Model Validation



A rigorous validation of footprint models is an issue of importance that cannot be overstated. This is so to make sure that their practical applications can be successful. Due to the relative limited amount of robust flux footprint tracer experimental data, emerging models are often compared with other models.


Sulfur Hexafluoride Lagrangian Model Natural Tracer Footprint Model Tracer 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.


  1. Finn D, Lamb B, Leclerc MY, Horst TW (1996) Experimental evaluation of analytical and Lagrangian surface-layer flux footprint models. Boundary-Layer Meteorol 80:283–308Google Scholar
  2. 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
  3. Foken T, Leclerc MY (2004) Methods and limitations in validation of footprint models. Agric Forest Meteorol 127:223–234CrossRefGoogle Scholar
  4. Göckede M, Markkaken T, Mauder M, Arnold K, Leps JP, Foken T (2005) Validation of footprint models using natural tracer measurements from a field experiment. Agric Forest Meteorol 135:314–325CrossRefGoogle Scholar
  5. 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. Biogeosci 5:433–450CrossRefGoogle Scholar
  6. Horst TW, Weil JC (1992) Footprint estimation for scalar flux measurements in the atmospheric surface layer. Boundary-Layer Meteorol 59:279–296CrossRefGoogle Scholar
  7. Horst TW, Weil JC (1994) How far is far enough?: the fetch requirements for micrometeorological measurement of surface fluxes. J Atm Oceanic Techn 11:1018–1025CrossRefGoogle Scholar
  8. Kljun N, Rotach MW, Schmid HP (2002) A three-dimensional backward Lagrangian footprint model for a wide range of boundary layer stratification. Boundary-Layer Meteorol 103:205–226CrossRefGoogle Scholar
  9. Kljun N, Kormann R, Rotach M, Meixner FX (2003) Comparison of the Lagrangian footprint model LPDM-B with an analytical footprint model. Boundary-Layer Meteorol 106:349–355CrossRefGoogle Scholar
  10. Kljun N, Kastner-Klein P, Federovich E, Rotach MW (2004) Evaluation of Lagrangian footprint model using data from wind tunnel convective boundary layer. Agric Forest Meteorol 127:189–201CrossRefGoogle Scholar
  11. Kormann R, Meixner FX (2001) An analytical footprint model for non-neutral stratification. Boundary-Layer Meteorol 99:207–224CrossRefGoogle Scholar
  12. Kurbanmuradov O, Sabelfeld KK (2000) Lagrangian stochastic models for turbulent dispersion in atmospheric boundary layers. Boundary-Layer Meteorol 97:191–218CrossRefGoogle Scholar
  13. Leclerc MY, Thurtell GW (1990) Footprint prediction of scalar fluxes using a Markovian analysis. Boundary-Layer Meteorol 52:247–258CrossRefGoogle Scholar
  14. Leclerc MY, Shen S, Lamb B (1997) Observations and large-eddy simulation modeling of footprints in the lower convective boundary layer. J Geophys Res 102(D8):9323–9334CrossRefGoogle Scholar
  15. Leclerc MY, Karipot A, Prabha T, Allwine G, Lamb B, Gholz HL (2003a) Impact of non-local advection on flux footprints over a tall forest canopy: a tracer flux experiment (Special issue: Advances in micrometeorology: Tribute to G. W. Thurtell). Agric Forest Meteorol 115:19–30CrossRefGoogle Scholar
  16. Leclerc MY, Meskhidze N, Finn D (2003b) Comparison between measured tracer fluxes and footprint modeling predictions over a homogeneous canopy of intermediate roughness. Agric Forest Meteorol 117:145–158CrossRefGoogle Scholar
  17. Markkanen T, Steinfeld G, Kljun N, Raasch S, Foken T (2009) Comparison of conventional Lagrangian stochastic footprint models against LES driven footprint estimates. Atmos Chem Phys 9:5575–5586CrossRefGoogle Scholar
  18. Moeng CH, Sullivan P (1994) A comparison of shear- and buoyancy-driven planetary boundary layer flows. J Atmos Sci 51:999–1022CrossRefGoogle Scholar
  19. Raasch S, Schröter M (2001) PALM—A large-eddy simulation model performing on massively parallel computers. Meteorol Z 10:363–372CrossRefGoogle Scholar
  20. Rannik Ü, Aubinet M, Kurbanmuradov O, Sabelfeld KK, Markkanen T, Vesala T (2000) Footprint analysis for measurements over heterogeneous forest. Boundary-Layer Meteorol 97:137–166CrossRefGoogle Scholar
  21. Rannik Ü, Markkanen T, Raittila T, Hari P, Vesala T (2003) Turbulence statistics inside and above forest: Influence on footprint prediction. Boundary-Layer Meteorol 109:163–189CrossRefGoogle Scholar
  22. Rebmann C et al (2005) Quality analysis applied on eddy covariance measurements at complex forest sites using footprint modelling. Theor Appl Climat 80:121–141CrossRefGoogle Scholar
  23. Schmid HP (1994) Source areas for scalars and scalar fluxes. Boundary-Layer Meteorol 67:293–318CrossRefGoogle Scholar
  24. Schmid HP (1997) Experimental design for flux measurements: matching scales of observations and fluxes. Agric Forest Meteorol 87:179–200CrossRefGoogle Scholar
  25. Schmid HP (2002) Footprint modeling for vegetation atmosphere exchange studies: A review and perspective. Agric Forest Meteorol 113:159–184CrossRefGoogle Scholar
  26. Schuepp PH, Leclerc MY, MacPherson JI, Desjardins RL (1990) Footprint prediction of scalar fluxes from analytical solutions of the diffusion equation. Boundary-Layer Meteorol 50:355–373CrossRefGoogle Scholar
  27. Sogachev A, Menzhulin G, Heimann M, Lloyd J (2002) A simple three dimensional canopy-planetray boundary layer simulation model for scalar concentrations and fluxes. Tellus 54B:784–819CrossRefGoogle Scholar
  28. Sogachev A, Lloyd J (2004) Using a one-and-a-half order closure model of atmospheric boundary layer for surface flux footprint estimation. Boundary-Layer Meteorol 112:467–502CrossRefGoogle Scholar
  29. Sogachev A, Leclerc MJ, Karipot A, Zhang G, Vesala T (2005) Effect of clearcuts on footprints and flux measurements above a forest canopy. Agric Forest Meteorol 133:182–196CrossRefGoogle Scholar
  30. Steinfeld G, Raasch S, Markkanen T (2008) Footprints in homogeneously and heterogeneously driven boundary layers derived from a Langrangian stochastic particle model embedded into large-eddy simulation. Boundary-Layer Meteorol 129:225–248CrossRefGoogle Scholar
  31. Thomson DJ (1987) Criteria for the selection of stochastic models of particle trajectories in turbulent flows. J Fluid Mech 189:529–556CrossRefGoogle Scholar
  32. Wang W, Rotach M (2010) Flux Footprints Over an Undulating Surface. Boundary-Layer Meteorol 136:325–340CrossRefGoogle Scholar
  33. Zhang G, Leclerc MY, Karipot A (2010) Local flux-profile relationships of wind speed and temperature in a canopy layer in atmospheric stable conditions. Biogeosciences 7:3625–3636CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Laboratory for Environmental PhysicsUniversity of GeorgiaGriffinUSA
  2. 2.Abteilung MikrometeorologieUniversität BayreuthBayreuthGermany

Personalised recommendations