• Banakh, V. A., and Smalikho I. N. , 1997: Estimation of turbulent energy dissipation rate from data of pulse Doppler lidar. Atmos. Oceanic Opt., 10 , 957965.

    • Search Google Scholar
    • Export Citation
  • Banakh, V. A., Smalikho I. N. , Köpp F. , and Werner C. , 1999: Measurements of turbulent energy dissipation rate with a CW Doppler lidar in the atmospheric boundary layer. J. Atmos. Oceanic Technol., 16 , 10441061.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouniol, D., Illingworth A. J. , and Hogan R. J. , 2003: Deriving turbulent kinetic energy dissipation rate within clouds using ground based 94 GHz radar. Preprints, 31st Conf. on Radar Meteorology, Seattle, WA, Amer. Meteor. Soc., 193–196.

    • Search Google Scholar
    • Export Citation
  • Brewster, K. A., and Zrnić D. S. , 1986: Comparison of eddy dissipation rates from spatial spectra of Doppler velocities and Doppler spectrum widths. J. Atmos. Oceanic Technol., 3 , 440452.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caughey, S. J., and Palmer S. G. , 1979: Some aspects of turbulence structure through the depth of the convective boundary layer. Quart. J. Roy. Meteor. Soc., 105 , 811827.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caughey, S. J., Wyngaard J. , and Kaimal J. , 1979: Turbulence in the evolving stable boundary layer. J. Atmos. Sci., 36 , 10411052.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chapman, D., and Browning K. , 2001: Measurements of dissipation rate in frontal zones. Quart. J. Roy. Meteor. Soc., 127 , 19391959.

  • Chen, W. Y., 1974: Energy dissipation rates of free atmospheric turbulence. J. Atmos. Sci., 31 , 22222225.

  • Cohn, S. A., 1995: Radar measurements of turbulent eddy dissipation rate in the troposphere: A comparison of techniques. J. Atmos. Oceanic Technol., 12 , 8595.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davies, F., Collier C. G. , Pearson G. N. , and Bozier K. E. , 2004: Doppler lidar measurements of turbulent structure function over an urban area. J. Atmos. Oceanic Technol., 21 , 753761.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davies, F., Collier C. G. , and Bozier K. E. , 2005: Errors associated with dual Doppler lidar turbulence measurements. J. Opt. A, 7 , 280289.

  • Doviak, R. J., and Zrnić D. S. , 1993: Doppler Radar and Weather Observations. 2nd ed. Academic Press, 562 pp.

  • Drobinski, P., Brown R. A. , Flamant P. H. , and Pelon J. , 2004: Evidence of organized large eddies by ground-based Doppler lidar, sonic anemometer and sodar. Bound.-Layer Meteor., 88 , 343361.

    • Search Google Scholar
    • Export Citation
  • Fairall, C. W., Markson R. , Schacher G. E. , and Davidson K. L. , 1980: An aircraft study of turbulence dissipation rate and temperature structure function in the unstable marine atmospheric boundary layer. Bound.-Layer Meteor., 18 , 453469.

    • Search Google Scholar
    • Export Citation
  • Frehlich, R., 2001: Estimation of velocity error for Doppler lidar measurements. J. Atmos. Oceanic Technol., 18 , 16281639.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frehlich, R., and Cornman L. , 2002: Estimating spatial velocity statistics with coherent Doppler lidar. J. Atmos. Oceanic Technol., 19 , 355366.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frehlich, R., Hannon S. , and Henderson S. , 1998: Coherent Doppler lidar measurements of wind field statistics. Bound.-Layer Meteor., 86 , 233256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gal-Chen, T., Xu M. , and Eberhard W. L. , 1992: Estimation of atmospheric boundary layer fluxes and other turbulence parameters from Doppler lidar data. J. Geophys. Res., 97 , 1840918423.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harrison, R. G., Heath A. M. , Hogan R. J. , and Rogers G. W. , 2009: Comparison of balloon-carried atmospheric motion sensors with Doppler lidar turbulence measurements. Rev. Sci. Instrum., 80 , 026108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hignett, P., 1991: Observations of diurnal variation in a cloud-capped marine boundary layer. J. Atmos. Sci., 48 , 14741482.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hill, M. K., Brooks B. J. , Norris S. J. , Smith M. H. , Brooks I. M. , and de Leeuw G. , 2008: A Compact Lightweight Aerosol Spectrometer Probe (CLASP). J. Atmos. Oceanic Technol., 25 , 19962006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kolmogorov, A. N., 1941: Dissipation of energy in locally isotropic turbulence. Dokl. Akad. Nauk SSSR, 32 , 1618.

  • Lenschow, D. H., Wulfmeyer V. , and Senff C. , 2000: Measuring second- through fourth-order moments in noisy data. J. Atmos. Oceanic Technol., 17 , 13301347.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lothon, M., Lenschow D. H. , and Mayor S. D. , 2009: Doppler lidar measurements of vertical velocity spectra in the convective planetary boundary layer. Bound.-Layer Meteor., 132 , 205226.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martin, D., and Coauthors, 2009: Tracer concentration profiles measured in central London as part of the REPARTEE campaign. Atmos. Chem. Phys. Discuss., 9 , 2524525274.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKay, J. A., 1998: Modeling of direct detection Doppler wind lidar. I. The edge technique. Appl. Opt., 37 , 64806486.

  • O’Connor, E. J., Hogan R. J. , and Illingworth A. J. , 2005: Retrieving stratocumulus drizzle parameters using Doppler radar and lidar. J. Appl. Meteor., 44 , 1427.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Panagi, P., Dicks E. , Hamer G. , and Nash J. , 2001: Preliminary results of the routine comparison of wind profiler data with the Meteorological Office Unified Model vertical wind profiles. Phys. Chem. Earth, 26B , 187191.

    • Search Google Scholar
    • Export Citation
  • Paquin, J. E., and Pond S. , 1971: The determination of the Kolmogoroff constants for velocity, temperature and moisture from second and third order structure functions. J. Fluid Mech., 50 , 257269.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pearson, G., Davies F. , and Collier C. , 2009: An analysis of the performance of the UFAM pulsed Doppler lidar for observing the boundary layer. J. Atmos. Oceanic Technol, 26 , 240250.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinsky, M., Khain A. , and Krugliak H. , 2008: Collisions of cloud droplets in a turbulent flow. Part V: Application of detailed tables of turbulent collision rate enhancement to simulation of droplet spectra evolution. J. Atmos. Sci., 65 , 357374.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rye, B. J., 1979: Antenna parameters for incoherent backscatter heterodyne lidar. Appl. Opt., 18 , 13901398.

  • Rye, B. J., and Hardesty R. M. , 1993: Discrete spectral peak estimation in incoherent backscatter heterodyne lidar. I: Spectral accumulation and the Cramer-Rao lower bound. IEEE Trans. Geosci. Remote Sens., 31 , 1627.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siebert, H., Wendisch M. , Conrath T. , Teichmann U. , and Heintzenberg J. , 2003: A new tethered balloon-borne payload for fine-scale observations in the cloudy boundary layer. Bound.-Layer Meteor., 106 , 461482.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siebert, H., Lehmann K. , and Wendisch M. , 2006: Observations of small-scale turbulence and energy dissipation rates in the cloudy boundary layer. J. Atmos. Sci., 63 , 14511466.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, G. I., 1935: Statistical theory of turbulence. Roy. Soc. London Proc., 151A , 421444.

  • Wehner, B., and Coauthors, 2010: Observations of turbulence-induced new particle formation in the residual layer. Atmos. Chem. Phys., 10 , 43194330.

    • Crossref
    • Search Google Scholar
    • Export Citation
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A Method for Estimating the Turbulent Kinetic Energy Dissipation Rate from a Vertically Pointing Doppler Lidar, and Independent Evaluation from Balloon-Borne In Situ Measurements

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  • * Department of Meteorology, University of Reading, Reading, United Kingdom
  • | + Finnish Meteorological Institute, Helsinki, Finland
  • | # University of Leeds, Leeds, United Kingdom
  • | @ School of Environment and Life Sciences, University of Salford, Salford, United Kingdom
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Abstract

A method of estimating dissipation rates from a vertically pointing Doppler lidar with high temporal and spatial resolution has been evaluated by comparison with independent measurements derived from a balloon-borne sonic anemometer. This method utilizes the variance of the mean Doppler velocity from a number of sequential samples and requires an estimate of the horizontal wind speed. The noise contribution to the variance can be estimated from the observed signal-to-noise ratio and removed where appropriate. The relative size of the noise variance to the observed variance provides a measure of the confidence in the retrieval. Comparison with in situ dissipation rates derived from the balloon-borne sonic anemometer reveal that this particular Doppler lidar is capable of retrieving dissipation rates over a range of at least three orders of magnitude.

This method is most suitable for retrieval of dissipation rates within the convective well-mixed boundary layer where the scales of motion that the Doppler lidar probes remain well within the inertial subrange. Caution must be applied when estimating dissipation rates in more quiescent conditions. For the particular Doppler lidar described here, the selection of suitably short integration times will permit this method to be applicable in such situations but at the expense of accuracy in the Doppler velocity estimates. The two case studies presented here suggest that, with profiles every 4 s, reliable estimates of ε can be derived to within at least an order of magnitude throughout almost all of the lowest 2 km and, in the convective boundary layer, to within 50%. Increasing the integration time for individual profiles to 30 s can improve the accuracy substantially but potentially confines retrievals to within the convective boundary layer. Therefore, optimization of certain instrument parameters may be required for specific implementations.

Corresponding author address: Ewan J. O’Connor, Dept. of Meteorology, University of Reading, Earley Gate, P.O. Box 243, Reading RG6 6BB, United Kingdom. Email: e.j.oconnor@reading.ac.uk

Abstract

A method of estimating dissipation rates from a vertically pointing Doppler lidar with high temporal and spatial resolution has been evaluated by comparison with independent measurements derived from a balloon-borne sonic anemometer. This method utilizes the variance of the mean Doppler velocity from a number of sequential samples and requires an estimate of the horizontal wind speed. The noise contribution to the variance can be estimated from the observed signal-to-noise ratio and removed where appropriate. The relative size of the noise variance to the observed variance provides a measure of the confidence in the retrieval. Comparison with in situ dissipation rates derived from the balloon-borne sonic anemometer reveal that this particular Doppler lidar is capable of retrieving dissipation rates over a range of at least three orders of magnitude.

This method is most suitable for retrieval of dissipation rates within the convective well-mixed boundary layer where the scales of motion that the Doppler lidar probes remain well within the inertial subrange. Caution must be applied when estimating dissipation rates in more quiescent conditions. For the particular Doppler lidar described here, the selection of suitably short integration times will permit this method to be applicable in such situations but at the expense of accuracy in the Doppler velocity estimates. The two case studies presented here suggest that, with profiles every 4 s, reliable estimates of ε can be derived to within at least an order of magnitude throughout almost all of the lowest 2 km and, in the convective boundary layer, to within 50%. Increasing the integration time for individual profiles to 30 s can improve the accuracy substantially but potentially confines retrievals to within the convective boundary layer. Therefore, optimization of certain instrument parameters may be required for specific implementations.

Corresponding author address: Ewan J. O’Connor, Dept. of Meteorology, University of Reading, Earley Gate, P.O. Box 243, Reading RG6 6BB, United Kingdom. Email: e.j.oconnor@reading.ac.uk

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