Articles | Volume 10, issue 11
https://doi.org/10.5194/gmd-10-4187-2017
https://doi.org/10.5194/gmd-10-4187-2017
Development and technical paper
 | 
17 Nov 2017
Development and technical paper |  | 17 Nov 2017

Numerical framework for the computation of urban flux footprints employing large-eddy simulation and Lagrangian stochastic modeling

Mikko Auvinen, Leena Järvi, Antti Hellsten, Üllar Rannik, and Timo Vesala

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Cited articles

Anderson, W.: Amplitude modulation of streamwise velocity fluctuations in the roughness sublayer: Evidence from large-eddy simulations, J. Fluid Mech., 789, 567–588, https://doi.org/10.1017/jfm.2015.744, 2016.
Aubinet, M., Vesala, T., and Papale, D. (Eds.): Eddy covariance. A Practical Guide to Measurement and Data Analysis, Springer, 2012.
Christen, A., Coops, N., Crawford, B., Kellett, R., Liss, K., Olchovski, I., Tooke, T., van der Laan, M., and Voogt, J.: Validation of modeled carbon-dioxide emissions from an urban neighborhood with direct eddy-covariance measurements, Atmos. Environ., 45, 6057–6069, 2011.
Deardorff, J.: Stratoculumus-capped mixed layers derived from a three-dimensional model, Bound-Lay. Meteorol., 18, 495–527, 1980.
Giometto, M., Christen, A., Meneveau, C., Fang, J., Krafczyk, M., and Parlange, M.: Spatial Characteristics of Roughness Sublayer Mean Flow and Turbulence Over a Realistic Urban Surface, Bound.-Lay. Meteorol., 160, 425–452, https://doi.org/10.1007/s10546-016-0157-6, 2016.
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Short summary
Correct spatial interpretation of a micrometeorological measurement requires the determination of its effective source area, or footprint. In urban areas the use of analytical models becomes highly questionable. This work introduces a computational methodology that enables the generation of footprints for complex urban sites. The methodology is based on conducting high-resolution flow and particle analysis on a model that features a detailed topographic description of a real city environment.
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