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Averaging, Detrending, and Filtering of Eddy Covariance Time Series

  • John Moncrieff
  • Robert Clement
  • John Finnigan
  • Tilden Meyers
Part of the Atmospheric and Oceanographic Sciences Library book series (ATSL, volume 29)

Abstract

Data from sensors in an eddy covariance system are routinely processed to remove trends and to produce fluctuations and means. Historically this has been seen to be a relatively straightforward task and the methods are well known. Such re-processing can result in the loss of real signal since the detrending and averaging methods act as high-pass filters. We review the main methods used to separate the active, turbulent transport that we treat as eddy flux from the slower, deterministic atmospheric motions and instrument drift. We discuss the advantages and disadvantages of various algorithms used in averaging, detrending and filtering and conclude that the best method is likely to be dependent on site conditions and data processing system in use. We recommend the use of the ogive to determine the optimal averaging period at any site. We illustrate outstanding issues with data from a number of FLUXNET sites.

Keywords

Planetary Boundary Layer Energy Balance Closur Eddy Covariance System Instrument Drift Tall Tower 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Aubinet, M., Grelle, A., Ibrom, A., Rannik, U., Moncrie, J., Foken, T., Kowalski, A. S., Martin, P. H., Berbigier, P., Bernhofer, Ch., Clement, R., Elbers, J., Granier, A., Grüunwald, T., Morgenstern, K., Pilegaard, K., Rebmann, C., Snijders, W., Valentini, R., Vesala, T.: 2000, ‘Estimates of the annual net carbon and water exchange of European forests: the EUROFLUX methodology’, Adv. Ecol. Res. 30, 113–175.Google Scholar
  2. Bendat, J. S., Piersol, A. G.: 1958, Measurement and Analysis of Random Data John Wiley and Sons, New York, 390 pp.Google Scholar
  3. Berger, B.W., Davis, K.J., Yi, C., Bakwin, P.S. Zhao, C.: 2001, ‘Long-term carbon dioxide fluxes from a very tall tower in a northern forest: Part I. Flux measurement methodology’, J. Atmos Oceanic Technol. 18, 529–542.CrossRefGoogle Scholar
  4. Clement, R., Moncrie, J. B., Jarvis, P. G.: 2003, ‘Net carbon productivity of sitka spruce forest in Scotland’, Scottish Forestry, 57, 5–10.Google Scholar
  5. Culf, A. D.: 2000, ‘Examples of the e ects of di erent averaging methods on carbon dioxide fluxes calculated using the eddy correlation method’, Hydrology & Earth System Sciences, 41, 93–198.Google Scholar
  6. Finnigan, J. J.: 2000, ‘Turbulence in plant canopies’, Ann. Rev. Fluid Mech., 32, 519–571.CrossRefGoogle Scholar
  7. Finnigan, J. J.: 2004, ‘A re-evaluation of long-term flux measurement techniques. 2: Coordinate systems’, Bound-Layer Meteorol., in press.Google Scholar
  8. Finnigan, J.J., Leuning, R.: 2000 ‘Longterm flux measurements — coordinate systems and averaging’, In: Proc. Int. Workshop Advanced Flux Network and Flux Evaluation 27–29 September 2000, Sapporo Japan, Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan.Google Scholar
  9. Finnigan, J.J., Clement, R., Malhi, Y., Leuning, R., Cleugh, H.: 2003, ‘A re-evaluation of long-term flux measurement techniques. 1: Averaging and coordinate rotation’, Bound.-Layer Meteorol., 107, 1–48.CrossRefGoogle Scholar
  10. Gash, J. H. C., Culf, A. D.: 1996, ‘Applying linear detrend to eddy correlation data in real time’, Bound.-Layer Meteorol., 79, 301–306.CrossRefGoogle Scholar
  11. Holloway, J. L.: 1958, ‘Smoothing and filtering of time series and space fields’, Adv. Geophys., 43, 51–388.Google Scholar
  12. Ifeachor, E. C., Jervis, B. W.: 1993, Digital Signal Processing: A Practical Approach Addison-Wesley, Harlow, 760 pp.Google Scholar
  13. Kaimal, J. C., Wyngaard, J. C., Izumi, Y., Cote, O. R.: 1972, ‘Spectral characteristics of surface layer turbulence’, Quart. J. R. Meteorol. Soc., 98, 563–589.CrossRefGoogle Scholar
  14. Kaimal, J. C., Finnigan, J.: 1994, Atmospheric Boundary Layer Flows: Their Structure and Measurement, Oxford University Press, Oxford.Google Scholar
  15. Kristensen, L.: 1998 Time Series Analysis: Dealing with Imperfect Data, Riso National Laboratory, Roskilde, Denmark, Riso-I-12289 (EN) pp 31.Google Scholar
  16. Kristensen, L., Fitzjarrald, D.: 1984, ‘The effect of line averaging on scalar flux measurement with a sonic anemometer near the surface’, J. Atmos. Oceanic Technol., 11, 38–146.Google Scholar
  17. Kristensen, L.: 1979, ‘On longitudinal spectral coherence’, Bound.-Layer Meteorol., 16, 145–153.Google Scholar
  18. Kristensen, L., Jensen, N. O.: 1979, ‘Lateral coherence in isotropic turbulence and in the natural wind’, Bound.-Layer Meteorol., 17, 353–373.CrossRefGoogle Scholar
  19. Kruijt, B., Malhi, Y., Lloyd, J., Miranda, A. C., Nobre, A. D., Pereira, M. G. P., Culf, A., Grace, J.: 2000, ‘Turbulence above and within two Amazon rainforest canopies’, Bound.-Layer Meteorol., 94, 297–331.CrossRefGoogle Scholar
  20. Lenschow, D.H., Mann, J., Christens, L.: 1994, ‘How long is long enough when measuring fluxes and other turbulence statistics?’, J. Atmos. Oceanic Technol., 11, 661–673.CrossRefGoogle Scholar
  21. Massman, W. J.: 1991, ‘The attenuation of concentration fluctuations in turbulent flow through a tube”, J. Gephys. Res., 96, 15269–15273.Google Scholar
  22. Massman, W. J., Fox, D. G., Zeller, K. F., Lukens, D.: 1990, Verifying Eddy Correlation Measurements of Dry Deposition: A Study of the Energy Balance Components of the Pawnee Grasslands, USDA Forest Service Research Paper RM-288, Rocky Mountain Forest and Range Experiment Station, Fort Collins, CO, 14 pp.Google Scholar
  23. McMillen, R. T.: 1988, ‘An eddy correlation technique with extended applicability to non simple terrain’, Bound.-Layer Meteorol., 43, 231–245.CrossRefGoogle Scholar
  24. Moncrie, J. B., Verma, S. B., Cook, D. R.: 1992, ‘Intercomparison of eddy correlation carbon dioxide sensors during FIFE 1989’, J. Geophys. Res., 97, 18725–18730.Google Scholar
  25. Moore, C. J.: 1986, ‘Frequency response corrections for eddy correlation systems’, Bound.-Layer Meteorol., 37, 17–35.CrossRefGoogle Scholar
  26. Pekour, M. S., Wesely, M. L., Martin, T. J., Cook, D. R.: 2002, ‘A study of block averaging versus recursive filters for computing scalar eddy covariances near the surface’, In: American Meteorol. Soc. 25th Conf. Agric. Forest Meteorol. Norfolk, VA, p144–45.Google Scholar
  27. Rannik, Ü., Vesala, T.: 1999, ‘Autoregressive filtering versus linear detrending in estimation of fluxes by the eddy covariance method’, Bound.-Layer Meteorol., 91, 259–280.CrossRefGoogle Scholar
  28. Sakai, R. K., Fitzjarrald, D. R., Moore, K. E.: 2001, ‘Importance of low-frequency contributions to eddy fluxes observed over rough surfaces’, J. Appl. Meteorol., 40, 2178–2192.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • John Moncrieff
  • Robert Clement
  • John Finnigan
  • Tilden Meyers

There are no affiliations available

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