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Corrections and Data Quality Control

  • Thomas Foken
  • Ray Leuning
  • Steven R. Oncley
  • Matthias Mauder
  • Marc Aubinet
Chapter
Part of the Springer Atmospheric Sciences book series (SPRINGERATMO)

Abstract

This chapter describes corrections that must be applied to measurements because practical instrumentation cannot fully meet the requirements of the underlying micrometeorological theory. Typically, measurements are made in a finite sampling volume rather than at a single point, and the maximum frequency response of the sensors is less than the highest frequencies of the turbulent eddies responsible for the heat and mass transport. Both of these cause a loss of the high-frequency component of the covariances used to calculate fluxes. Errors also arise in calculating fluxes of trace gas quantities using open-path analyzers because of spurious density fluctuations arising from the fluxes of heat and water vapor. This chapter gives the reader an overview of how these sources of error can be eliminated or reduced using some model assumptions and additional measurements. Corrections needed for some specific instruments are presented (Sect. 4.1), followed by a discussion of the generally observed lack of closure of the energy balance using the sum of latent and sensible heat fluxes (Sect. 4.2). The chapter closes with a discussion of measures needed to determine the quality of the final calculated fluxes (Sect. 4.3)

Keywords

Latent Heat Flux Turbulent Flux Eddy Covariance Sonic Anemometer Buoyancy Flux 
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.

Notes

Acknowledgments

MA acknowledges financial support by the European Union (FP 5, 6, and 7), the Belgian Fonds de la recherche Scientifique (FNRS-FRS), the Belgian Federal Science Policy Office (BELSPO), and the Communauté française de Belgique (Action de Recherche Concertée).

References

  1. Amiro BD (1990) Comparison of turbulence statistics within three boreal forest canopies. Bound Layer Meteorol 51:99–121Google Scholar
  2. Anderson DE, Verma SB, Clement RJ, Baldocchi DD, Matt DR (1986) Turbulence spectra of CO2, water vapour, temperature and velocity over a deciduous forest. Agric For Meteorol 38:81–99Google Scholar
  3. Arya SP (2001) Introduction to micrometeorology. Academic, San Diego, 415 ppGoogle Scholar
  4. Aubinet M et al (2000) Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology. Adv Ecol Res 30:113–175Google Scholar
  5. Aubinet M, Chermanne B, Vandenhaute M, Longdoz B, Yernaux M, Laitat E (2001) Long term carbon dioxide exchange above a mixed forest in the Belgian Ardennes. Agric For Meteorol 108:293–315Google Scholar
  6. Bernhardt K, Piazena H (1988) Zum Einfluß turbulenzbedingter Dichteschwankungen auf die Bestimmung turbulenter Austauschströme in der Bodenschicht. Z Meteorol 38:234–245Google Scholar
  7. Beyrich F, Mengelkamp H-T (2006) Evaporation over a heterogeneous land surface: EVA_GRIPS and the LITFASS-2003 experiment – an overview. Bound Layer Meteorol 121:5–32Google Scholar
  8. Beyrich F, Herzog H-J, Neisser J (2002) The LITFASS project of DWD and the LITFASS-98 experiment: the project strategy and the experimental setup. Theor Appl Climatol 73:3–18Google Scholar
  9. Brook RR (1978) The influence of water vapor fluctuations on turbulent fluxes. Bound Layer Meteorol 15:481–487Google Scholar
  10. Burba G, Anderson D (2010) A brief practical guide to eddy covariance flux measurements. Li-COR Inc., LincolnGoogle Scholar
  11. Burba G, McDermitt DK, Grelle A, Anderson DJ, Xu L (2008) Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers. Glob Chang Biol 14:1854–1876Google Scholar
  12. Cava D, Contini D, Donateo A, Martano P (2008) Analysis of short-term closure of the surface energy balance above short vegetation. Agric For Meteorol 148:82–93Google Scholar
  13. Clement RJ, Burba GG, Grelle A, Anderson DJ, Moncrieff JB (2009) Improved trace gas flux estimation through IRGA sampling optimization. Agric For Meteorol 149:623–638Google Scholar
  14. Culf AD, Foken T, Gash JHC (2004) The energy balance closure problem. In: Kabat P et al (eds) Vegetation, water, humans and the climate. A new perspective on an interactive system. Springer, Berlin/Heidelberg, pp 159–166Google Scholar
  15. de Ligne A, Heinesch B, Aubinet M (2010) New transfer functions for correcting turbulent water vapour fluxes. Bound Layer Meteorol 137(2):205–221Google Scholar
  16. DeGaetano AT (1997) A quality-control routine for hourly wind observations. J Atmos Ocean Technol 14:308–317Google Scholar
  17. Desjardins RL (1985) Carbon dioxide budget of maize. Agric For Meteorol 36:29–41Google Scholar
  18. Desjardins RL, MacPherson JI, Schuepp PH, Karanja F (1989) An evaluation of aircraft flux measurements of CO2, water vapor and sensible heat. Bound Layer Meteorol 47:55–69Google Scholar
  19. Dyer AJ (1981) Flow distortion by supporting structures. Bound Layer Meteorol 20:363–372Google Scholar
  20. Essenwanger OM (1969) Analytical procedures for the quality control of meteorological data. In: Proceedings of the American meteorological society symposium on meteorological observations and instrumentation. Meteorol Monogr 11(33):141–147Google Scholar
  21. Eugster W, Senn W (1995) A cospectral correction for measurement of turbulent NO2 flux. Bound Layer Meteorol 74:321–340Google Scholar
  22. Finkelstein PL, Sims PF (2001) Sampling error in eddy correlation flux measurements. J Geophys Res D106:3503–3509Google Scholar
  23. Finnigan JJ, Clement R, Malhi Y, Leuning R, Cleugh HA (2003) A re-evaluation of long-term flux measurement techniques, part I: averaging and coordinate rotation. Bound Layer Meteorol 107:1–48Google Scholar
  24. Foken T (2008a) The energy balance closure problem - an overview. Ecol Appl 18:1351–1367Google Scholar
  25. Foken T (2008b) Micrometeorology. Springer, Berlin/Heidelberg, 308 ppGoogle Scholar
  26. Foken T, Oncley SP (1995) Results of the workshop ‘Instrumental and methodical problems of land surface flux measurements’. Bull Am Meteorol Soc 76:1191–1193Google Scholar
  27. Foken T, Wichura B (1996) Tools for quality assessment of surface-based flux measurements. Agric For Meteorol 78:83–105Google Scholar
  28. Foken T, Skeib G, Richter SH (1991) Dependence of the integral turbulence characteristics on the stability of stratification and their use for Doppler-Sodar measurements. Z Meteorol 41:311–315Google Scholar
  29. Foken T, Dlugi R, Kramm G (1995) On the determination of dry deposition and emission of gaseous compounds at the biosphere-atmosphere interface. Meteorol Z 4:91–118Google Scholar
  30. Foken T et al~(1997) Results of the LINEX-96/2 experiment, vol 48, Dt Wetterdienst, Forsch. Entwicklung, Arbeitsergebnisse. DWD, Geschäftsbereich Forschung und Entwicklung, Offenbach am Main, 75 ppGoogle Scholar
  31. Foken T, Göckede M, Mauder M, Mahrt L, Amiro BD, Munger JW (2004) Post-field data quality control. In: Lee X et al (eds) Handbook of micrometeorology: a guide for surface flux measurement and analysis. Kluwer, Dordrecht, pp 181–208Google Scholar
  32. Foken T, Wimmer F, Mauder M, Thomas C, Liebethal C (2006) Some aspects of the energy balance closure problem. Atmos Chem Phys 6:4395–4402Google Scholar
  33. Foken T et al (2010) Energy balance closure for the LITFASS-2003 experiment. Theor Appl Climatol 101:149–160Google Scholar
  34. Foken T,~Aubinet M, Finnigan J, Leclerc MY, Mauder M, Paw UKT (2011) Results of a panel discussion about the energy balance closure correction for trace gases. Bull Am Meteorol Soc 92:ES13–ES18. doi: 10.1175/2011BAMS3130.1171 Google Scholar
  35. Friedrich K, Mölders N, Tetzlaff G (2000) On the influence of surface heterogeneity on the Bowen-ratio: a theoretical case study. Theor Appl Climatol 65:181–196Google Scholar
  36. Fuehrer PL, Friehe CA (2002) Flux correction revised. Bound Layer Meteorol 102:415–457Google Scholar
  37. Garratt JR (1990) The internal boundary layer - a review. Bound Layer Meteorol 50:171–203Google Scholar
  38. Göckede M et al (2008) Quality control of CarboEurope flux data – part 1: coupling footprint analyses with flux data quality assessment to evaluate sites in forest ecosystems. Biogeosciences 5:433–450Google Scholar
  39. Grelle A, Burba G (2007) Fine-wire thermometer to correct CO2 fluxes by open-path analyzers for artificial density fluctuations. Agric For Meteorol 147:48–57Google Scholar
  40. Gurjanov AE, Zubkovskij SL, Fedorov MM (1984) Mnogokanalnaja avtomatizirovannaja sistema obrabotki signalov na baze EVM (Automatic multi-channel system for signal analysis with electronic data processing). Geod Geophys Veröff, R II 26:17–20Google Scholar
  41. Gurvitch AS (1962) Spectry pulsacii vertikalnoj komponenty skorosti vetra i ich svjazi s mikrometeorologitcheskimi uslovijach (Spectra of the fluctuations of the vertical wind component and the connection to micrometeorological conditions). Atmos Turbulent – Trudy inst fiziki atmos AN SSSR 4:101–136Google Scholar
  42. Hatfield JL, Baker JM (eds) (2005) Micrometeorology in agricultural systems. American Society of Agronomy, Madison, 584 ppGoogle Scholar
  43. Haugen DA (1978) Effects of sampling rates and averaging periods on meteorological measurements. In: Fourth symposium meteorological observations and Instrumentation, Am Meteorol Soc, pp 15–18Google Scholar
  44. Heusinkveld BG, Jacobs AFG, Holtslag AAM, Berkowicz SM (2004) Surface energy balance closure in an arid region: role of soil heat flux. Agric For Meteorol 122:21–37Google Scholar
  45. Hiller R, Zeeman MJ, Eugster W (2008) Eddy-covariance flux measurements in the complex terrain of an Alpine valley in Switzerland. Bound Layer Meteorol 127:449–467Google Scholar
  46. Högström U, Smedman A (2004) Accuracy of sonic anemometers: laminar wind-tunnel calibrations compared to atmospheric in situ calibrations against a reference instrument. Bound Layer Meteorol 111:33–54Google Scholar
  47. Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Bound Layer Meteorol 21:341–356Google Scholar
  48. Højstrup J (1993) A statistical data screening procedure. Meas Sci Technol 4:153–157Google Scholar
  49. Horst TW (1973) Spectral transfer functions for a three component sonic-anemometer. J Appl Meteorol 12:1072–1075Google Scholar
  50. Horst TW (1997) A simple formula for attenuation of eddy fluxes measured with first-order-response scalar sensors. Bound Layer Meteorol 82:219–233Google Scholar
  51. Horst TW (2000) On frequency response corrections for eddy covariance flux measurements. Bound Layer Meteorol 94:517–520Google Scholar
  52. Horst TW, Lenschow DH (2009) Attenuation of scalar fluxes measured with spatially-displaced sensors. Bound Layer Meteorol 130:275–300Google Scholar
  53. Hyson P, Garratt JR, Francey RJ (1977) Algebraic und elektronic corrections of measured uw covariance in the lower atmosphere. Bound Layer Meteorol 16:43–47Google Scholar
  54. Ibrom A, Dellwik E, Flyvbjerg H, Jensen NO, Pilegaard K (2007a) Strong low-pass filtering effects on water vapour flux measurements with closed-path eddy correlation systems. Agric For Meteorol 147:140–156Google Scholar
  55. Ibrom A, Dellwik E, Larsen SE, Pilegaard K (2007b) On the use of the Webb–Pearman–Leuning theory for closed-path eddy correlation measurements. Tellus B 59:937–946Google Scholar
  56. Inagaki A, Letzel MO, Raasch S, Kanda M (2006) Impact of surface heterogeneity on energy balance: a study using LES. J Meteorol Soc Jpn 84:187–198Google Scholar
  57. Ingwersen J et al (2011) Comparison of Noah simulations with eddy covariance and soil water measurements at a winter wheat stand. Agric For Meteorol 151:345–355Google Scholar
  58. Järvi L, Mammarella I, Eugster W, Ibrom A, Siivola E, Dellwik E, Keronen P, Burba G, Vesala T (2009) Comparison of net CO2 fluxes measured with open- and closed-path infrared gas analyzers in urban complex environment. Boreal Environ Res 14:499–514Google Scholar
  59. Johansson C, Smedman A, Högström U, Brasseur JG, Khanna S (2001) Critical test of Monin-Obukhov similarity during convective conditions. J Atmos Sci 58:1549–1566Google Scholar
  60. Kaimal JC, Finnigan JJ (1994) Atmospheric boundary layer flows: their structure and measurement. Oxford University Press, New York, 289 ppGoogle Scholar
  61. Kaimal JC, Gaynor JE (1991) Another look to sonic thermometry. Bound Layer Meteorol 56:401–410Google Scholar
  62. Kaimal JC, Wyngaard JC, Haugen DH (1968) Deriving power spectra from a three component sonic anemometer. J Appl Meteorol 7:827–834Google Scholar
  63. Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface layer turbulence. Q J R Meteorol Soc 98:563–589Google Scholar
  64. Kanda M, Inagaki A, Letzel MO, Raasch S, Watanabe T (2004) LES study of the energy imbalance problem with eddy covariance fluxes. Bound Layer Meteorol 110:381–404Google Scholar
  65. Klaassen W, van Breugel PB, Moors EJ, Nieveen JP (2002) Increased heat fluxes near a forest edge. Theor Appl Climatol 72:231–243Google Scholar
  66. Kljun N, Calanca P, Rotach M, Schmid HP (2004) A simple parameterization for flux footprint predictions. Bound Layer Meteorol 112:503–523Google Scholar
  67. Kondo F, Tsukamoto O (2008) Evaluation of Webb correction on CO2 flux by eddy covariance technique using open-path gas analyzer over asphalt. J Agric Meteorol 64:1–8Google Scholar
  68. Kristensen L, Mann J, Oncley SP, Wyngaard JC (1997) How close is close enough when measuring scalar fluxes with displaced sensors. J Atmos Ocean Technol 14:814–821Google Scholar
  69. Lee X, Black TA (1994) Relating eddy correlation sensible heat flux to horizontal sensor separation in the unstable atmospheric surface layer. J Geophys Res 99(D9):18545–18553Google Scholar
  70. Lenschow DH, Raupach MR (1991) The attenuation of fluctuations in scalar concentrations through sampling tubes. J Geophys Res 96:5259–5268Google Scholar
  71. Leuning R (2004) Measurements of trace gas fluxes in the atmosphere using eddy covariance: WPL corrections revisited. In: Lee X et al (eds) Handbook of micrometeorology: a guide for surface flux measurements and analysis. Kluwer, Dordrecht, pp 119–132Google Scholar
  72. Leuning R (2007) The correct form of the Webb, Pearman and Leuning equation for eddy fluxes of trace gases in steady and non-steady state, horizontally homogeneous flows. Bound Layer Meteorol 123:263–267Google Scholar
  73. Leuning R, Judd MJ (1996) The relative merits of open- and closed path analysers for measurements of eddy fluxes. Glob Chang Biol 2:241–254Google Scholar
  74. Leuning R, King KM (1992) Comparison of eddy-covariance measurements of CO2 fluxes by open- and closed-path CO2 analysers. Bound Layer Meteorol 59:297–311Google Scholar
  75. Leuning R, Legg BJ (1982) Comments on ‘The influence of water vapor fluctuations on turbulent fluxes’ by Brook. Bound Layer Meteorol 23:255–258Google Scholar
  76. Leuning RL, Moncrieff JB (1990) Eddy covariance CO2 flux measurements using open and closed path CO2 analysers: correction for analyser water vapour sensitivity and damping of fluctuations in air sampling tubes. Bound Layer Meteorol 53:63–76Google Scholar
  77. Liebethal C (2006) On the determination of the ground heat flux in micrometeorology and its influence on the energy balance closure. PhD thesis, University of BayreuthGoogle Scholar
  78. Liebethal C, Foken T (2003) On the significance of the Webb correction to fluxes. Bound Layer Meteorol 109:99–106Google Scholar
  79. Liebethal C, Foken T (2004) On the significance of the Webb correction to fluxes, Corrigendum. Bound Layer Meteorol 113:301Google Scholar
  80. Liu H (2005) An alternative approach for CO2 flux correction caused by heat and water vapour transfer. Bound Layer Meteorol 115:151–168Google Scholar
  81. Liu H, Peters G, Foken T (2001) New equations for sonic temperature variance and buoyancy heat flux with an omnidirectional sonic anemometer. Bound Layer Meteorol 100:459–468Google Scholar
  82. Liu H, Randerson JT, Lindfors J, Massman WJ, Foken T (2006) Consequences of incomplete surface energy balance closure for CO2 fluxes from open-path CO2/H2O infrared gas analyzers. Bound Layer Meteorol 120:65–85Google Scholar
  83. Loescher HW et al (2005) Comparison of temperature and wind statistics in contrasting environments among different sonic anemometer–thermometers. Agric For Meteorol 133: 119–139Google Scholar
  84. Mahrt L (1991) Eddy asymmetry in the sheared heated boundary layer. J Atmos Sci 48: 472–492Google Scholar
  85. Mahrt L (1998) Flux sampling errors for aircraft and towers. J Atmos Ocean Technol 15: 416–429Google Scholar
  86. Mammarella I, Launiainen S, Grönholm T, Keronen P, Pumpanen J, Rannik Ü, Vesala T (2009) Relative humidity effect on the high frequency attenuation of water vapour flux measured by a closed-path eddy covariance system. J Atmos Ocean Technol A26:1856–1866Google Scholar
  87. Massman WJ (2000) A simple method for estimating frequency response corrections for eddy covariance systems. Agric For Meteorol 104:185–198Google Scholar
  88. Massman WJ, Ibrom A (2008) Attenuation of concentration fluctuations of water vapor and other trace gases in turbulent tube flow. Atmos Chem Phys 8:6245–6259Google Scholar
  89. Mauder M, Foken T (2004) Documentation and instruction manual of the eddy covariance software package TK2, vol 26, Arbeitsergebn, Univ Bayreuth, Abt Mikrometeorol. Univ., Abt. Mikrometeorologie, Bayreuth, 42 pp. ISBN 1614–8916Google Scholar
  90. Mauder M, Foken T (2006) Impact of post-field data processing on eddy covariance flux estimates and energy balance closure. Meteorol Z 15:597–609Google Scholar
  91. Mauder M, Liebethal C, Göckede M, Leps J-P, Beyrich F, Foken T (2006) Processing and quality control of flux data during LITFASS-2003. Bound Layer Meteorol 121:67–88Google Scholar
  92. Mauder M, Jegede OO, Okogbue EC, Wimmer F, Foken T (2007a) Surface energy flux measurements at a tropical site in West-Africa during the transition from dry to wet season. Theor Appl Climatol 89:171–183Google Scholar
  93. Mauder M et al (2007b) The energy balance experiment EBEX-2000. Part II: Intercomparison of eddy covariance sensors and post-field data processing methods. Bound Layer Meteorol 123:29–54Google Scholar
  94. Mauder M, Foken T, Clement R, Elbers J, Eugster W, Grünwald T, Heusinkveld B, Kolle O (2008) Quality control of CarboEurope flux data - part 2: inter-comparison of eddy-covariance software. Biogeosciences 5:451–462Google Scholar
  95. Meijninger WML, Lüdi A, Beyrich F, Kohsiek W, DeBruin HAR (2006) Scintillometer-based turbulent surface fluxes of sensible and latent heat over heterogeneous a land surface - a contribution to LITFASS-2003. Bound Layer Meteorol 121:89–110Google Scholar
  96. Mengelkamp H-T et al (2006) Evaporation over a heterogeneous land surface: the EVA_GRIPS project. Bull Am Meteorol Soc 87:775–786Google Scholar
  97. Meyers TP, Hollinger SE (2004) An assessment of storage terms in the surface energy of maize and soybean. Agric For Meteorol 125:105–115Google Scholar
  98. Moncrieff J (2004) Surface turbulent fluxes. In: Kabat P et al (eds) Vegetation, water, humans and the climate. A new perspective on an interactive system. Springer, Berlin/Heidelberg, pp 173–182Google Scholar
  99. Moncrieff JB et al (1997) A system to measure surface fluxes of momentum, sensible heat, water vapor and carbon dioxide. J Hydrol 188–189:589–611Google Scholar
  100. Monji N, Inoue M, Hamotani K (1994) Comparison of eddy heat fluxes between inside and above a coniferous forest. J Agric Meteorol 50:23–31Google Scholar
  101. Monteith JL, Unsworth MH (2008) Principles of environmental physics, 3rd edn. Elsevier/Academic Press, Amsterdam/Boston, 418 ppGoogle Scholar
  102. Moore CJ (1986) Frequency response corrections for eddy correlation systems. Bound Layer Meteorol 37:17–35Google Scholar
  103. Nakai T, van der Molen MK, Gash JHC, Kodama Y (2006) Correction of sonic anemometer angle of attack errors. Agric For Meteorol 136:19–30Google Scholar
  104. Nicholls S, Smith FB (1982) On the definition of the flux of sensible heat. Bound Layer Meteorol 24:121–127Google Scholar
  105. Obukhov AM (1960) O strukture temperaturnogo polja i polja skorostej v uslovijach konvekcii (Structure of the temperature and velocity fields under conditions of free convection). Izv AN SSSR, ser Geofiz 1392–1396Google Scholar
  106. Oncley SP, Businger JA, Itsweire EC, Friehe CA, LaRue JC, Chang SS (1990) Surface layer profiles and turbulence measurements over uniform land under near-neutral conditions. In: 9th symposium on boundary layer and turbulence, Roskilde, Denmark, April 30–May 3, 1990, Am Meteorol Soc City pp 237–240Google Scholar
  107. Oncley SP et al (2007) The energy balance experiment EBEX-2000, part I: overview and energy balance. Bound Layer Meteorol 123:1–28Google Scholar
  108. Othaki E (1985) On the similarity in atmospheric fluctuations of atmospheric carbon dioxide, water vapour and temperature over vegetated fields. Bound Layer Meteorol 32:25–37Google Scholar
  109. Panin GN, Tetzlaff G, Raabe A (1998) Inhomogeneity of the land surface and problems in the parameterization of surface fluxes in natural conditions. Theor Appl Climatol 60:163–178Google Scholar
  110. Panofsky HA, Dutton JA (1984) Atmospheric turbulence – models and methods for engineering applications. Wiley, New York, 397 ppGoogle Scholar
  111. Raabe A (1991) Die Höhe der internen Grenzschicht. Z Meteorol 41:251–261Google Scholar
  112. Richardson AD et al (2006) A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes. Agric For Meteorol 136:1–18Google Scholar
  113. Ruppert J, Thomas C, Foken T (2006) Scalar similarity for relaxed eddy accumulation methods. Bound Layer Meteorol 120:39–63Google Scholar
  114. Sakai R, Fitzjarrald D, Moore KE (2001) Importance of low-frequency contributions to eddy fluxes observed over rough surfaces. J Appl Meteorol 40:2178–2192Google Scholar
  115. Schmid HP, Bünzli D (1995a) The influence of the surface texture on the effective roughness length. Q J R Meteorol Soc 121:1–21Google Scholar
  116. Schmid HP, Bünzli D (1995b) Reply to comments by E. M. Blyth on ‘The influence of surface texture on the effective roughness length’. Q J R Meteorol Soc 121:1173–1176Google Scholar
  117. Schotanus P, Nieuwstadt FTM, DeBruin HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluctuations. Bound Layer Meteorol 26:81–93Google Scholar
  118. Schuepp PH, Leclerc MY, MacPherson JI, Desjardins RL (1990) Footprint prediction of scalar fluxes from analytical solutions of the diffusion equation. Bound Layer Meteorol 50:355–373Google Scholar
  119. Shearman RJ (1992) Quality assurance in the observation area of the Meteorological Office. Meteorol Mag 121:212–216Google Scholar
  120. Silverman BA (1968) The effect of the spectral averaging on spectral estimation. J Appl Meteorol 7:168–172Google Scholar
  121. Smith SR, Camp JP, Legler DM (1996) Handbook of quality control, procedures and methods for surface meteorology data. Center for Ocean Atmospheric Prediction Studies,TOGA/COARE, Technical Report. 96–3:60 pp. [Available from Florida State University, Tallahassee, FL, 32306–33041]Google Scholar
  122. Steinfeld G, Letzel MO, Raasch S, Kanda M, Inagaki A (2007) Spatial representativeness of single tower measurements and the imbalance problem with eddy-covariance fluxes: results of a large-eddy simulation study. Bound Layer Meteorol 123:77–98Google Scholar
  123. Stull RB (1988) An introduction to boundary layer meteorology. Kluwer Acad. Publ, Dordrecht/Boston/London, 666 ppGoogle Scholar
  124. Su HB, Schmid HP, Grimmond CSB, Vogel CS, Oliphant AJ (2004) Spectral characteristics and correction of long-term eddy-covariance measurements over two mixed hardwood forests in non-flat terrain. Bound Layer Meteorol 110:213–253Google Scholar
  125. Tanner CB, Thurtell GW (1969) Anemoclinometer measurements of Reynolds stress and heat transport in the atmospheric surface layer. ECOM, United States Army Electronics Command, Research and Development, University of Wisconsin, MadisonGoogle Scholar
  126. Tanner BD, Swiatek E, Greene JP (1993) Density fluctuations and use of the krypton hygrometer in surface flux measurements. In: Allen RG (ed) Management of irrigation and drainage systems: integrated perspectives. American Society of Civil Engineers, New York, pp 945–952Google Scholar
  127. Thomas C, Foken T (2002) Re-evaluation of integral turbulence characteristics and their parameterisations. In: 15th conference on turbulence and boundary layers, Wageningen, NL, 15–19 July 2002, Am Meteorol Soc, City, pp 129–132Google Scholar
  128. Thomas C, Foken T (2007) Flux contribution of coherent structures and its implications for the exchange of energy and matter in a tall spruce canopy. Bound Layer Meteorol 123:317–337Google Scholar
  129. Twine TE, Kustas WP, Norman JM, Cook DR, Houser PR, Meyers TP, Prueger JH, Starks PJ, Wesely ML (2000) Correcting eddy-covariance flux underestimates over a grassland. Agric For Meteorol 103:279–300Google Scholar
  130. van der Molen MK, Gash JHC, Elbers JA (2004) Sonic anemometer (co)sine response and flux measurement: II the effect of introducing an angle of attack dependent calibration. Agric For Meteorol 122:95–109Google Scholar
  131. van Dijk A (2002) Extension to 3D of “The effect of line averaging on scalar flux measurements with a sonic anemometer near the surface” by Kristensen and Fitzjarrald. J Atmos Ocean Technol 19:80–82Google Scholar
  132. van Dijk A, Kohsiek W, DeBruin HAR (2003) Oxygen sensitivity of krypton and Lyman-alpha hygrometers. J Atmos Ocean Technol 20:143–151Google Scholar
  133. van Dijk A, Kohsiek W, DeBruin HAR (2004) The principles of surface flux physics: theory, practice and description of the ECPACK library. University of Wageningen, WageningenGoogle Scholar
  134. VDI (2011) Umweltmeteorologie – Meteorologische Messungen - Grundlagen (Environmental meteorology – Meteorological measurements - Basics). Beuth-Verlag, Berlin, VDI 3786, Blatt 3781, in print ppGoogle Scholar
  135. Vickers D, Mahrt L (1997) Quality control and flux sampling problems for tower and aircraft data. J Atmos Ocean Technol 14:512–526Google Scholar
  136. Webb EK (1982) On the correction of flux measurements for effects of heat and water vapour transfer. Bound Layer Meteorol 23:251–254Google Scholar
  137. Webb EK, Pearman GI, Leuning R (1980) Correction of the flux measurements for density effects due to heat and water vapour transfer. Q J R Meteorol Soc 106:85–100Google Scholar
  138. Werle P, D’Amato F, Viciani S (2008) Tunable diode-laser spectroscopy: principles, performance, perspectives. In: Lackner M (ed) Lasers in chemistry – probing matter. Wiley-VCH, Weinheim, pp 255–275Google Scholar
  139. Wilczak JM, Oncley SP, Stage SA (2001) Sonic anemometer tilt correction algorithms. Bound Layer Meteorol 99:127–150Google Scholar
  140. Wilson KB et al (2002) Energy balance closure at FLUXNET sites. Agric For Meteorol 113:223–234Google Scholar
  141. Wyngaard JC (1981) The effects of probe-induced flow distortion on atmospheric turbulence measurements. J Appl Meteorol 20:784–794Google Scholar
  142. Wyngaard JC, Coté OR (1971) The budgets of turbulent kinetic energy and temperature variance in the atmospheric surface layer. J Atmos Sci 28:190–201Google Scholar
  143. Wyngaard JC, Coté OR, Izumi Y (1971) Local free convection, similarity and the budgets of shear stress and heat flux. J Atmos Sci 28:1171–1182Google Scholar
  144. Zhang G, Thomas C, Leclerc MY, Karipot A, Gholz HL, Foken T (2007) On the effect of clearcuts on turbulence structure above a forest canopy. Theor Appl Climatol 88:133–137Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Thomas Foken
    • 1
  • Ray Leuning
    • 2
  • Steven R. Oncley
    • 3
  • Matthias Mauder
    • 4
  • Marc Aubinet
    • 5
  1. 1.Department of MicrometeorologyUniversity of BayreuthBayreuthGermany
  2. 2.Marine and Atmospheric Research, CSIROCanberraAustralia
  3. 3.Earth Observing Laboratory, NCARBoulderUSA
  4. 4.Institute for Meteorology and Climate Research, Atmospheric Environmental ResearchKarlsruhe Institute of TechnologyGarmisch-PartenkirchenGermany
  5. 5.Unit of Biosystem Physics, Gembloux Agro-Bio TechUniversity of LiegeGemblouxBelgium

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