech, B.,
Ardhuin-Girard, F., Dessens, J., Dupont, E., and Carissimo, B.: Turbulent
dissipation rate in the boundary layer via UHF wind profiler Doppler
spectral width measurements, Bound.-Lay. Meterol., 103, 3061–389,
https://doi.org/10.1023/A:1014985111855, 2002.
Jaiswal, A., Phanikumar, D. V., Bhattacharjee, S., and Naja, M.: Estimation
of turbulence parameters using ARIES ST radar and GPS radiosonde
measurements: First results from the Central Himalayan region, Radio Sci.,
55, e2019RS006979, https://doi.org/10.1029/2019RS006979, 2020.
Kalapureddy, M. C. R., Kumar, K. K., Sivakumar, V., Ghosh, A. K., Jain, A.
R., and Reddy, K. K.: Diurnal and seasonal variability of TKE dissipation
rate in the ABL over a tropical station using UHF wind profiler, J. Atmos.
Sol.-Terr. Phy., 69, 419–430, https://doi.org/10.1016/j.jastp.2006.10.016, 2007.
Kaltenbach, H.-J., Gerz, T., and Schumann, U.: Large-eddy simulation of
homogeneous turbulence and diffusion in stably stratified shear flow, J.
Fluid. Mech, 280, 1–40, https://doi.org/10.1017/S0022112094002831, 1994.
Kantha, L.: Turbulence dissipation rates in the free atmosphere from
high-resolution radiosondes, in: Chap. 7: Turbulence:Theory, Types and
Simulation, edited by: Marcuso, R. J., Nova Science Publ., 2010.
Kantha, L. and Hocking, W. K.: Dissipation rates of turbulence kinetic
energy in the free atmosphere: MST radar and radiosondes, J. Atmos. Sol.-Terr. Phy., 73, 1043–1051, https://doi.org/10.1016/j.jastp.2010.11.024, 2011.
Kantha, L., Lawrence, D., Luce, H., Hashiguchi, H., Tsuda, T., Wilson, R.,
Mixa, T., and Yabuki, M.: Shigaraki UAV-Radar Experiment (ShUREX 2015): An
overview of the campaign with some preliminary results, Progress in Earth and Planetary Science, 4, 19, https://doi.org/10.1186/s40645-017-0133-x, 2017.
Kohma, M., Sato, K., Tomikawa, Y., Nishimura, K., and Sato, T.: Estimate of
turbulent energy dissipation rate from the VHF radar and radiosonde
observations in the Antarctic, J. Geophys. Res.-Atmos., 124, 2976–2993,
doi.org/10.1029/2018JD029521, 2019.
Kurosaki, S., Yamanaka, M. D., Hashiguchi, H., Sato, T., and Fukao, S.:
Vertical eddy diffusivity in the lower and middle atmosphere: a climatology
based on the MU radar observations during 1986-1992, J. Atmos. Sol.-Terr.
Phy., 58, 727–734, https://doi.org/10.1016/0021-9169(95)00070-4, 1996.
Labitt, M.: Some basic relations concerning the radar measurements of air
turbulence, MIT Lincoln Laboratory, ATC Working Paper NO 46WP-5001, 1979.
Li, Q., Rapp, M., Schrön, A., Schneider, A., and Stober, G.: Derivation of turbulent energy dissipation rate with the Middle Atmosphere Alomar Radar System (MAARSY) and radiosondes at Andøya, Norway, Ann. Geophys., 34, 1209–1229, https://doi.org/10.5194/angeo-34-1209-2016, 2016.
Luce, H., Kantha, L., Hashiguchi, H., Lawrence, D., Yabuki, M., Tsuda, T., and Mixa, T.: Comparisons between high-resolution profiles of squared refractive index gradient M2 measured by the Middle and Upper Atmosphere Radar and unmanned aerial vehicles (UAVs) during the Shigaraki UAV-Radar Experiment 2015 campaign, Ann. Geophys., 35, 423–441, https://doi.org/10.5194/angeo-35-423-2017, 2017.
Luce, H., Kantha, L., Hashiguchi, H., Lawrence, D., and Doddi A.: Turbulence
kinetic energy dissipation rates estimated from concurrent UAV and MU radar
measurements, Earth Planets Space, 70, 207, https://doi.org/10.1186/s40623-018-0979-1, 2018.
Mater, B. D. and Venayagamoorthy, S. K.: A unifying framework for
parameterizing stably stratified shear-flow turbulence, Phys. Fluids, 26,
036601, https://doi.org/10.1063/1.4868142, 2014.
Mater, B. D., Schaad, S. M., and Venayagamoorthy, S. K.: Relevance of the
Thorpe length scale in stably stratified turbulence, Phys. Fluids, 25, 076604, https://doi.org/10.1063/1.4813809, 2013.
McCaffrey, K., Bianco, L., and Wilczak, J. M.: Improved observations of turbulence dissipation rates from wind profiling radars, Atmos. Meas. Tech., 10, 2595–2611, https://doi.org/10.5194/amt-10-2595-2017, 2017.
Mellor, G. L. and Yamada, T.: Development of turbulence closure model for
geophysical fluid problems, Rev. Geophys., 20, 851–875,
https://doi.org/10.1029/RG020i004p00851, 1982.
Nastrom, G. D.: Doppler radar spectral width broadening due to beamwidth and wind shear, Ann. Geophys., 15, 786–796, https://doi.org/10.1007/s00585-997-0786-7, 1997.
Naström, G. D. and Eaton, F. D.: Turbulence eddy dissipation rates from
radar observations at 5–20 km at White Sands Missile Range, New Mexico, J.
Geophys. Res.-Atmos., 102, 19495–19505, https://doi.org/10.1029/97JD01262, 1997.
Naström, G. D. and Eaton, F. D.: Seasonal variability of turbulence
parameters at 2 to 21 km from MST radar measurements at Vandenberg Air Force
Base, California, J. Geophys. Res., 110, D19110, https://doi.org/10.1029/2005JD005782,
2005.
RISH: Boundary Layer Radar (BLR)/Lower Troposphere Radar (LTR)/LQ7
Observation Data, RISH (Research Institute for Sustainable Humanosphere) [data set], Version 02.0212, http://www.rish.kyoto-u.ac.jp/radar-group/blr/shigaraki/data/ (last access: 26 July 2023), 2023.
Satheesan, K. and Krishna Murthy, B. V.: Turbulence parameters in the tropical
troposphere and lower stratosphere, J. Geophys. Res., 107, 4002,
https://doi.org/10.1029/2000JD000146, 2002.
Schumann, U.: Correlations in homogeneous stratified shear turbulence, Acta
Mech., 4, 105–111, 1994.
Schumann, U. and Gerz, T.: Turbulent mixing in stably stratified shear flows, J. Appl. Meteorol., 34, 33–48, https://doi.org/10.1175/1520-0450-34.1.33, 1995.
Shaw, W. J. and LeMone, M. A.: Turbulence dissipation rate measured by 915
MHz wind profiling radars compared with in-situ tower and aircraft data, in:
12th Symposium on Meteorological Observations and Instrumentation, Am.
Meteorol. Soc., California, 2003.
Singh, N., Joshi, R. R., Chun, H.-Y., Pant, G. B., Damle, S. H., and Vashishtha, R. D.: Seasonal, annual and inter-annual features of turbulence parameters over the tropical station Pune (18∘32′ N, 73∘51′ E) observed with UHF wind profiler, Ann. Geophys., 26, 3677–3692, https://doi.org/10.5194/angeo-26-3677-2008, 2008.
Smyth, W. D., Moum, J. M., and Caldwell, D. R.: The efficiency of mixing in
turbulent patches: Inferences from direct simulations and microstructure
observations, J. Phys. Oceanogr., 31, 1969–1992,
https://doi.org/10.1175/1520-0485(2001)031<1969:TEOMIT>2.0.CO;2, 2001.
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Kluwer
Academic, 666 pp., https://doi.org/10.1007/978-94-009-3027-8, 1988.
Weinstock, J.: Energy dissipation rates of turbulence in the free stable
atmosphere, J. Atmos. Sci., 38, 880–883,
https://doi.org/10.1175/1520-0469(1981)038<0880:EDROTI>2.0.CO;2, 1981.
White, A. B., Lataitis, R. J., and Lawrence, R. S.: Space and time filtering
of remotely sensed velocity turbulence, J. Atmos. Sci. Tech., 16, 1967–1972,
https://doi.org/10.1175/1520-0426(1999)016<1967:SATFOR>2.0.CO;2, 1999.
Wijesekera, H. W. and Dillon, T. M.: Shannon entropy as an indicator of age
for turbulent overturns in the oceanic thermocline, J. Geophys. Res., 102,
3279–3291, https://doi.org/10.1029/96JC03605, 1997.
Wilson, R., Dalaudier, F., and Bertin, F.: Estimation of the turbulent
fraction in the free atmosphere from MST radar measurements, J. Atmos.
Ocean. Tech., 22, 1326–1339, https://doi.org/10.1175/JTECH1783.1, 2005.
Wilson, R., Luce, H., Hashiguchi, H., Nishi, N., and Yabuki, M.: Energetics
of persistent turbulent layers underneath mid-level clouds estimated from
concurrent radar and radiosonde data, J. Atmos. Sol.-Terr. Phy., 118, 78–89, https://doi.org/10.1016/j.jastp.2014.01.005, 2014.
Short summary
Doppler radars can be used to estimate turbulence kinetic energy dissipation rates in the atmosphere. The performance of various models is evaluated from comparisons between UHF wind profiler and in situ measurements with UAVs. For the first time, we assess a model supposed to be valid for weak stratification or strong shear conditions. This model provides better agreements with in situ measurements than the classical model based on the hypothesis of a stable stratification.
Doppler radars can be used to estimate turbulence kinetic energy dissipation rates in the...