Articles | Volume 15, issue 8
https://doi.org/10.5194/gmd-15-3315-2022
https://doi.org/10.5194/gmd-15-3315-2022
Model evaluation paper
 | 
22 Apr 2022
Model evaluation paper |  | 22 Apr 2022

On the application and grid-size sensitivity of the urban dispersion model CAIRDIO v2.0 under real city weather conditions

Michael Weger, Holger Baars, Henriette Gebauer, Maik Merkel, Alfred Wiedensohler, and Bernd Heinold

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Cited articles

Allegrini, J., Dorer, V., and Carmeliet, J.: Wind tunnel measurements of buoyant flows in street canyons, Build. Environ., 59, 315–326, https://doi.org/10.1016/j.buildenv.2012.08.029, 2013. a
Auguste, F., Lac, C., Masson, V., and Cariolle, D.: Large-eddy simulations with an immersed boundary method: pollutant dispersion over urban terrain, Atmosphere, 11, 113–135, https://doi.org/10.3390/atmos11010113, 2020. a
Baars, H., Ansmann, A., Engelmann, R., and Althausen, D.: Continuous monitoring of the boundary-layer top with lidar, Atmos. Chem. Phys., 8, 7281–7296, https://doi.org/10.5194/acp-8-7281-2008, 2008. a
Baars, H., Kanitz, T., Engelmann, R., Althausen, D., Heese, B., Komppula, M., Preißler, J., Tesche, M., Ansmann, A., Wandinger, U., Lim, J.-H., Ahn, J. Y., Stachlewska, I. S., Amiridis, V., Marinou, E., Seifert, P., Hofer, J., Skupin, A., Schneider, F., Bohlmann, S., Foth, A., Bley, S., Pfüller, A., Giannakaki, E., Lihavainen, H., Viisanen, Y., Hooda, R. K., Pereira, S. N., Bortoli, D., Wagner, F., Mattis, I., Janicka, L., Markowicz, K. M., Achtert, P., Artaxo, P., Pauliquevis, T., Souza, R. A. F., Sharma, V. P., van Zyl, P. G., Beukes, J. P., Sun, J., Rohwer, E. G., Deng, R., Mamouri, R.-E., and Zamorano, F.: An overview of the first decade of PollyNET: an emerging network of automated Raman-polarization lidars for continuous aerosol profiling, Atmos. Chem. Phys., 16, 5111–5137, https://doi.org/10.5194/acp-16-5111-2016, 2016. a
Banzhaf, E. and Kollai, H.: Land use/Land cover for Leipzig, Germany, for 2012 by an object-based image analysis (OBIA), PANGAEA, https://doi.org/10.1594/PANGAEA.895391, 2018. a
Short summary
Numerical models are an important tool to assess the air quality in cities, as they can provide near-continouos data in time and space. In this paper, air pollution for an entire city is simulated at a high spatial resolution of 40 m. At this spatial scale, the effects of buildings on the atmosphere, like channeling or blocking of the air flow, are directly represented by diffuse obstacles in the used model CAIRDIO. For model validation, measurements from air-monitoring sites are used.
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