Evaluating Large-Eddy Simulations Using Volume Imaging Lidar Data

Shane D. Mayor Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by Shane D. Mayor in
Current site
Google Scholar
PubMed
Close
,
Gregory J. Tripoli Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by Gregory J. Tripoli in
Current site
Google Scholar
PubMed
Close
, and
Edwin W. Eloranta Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by Edwin W. Eloranta in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The authors apply data analysis techniques that demonstrate the power of using volume imaging lidar observations to evaluate several aspects of large-eddy simulations (LESs). They present observations and simulations of an intense and spatially evolving convective boundary layer on 13 January 1998 during the Lake-Induced Convection Experiment (Lake-ICE). To enable comparison of observed and simulated eddy structure, aerosol scattering was estimated from LES output of relative humidity, a passive tracer, and liquid water. Spatial and temporal correlation functions of aerosol structure from horizontal planes reveal the mean and turbulent convective structure. The correlation functions of the observed and simulated aerosol backscatter are presented as a function of altitude and offshore distance. Best-fit ellipses to the closed contours encircling the origin of the correlation functions are used to obtain the mean ellipticity and orientation of the structures. The authors demonstrate that these two parameters are not sensitive to minor changes in the functional relationship between humidity and optical scattering. The lidar-derived mean wind field is used as a reference for evaluating the LES mean flow.

The ellipses from lidar data indicate that structures near the surface tend to be aligned with the mean wind direction, while in the entrainment zone they are aligned perpendicular to the mean wind direction. In the middle of the mixed layer, convective plumes tended to be circular and, therefore, had no preferred orientation at small lags. At longer lags, however, the correlation functions from the middle of the mixed layer show that the observed convective plumes were organized into linear bands oriented perpendicular to the mean wind direction. The perpendicular bands suggest the important role of gravity waves in organizing convective structures. The study shows that the model generates reasonable coherent structures (open cells) where the LES technique is expected to perform poorly (near the surface) and fails to capture the wind-perpendicular organization of closed cells in the middle of the mixed layer where the LES technique is expected to be robust. The authors attribute this failure to the boundary conditions that limited the growth of waves above the mixed layer.

Current affiliation: Atmospheric Technology Division, National Center for Atmospheric Research, Boulder, Colorado

Corresponding author address: Dr. Shane D. Mayor, Atmospheric Technology Division, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000. Email: shane@ucar.edu

Abstract

The authors apply data analysis techniques that demonstrate the power of using volume imaging lidar observations to evaluate several aspects of large-eddy simulations (LESs). They present observations and simulations of an intense and spatially evolving convective boundary layer on 13 January 1998 during the Lake-Induced Convection Experiment (Lake-ICE). To enable comparison of observed and simulated eddy structure, aerosol scattering was estimated from LES output of relative humidity, a passive tracer, and liquid water. Spatial and temporal correlation functions of aerosol structure from horizontal planes reveal the mean and turbulent convective structure. The correlation functions of the observed and simulated aerosol backscatter are presented as a function of altitude and offshore distance. Best-fit ellipses to the closed contours encircling the origin of the correlation functions are used to obtain the mean ellipticity and orientation of the structures. The authors demonstrate that these two parameters are not sensitive to minor changes in the functional relationship between humidity and optical scattering. The lidar-derived mean wind field is used as a reference for evaluating the LES mean flow.

The ellipses from lidar data indicate that structures near the surface tend to be aligned with the mean wind direction, while in the entrainment zone they are aligned perpendicular to the mean wind direction. In the middle of the mixed layer, convective plumes tended to be circular and, therefore, had no preferred orientation at small lags. At longer lags, however, the correlation functions from the middle of the mixed layer show that the observed convective plumes were organized into linear bands oriented perpendicular to the mean wind direction. The perpendicular bands suggest the important role of gravity waves in organizing convective structures. The study shows that the model generates reasonable coherent structures (open cells) where the LES technique is expected to perform poorly (near the surface) and fails to capture the wind-perpendicular organization of closed cells in the middle of the mixed layer where the LES technique is expected to be robust. The authors attribute this failure to the boundary conditions that limited the growth of waves above the mixed layer.

Current affiliation: Atmospheric Technology Division, National Center for Atmospheric Research, Boulder, Colorado

Corresponding author address: Dr. Shane D. Mayor, Atmospheric Technology Division, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000. Email: shane@ucar.edu

Save
  • Andren, A., A. R. Brown, J. Graf, P. J. Mason, C-H. Moeng, F. T. M. Nieuwstadt, and U. Schumann, 1994: Large-eddy simulation of a neutrally stratified boundary layer: A comparison of four computer codes. Quart. J. Roy. Meteor. Soc., 120 , 1457–1484.

    • Search Google Scholar
    • Export Citation
  • Arakawa, A., and V. R. Lamb, 1981: A potential enstrophy and energy conserving scheme for the shallow water equations. Mon. Wea. Rev., 109 , 18–36.

    • Search Google Scholar
    • Export Citation
  • Atkinson, B. W., and J. W. Zhang, 1996: Mesoscale shallow convection in the atmosphere. Rev. Geophys., 34 , 403–431.

  • Avissar, R., E. W. Eloranta, K. Gurer, and G. J. Tripoli, 1998: An evaluation of the large-eddy simulation option of the regional atmospheric modeling system in simulating a convective boundary layer: A FIFE case study. J. Atmos. Sci., 55 , 1109–1130.

    • Search Google Scholar
    • Export Citation
  • Bevington, P. R., 1969: Data Reduction and Error Analysis for the Physical Sciences. McGraw-Hill, 336 pp.

  • Covert, D. S., R. J. Charlson, and N. C. Ahlquist, 1972: A study of the relationship of chemical composition and humidity to light scattering by aerosols. J. Appl. Meteor., 11 , 968–976.

    • Search Google Scholar
    • Export Citation
  • Eloranta, E. W., and D. K. Forrest, 1992: Volume-imaging lidar observations of the convective structure surrounding the flight path of a flux-measuring aircraft. J. Geophys. Res., 97 , 18383–18393.

    • Search Google Scholar
    • Export Citation
  • Eloranta, E. W., J. M. King, and J. A. Weinman, 1975: The determination of wind speeds in the boundary layer by monostatic lidar. J. Appl. Meteor., 14 , 1485–1489.

    • Search Google Scholar
    • Export Citation
  • Ferrare, R. A., E. W. Eloranta, and R. Coulter, 1991: Lidar observations of banded convection during BLX83. J. Appl. Meteor., 30 , 312–326.

    • Search Google Scholar
    • Export Citation
  • Fitzgerald, J. W., W. A. Hoppel, and M. A. Vietti, 1982: The size and scattering coefficient of urban aerosol particles at Washington, DC as a function of relative humidity. J. Atmos. Sci., 39 , 1838–1852.

    • Search Google Scholar
    • Export Citation
  • Gander, W., G. H. Golub, and R. Strebel, 1994: Fitting of circles and ellipses least squares solution. Tech. Rep. 217, Institut fur Wissenschaftliches Rechnen, Departement Informatik, ETH Zurich, Zurich, Switzerland, 57 pp.

    • Search Google Scholar
    • Export Citation
  • Gluhovsky, A., and E. Agee, 1994: A definitive approach to turbulence statistical studies in planetary boundary layers. J. Atmos. Sci., 51 , 1682–1690.

    • Search Google Scholar
    • Export Citation
  • Hauf, T., and T. L. Clark, 1989: Three-dimensional numerical experiments on convectively forced internal gravity waves. Quart. J. Roy. Meteor. Soc., 115 , 309–333.

    • Search Google Scholar
    • Export Citation
  • Khanna, S., and J. G. Brasseur, 1997: Analysis of Monin–Obukhov similarity from large-eddy simulations. J. Fluid Mech., 345 , 251–286.

    • Search Google Scholar
    • Export Citation
  • Konrad, T. G., 1970: The dynamics of the convective process in clear air as seen by radar. J. Atmos. Sci., 27 , 1138–1147.

  • Kristovich, D. A. R., and Coauthors. 2000: The Lake-Induced Convection Experiment and the Snowband Dynamics Project. Bull. Amer. Meteor. Soc., 81 , 519–542.

    • Search Google Scholar
    • Export Citation
  • Kunkel, K., 1978: Measurement of upper convective boundary layer parameters by means of lidar. Ph.D. thesis, University of Wisconsin—Madison, 129 pp.

    • Search Google Scholar
    • Export Citation
  • Kunkel, K., E. W. Eloranta, and J. Weinman, 1980: Remote determination of winds, turbulence spectra and energy dissipation rates in the boundary layer from lidar measurements. J. Atmos. Sci., 37 , 978–985.

    • Search Google Scholar
    • Export Citation
  • Lenschow, D. H., J. Mann, and L. Kristensen, 1994: How long is long enough when measuring fluxes and other turbulence statistics? J. Atmos. Oceanic Technol., 11 , 661–673.

    • Search Google Scholar
    • Export Citation
  • Lund, T. S., X. Wu, and K. D. Squires, 1998: Generation of turbulent inflow data for spatially-developing boundary layer simulations. J. Comput. Phys., 140 , 233–258.

    • Search Google Scholar
    • Export Citation
  • Mason, P. J., 1989: Large-eddy simulation of the convective atmospheric boundary layer. J. Atmos. Sci., 46 , 1492–1516.

  • Mayor, S. D., 2001: Volume imaging lidar observations and large-eddy simulations of convective internal boundary layers. Ph.D. thesis, University of Wisconsin—Madison, 177 pp.

    • Search Google Scholar
    • Export Citation
  • Mayor, S. D., and E. W. Eloranta, 2001: Two-dimensional vector wind fields from volume imaging lidar data. J. Appl. Meteor., 40 , 1331–1346.

    • Search Google Scholar
    • Export Citation
  • Mayor, S. D., G. J. Tripoli, E. W. Eloranta, and B. Hoggatt, 1999: Comparison of microscale convection patterns seen in lidar data and large-eddy simulations. Preprints, 13th Symp. on Boundary Layers and Turbulence, Dallas, TX, Amer. Meteor. Soc., 271–274.

    • Search Google Scholar
    • Export Citation
  • Mayor, S. D., P. R. Spalart, and G. J. Tripoli, 2002: Application of a perturbation recycling method to a large eddy simulation of a mesoscale convective internal boundary layer. J. Atmos. Sci., 59 , 2385–2395.

    • Search Google Scholar
    • Export Citation
  • Nieuwstadt, F. T. M., P. J. Mason, C-H. Moeng, and U. Schumann, 1993: Large-eddy simulation of the convective boundary layer: A comparison of four computer codes. Turbulent Shear Flows 8, F. Friedrich, et al., Eds., Springer-Verlag, 343–367.

    • Search Google Scholar
    • Export Citation
  • Porch, W. M., and D. A. Gillette, 1977: A comparison of aerosol and momentum mixing in dust storms using fast-response instruments. J. Appl. Meteor., 16 , 1273–1281.

    • Search Google Scholar
    • Export Citation
  • Sadourny, R., 1975: The dynamics of finite-difference models of the shallow-water equations. J. Atmos. Sci., 32 , 680–689.

  • Sasano, Y., H. Hirohara, T. Yamasaki, H. Shimizu, N. Takeuchi, and T. Kawamura, 1982: Horizontal wind vector determination from the displacement of aerosol distribution patterns observed by a scanning lidar. J. Appl. Meteor., 21 , 1516–1523.

    • Search Google Scholar
    • Export Citation
  • Schmidt, H., and U. Schumann, 1989: Coherent structures of the convective boundary layer derived from large-eddy simulations. J. Fluid Mech., 200 , 511–562.

    • Search Google Scholar
    • Export Citation
  • Schols, J. L., and E. W. Eloranta, 1992: The calculation of area-averaged vertical profiles of the horizontal wind velocity from volume imaging lidar data. J. Geophys. Res., 97 , 18395–18407.

    • Search Google Scholar
    • Export Citation
  • Spalart, P. R., 1988: Direct simulation of a turbulent boundary layer up to rθ = 1140. J. Fluid Mech., 187 , 61–98.

  • Stevens, B., and D. H. Lenschow, 2001: Observations, experiments, and large eddy simulations. Bull. Amer. Meteor. Soc., 82 , 283–294.

    • Search Google Scholar
    • Export Citation
  • Tripoli, G. J., 1992: A nonhydrostatic mesoscale model designed to simulate scale interaction. Mon. Wea. Rev., 120 , 1342–1359.

  • Willis, G. E., and J. W. Deardorff, 1979: Laboratory observations of turbulent penetrative-convection planforms. J. Geophys. Res., 84 , 295–302.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 89 43 4
PDF Downloads 47 25 1