• Antonia, R. A., , and Zhu Y. , 1994: Inertial range behavior of the longitudinal heat flux cospectrum. Bound.-Layer Meteor., 70, 429434.

    • Search Google Scholar
    • Export Citation
  • Avissar, R., and Coauthors, 2009: The Duke University Helicopter Observation Platform. Bull. Amer. Meteor. Soc., 90, 939954.

  • Beswick, K. M., , Gallagher M. W. , , Webb A. R. , , Norton E. G. , , and Perry F. , 2008: Application of the Aventech AIMMS20AQ airborne probe for turbulence measurements during the Convective Storm Initiation Project. Atmos. Chem. Phys., 8, 54495463.

    • Search Google Scholar
    • Export Citation
  • Brown, E. N., , Friehe C. A. , , and Lenschow D. H. , 1983: The use of pressure fluctuations on the nose of an aircraft for measuring air motion. J. Climate Appl. Meteor., 22, 171180.

    • Search Google Scholar
    • Export Citation
  • Crawford, T. L., , and Dobosy R. J. , 1992: A sensitive fast-response probe to measure turbulence and heat flux from any airplane. Bound.-Layer Meteor., 59, 257278.

    • Search Google Scholar
    • Export Citation
  • Datig, M., , and Schlurmann T. , 2004: Performance and limitations of the Hilbert–Huang transformation (HHT) with an application to irregular water waves. Ocean Eng., 31, 17831834.

    • Search Google Scholar
    • Export Citation
  • Duffy, D. G., 2004: The application of Hilbert–Huang transforms to meteorological datasets. J. Atmos. Oceanic Technol., 21, 599611.

    • Search Google Scholar
    • Export Citation
  • Huang, N. E., , Long S. R. , , and Shen Z. , 1996: The mechanism for frequency downshift in nonlinear wave evolution. Adv. Appl. Mech., 32, 59111.

    • Search Google Scholar
    • Export Citation
  • Huang, N. E., and Coauthors, 1998: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. London, 454, 903993.

    • Search Google Scholar
    • Export Citation
  • Huang, N. E., , Shen Z. , , and Long S. R. , 1999: A new view of nonlinear water waves—The Hilbert spectrum. Annu. Rev. Fluid Mech., 31, 417457.

    • Search Google Scholar
    • Export Citation
  • Huang, N. E., , Wu M. L. , , Long S. R. , , Shen S. S. , , Qu W. D. , , Gloersen P. , , and Fan K. L. , 2003: A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc. Roy. Soc. London, 459A, 23172345.

    • Search Google Scholar
    • Export Citation
  • Kader, B. A., , and Yaglom A. M. , 1991: Spectra and correlation functions of surface layer atmospheric turbulence in unstable thermal stratification. Turbulence and Coherent Structures, O. Metals and M. Lesieur, Eds., Kluwer, 387–412.

    • Search Google Scholar
    • Export Citation
  • Kaimal, J. C., , Wyngaard J. C. , , Izumi Y. , , and Coté O. R. , 1972: Spectral characteristics of surface-layer turbulence. Quart. J. Roy. Meteor. Soc., 98, 563589.

    • Search Google Scholar
    • Export Citation
  • Lenschow, D. H., 1986: Aircraft measurements in the boundary layer. Probing the Atmospheric Boundary Layer, D. H. Lenschow, Ed., Amer. Meteor. Soc, 39–55.

    • Search Google Scholar
    • Export Citation
  • Lumley, J. L., 1964: The spectrum of nearly inertial turbulence in a stably stratified fluid. J. Atmos. Sci., 21, 99102.

  • Lundquist, J. K., 2003: Intermittent and elliptical inertial oscillations in the atmospheric boundary layer. J. Atmos. Sci., 60, 26612673.

    • Search Google Scholar
    • Export Citation
  • Mickle, R. E., 2005: Evaluation of the AIMMS-20 Airborne Meteorological Package. REMSpC Rep. 2005-01, 21 pp. [Available online at http://www.sergreport.net.]

    • Search Google Scholar
    • Export Citation
  • Pan, J., , Yan X.-H. , , Zheng Q. , , Liu W. T. , , and Klemas V. V. , 2002: Interpretation of scatterometer ocean surface wind vector EOFs over the northwestern Pacific. Remote Sens. Environ., 84, 5368.

    • Search Google Scholar
    • Export Citation
  • Rilling, G., , Flandrin P. , , and Gonçalvès P. , 2003: On empirical mode decomposition and its algorithms. Proc. Workshop on Nonlinear Signal and Image Processing, Grado, Italy, IEEE–EURASIP.

    • Search Google Scholar
    • Export Citation
  • Salisbury, J. I., , and Wimbush M. , 2002: Using modern time series analysis techniques to predict ENSO events from the SOI time series. Nonlinear Processes Geophys., 9, 341345.

    • Search Google Scholar
    • Export Citation
  • Van Atta, C. W., , and Wyngaard J. C. , 1975: On higher-order spectra of turbulence. J. Fluid Mech., 72, 673694.

  • Veltcheva, A. D., , and Soares C. G. , 2004: Identification of the components of wave spectra by the Hilbert–Huang transform method. Appl. Ocean Res., 26, 112.

    • Search Google Scholar
    • Export Citation
  • Wood, R., , Stromberg I. M. , , Jonas P. R. , , and Mill C. S. , 1997: Analysis of an air motion system on a light aircraft for boundary layer research. J. Atmos. Oceanic Technol., 14, 960968.

    • Search Google Scholar
    • Export Citation
  • Wyngaard, J. C., , and Coté O. R. , 1972: Cospectral similarity in the atmospheric surface layer. Quart. J. Roy. Meteor. Soc., 98, 590603.

    • Search Google Scholar
    • Export Citation
  • Wyngaard, J. C., , Pennell W. T. , , Lenschow D. H. , , and LeMone M. A. , 1978: The temperature–humidity covariance budget in the convective boundary layer. J. Atmos. Sci., 35, 4758.

    • Search Google Scholar
    • Export Citation
  • Zhaoyang, J., , Qiang Y. , , Shuhe L. , , Tianduo W. , , Xiaomin S. , , and Renhua Z. , 2006: Comparative analysis of temperature and CO2 fluxes for winter wheat in Tibetan Plain and North China Plain using the EMD method. Prog. Nat. Sci., 16, 10661071.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 37 37 2
PDF Downloads 28 28 2

Processing Turbulence Data Collected on board the Helicopter Observation Platform (HOP) with the Empirical Mode Decomposition (EMD) Method

View More View Less
  • 1 Department of Civil and Environmental Engineering, Edmund T. Pratt School of Engineering, Duke University, Durham, North Carolina
© Get Permissions
Restricted access

Abstract

The Duke University Helicopter Observation Platform (HOP) has previously been shown to be a useful instrument for the measurement of turbulent atmospheric fluxes. As with all such measurements, especially those made from moving platforms, spurious signals, such as instrument noise and mesoscale atmospheric motions, are superposed on the desired signal. Empirical mode decomposition (EMD) is applied in a novel way to identify and separate out different signals represented by intrinsic mode functions (IMFs) in the HOP data and is shown to be an effective tool for the task. The method produces a basis that is adaptive, unique, and orthogonal, all of which are required for this type of data processing, and none of which are present in more traditional techniques. The results of applying EMD are shown to be nonlinear, and occasionally the removal of the correct number of IMFs increases the observed value of energy and fluxes calculated with the eddy correlation technique.

Corresponding author address: Roni Avissar, Rosenstiel School of Marine and Atmospheric Science, University of Miami, 4600 Rickenbacker Cswy., Miami, FL 33149-1031. E-mail: avissar@miami.edu

Abstract

The Duke University Helicopter Observation Platform (HOP) has previously been shown to be a useful instrument for the measurement of turbulent atmospheric fluxes. As with all such measurements, especially those made from moving platforms, spurious signals, such as instrument noise and mesoscale atmospheric motions, are superposed on the desired signal. Empirical mode decomposition (EMD) is applied in a novel way to identify and separate out different signals represented by intrinsic mode functions (IMFs) in the HOP data and is shown to be an effective tool for the task. The method produces a basis that is adaptive, unique, and orthogonal, all of which are required for this type of data processing, and none of which are present in more traditional techniques. The results of applying EMD are shown to be nonlinear, and occasionally the removal of the correct number of IMFs increases the observed value of energy and fluxes calculated with the eddy correlation technique.

Corresponding author address: Roni Avissar, Rosenstiel School of Marine and Atmospheric Science, University of Miami, 4600 Rickenbacker Cswy., Miami, FL 33149-1031. E-mail: avissar@miami.edu
Save