Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1469-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1469-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
15 Mar 2021
Model description paper |
| 15 Mar 2021
| 15 Mar 2021
An urban large-eddy-simulation-based dispersion model for marginal grid resolutions: CAIRDIO v1.0
Michael Weger
CORRESPONDING AUTHOR
Leibniz Institute for Tropospheric Research, Leipzig, Germany
Oswald Knoth
Leibniz Institute for Tropospheric Research, Leipzig, Germany
Bernd Heinold
Leibniz Institute for Tropospheric Research, Leipzig, Germany
Related authors
Michael Weger and Bernd Heinold
Atmos. Chem. Phys., 23, 13769–13790, https://doi.org/10.5194/acp-23-13769-2023, https://doi.org/10.5194/acp-23-13769-2023, 2023
Short summary
Short summary
This study investigates the effects of complex terrain on air pollution trapping using a numerical model which simulates the dispersion of emissions under real meteorological conditions. The additionally simulated aerosol age allows us to distinguish areas that accumulate aerosol over time from areas that are more influenced by fresh emissions. The Dresden Basin, a widened section of the Elbe Valley in eastern Germany, is selected as the target area in a case study to demonstrate the concept.
Michael Weger, Holger Baars, Henriette Gebauer, Maik Merkel, Alfred Wiedensohler, and Bernd Heinold
Geosci. Model Dev., 15, 3315–3345, https://doi.org/10.5194/gmd-15-3315-2022, https://doi.org/10.5194/gmd-15-3315-2022, 2022
Short summary
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.
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Julian Hofer, Moritz Haarig, Ulla Wandinger, Bernd Heinold, Ina Tegen, Matthias Faust, Holger Baars, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Atmos. Chem. Phys., 25, 9737–9764, https://doi.org/10.5194/acp-25-9737-2025, https://doi.org/10.5194/acp-25-9737-2025, 2025
Short summary
Short summary
This study investigates how hematite in Sahara dust affects how dust particles interact with radiation. Using lidar data from Cabo Verde (2021–2022) and hematite content from atmospheric model simulations, the results show that a higher hematite fraction leads to a decrease in the particle backscattering coefficients in a spectrally different way. These findings can improve the representation of mineral dust in climate models, particularly regarding their radiative effect.
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025, https://doi.org/10.5194/gmd-18-4183-2025, 2025
Short summary
Short summary
This study represents the primary marine organic aerosol (PMOA) emissions, focusing on their sea–atmosphere transfer. Using the FESOM2.1–REcoM3 model, concentrations of key organic biomolecules were estimated and integrated into the ECHAM6.3–HAM2.3 aerosol–climate model. Results highlight the influence of marine biological activity and surface winds on PMOA emissions, with reasonably good agreement with observations improving aerosol representation in the southern oceans.
Anisbel Leon-Marcos, Manuela van Pinxteren, Sebastian Zeppenfeld, Moritz Zeising, Astrid Bracher, Laurent Oziel, Ina Tegen, and Bernd Heinold
EGUsphere, https://doi.org/10.5194/egusphere-2025-2829, https://doi.org/10.5194/egusphere-2025-2829, 2025
Short summary
Short summary
This study links modelled ocean surface concentrations of key marine organic groups with the aerosol-climate model ECHAM-HAM to quantify species-resolved primary marine organic aerosol emissions from 1990 to 2019. Results show strong seasonality, driven by productivity and summer sea ice loss. Emissions and burdens increased over time with more frequent positive anomalies in the last decade, revealing an overall upward trend with regional differences across the Arctic and aerosol species.
Levin Rug, Willi Schimmel, Fabian Hoffmann, and Oswald Knoth
EGUsphere, https://doi.org/10.5194/egusphere-2025-380, https://doi.org/10.5194/egusphere-2025-380, 2025
Short summary
Short summary
We present the Chemical Mechanism Integrator (Cminor) v1.0, a tool to predict concentrations of chemical compounds undergoing arbitrary reactions. Cminor is an advanced, open-source solver to model either combustion chemistry, or atmospheric chemistry and its direct influence on condensation of cloud droplets and the subsequent processing of aerosol. It uses the superdroplet idea, making it particularly feasible for coupling with such models, which is part of future work.
Jamie R. Banks, Bernd Heinold, and Kerstin Schepanski
Atmos. Chem. Phys., 24, 11451–11475, https://doi.org/10.5194/acp-24-11451-2024, https://doi.org/10.5194/acp-24-11451-2024, 2024
Short summary
Short summary
The Aralkum is a new desert in Central Asia formed by the desiccation of the Aral Sea. This has created a source of atmospheric dust, with implications for the balance of solar and thermal radiation. Simulating these effects using a dust transport model, we find that Aralkum dust adds radiative cooling effects to the surface and atmosphere on average but also adds heating events. Increases in surface pressure due to Aralkum dust strengthen the Siberian High and weaken the summer Asian heat low.
Andreas Walbröl, Janosch Michaelis, Sebastian Becker, Henning Dorff, Kerstin Ebell, Irina Gorodetskaya, Bernd Heinold, Benjamin Kirbus, Melanie Lauer, Nina Maherndl, Marion Maturilli, Johanna Mayer, Hanno Müller, Roel A. J. Neggers, Fiona M. Paulus, Johannes Röttenbacher, Janna E. Rückert, Imke Schirmacher, Nils Slättberg, André Ehrlich, Manfred Wendisch, and Susanne Crewell
Atmos. Chem. Phys., 24, 8007–8029, https://doi.org/10.5194/acp-24-8007-2024, https://doi.org/10.5194/acp-24-8007-2024, 2024
Short summary
Short summary
To support the interpretation of the data collected during the HALO-(AC)3 campaign, which took place in the North Atlantic sector of the Arctic from 7 March to 12 April 2022, we analyze how unusual the weather and sea ice conditions were with respect to the long-term climatology. From observations and ERA5 reanalysis, we found record-breaking warm air intrusions and a large variety of marine cold air outbreaks. Sea ice concentration was mostly within the climatological interquartile range.
Junghwa Lee, Patric Seifert, Tempei Hashino, Maximilian Maahn, Fabian Senf, and Oswald Knoth
Atmos. Chem. Phys., 24, 5737–5756, https://doi.org/10.5194/acp-24-5737-2024, https://doi.org/10.5194/acp-24-5737-2024, 2024
Short summary
Short summary
Spectral bin model simulations of an idealized supercooled stratiform cloud were performed with the AMPS model for variable CCN and INP concentrations. We performed radar forward simulations with PAMTRA to transfer the simulations into radar observational space. The derived radar reflectivity factors were compared to observational studies of stratiform mixed-phase clouds. These studies report a similar response of the radar reflectivity factor to aerosol perturbations as we found in our study.
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Matthias Faust, Holger Baars, Bernd Heinold, Julian Hofer, Ina Tegen, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Geosci. Model Dev., 17, 1271–1295, https://doi.org/10.5194/gmd-17-1271-2024, https://doi.org/10.5194/gmd-17-1271-2024, 2024
Short summary
Short summary
Mineral dust aerosol particles vary greatly in their composition depending on source region, which leads to different physicochemical properties. Most atmosphere–aerosol models consider mineral dust aerosols to be compositionally homogeneous, which ultimately increases model uncertainty. Here, we present an approach to explicitly consider the heterogeneity of the mineralogical composition for simulations of the Saharan atmospheric dust cycle with regard to dust transport towards the Atlantic.
Michael Weger and Bernd Heinold
Atmos. Chem. Phys., 23, 13769–13790, https://doi.org/10.5194/acp-23-13769-2023, https://doi.org/10.5194/acp-23-13769-2023, 2023
Short summary
Short summary
This study investigates the effects of complex terrain on air pollution trapping using a numerical model which simulates the dispersion of emissions under real meteorological conditions. The additionally simulated aerosol age allows us to distinguish areas that accumulate aerosol over time from areas that are more influenced by fresh emissions. The Dresden Basin, a widened section of the Elbe Valley in eastern Germany, is selected as the target area in a case study to demonstrate the concept.
Suvarna Fadnavis, Bernd Heinold, T. P. Sabin, Anne Kubin, Katty Huang, Alexandru Rap, and Rolf Müller
Atmos. Chem. Phys., 23, 10439–10449, https://doi.org/10.5194/acp-23-10439-2023, https://doi.org/10.5194/acp-23-10439-2023, 2023
Short summary
Short summary
The influence of the COVID-19 lockdown on the Himalayas caused increases in snow cover and a decrease in runoff, ultimately leading to an enhanced snow water equivalent. Our findings highlight that, out of the two processes causing a retreat of Himalayan glaciers – (1) slow response to global climate change and (2) fast response to local air pollution – a policy action on the latter is more likely to be within the reach of possible policy action to help billions of people in southern Asia.
Fabian Senf, Bernd Heinold, Anne Kubin, Jason Müller, Roland Schrödner, and Ina Tegen
Atmos. Chem. Phys., 23, 8939–8958, https://doi.org/10.5194/acp-23-8939-2023, https://doi.org/10.5194/acp-23-8939-2023, 2023
Short summary
Short summary
Wildfire smoke is a significant source of airborne atmospheric particles that can absorb sunlight. Extreme fires in particular, such as those during the 2019–2020 Australian wildfire season (Black Summer fires), can considerably affect our climate system. In the present study, we investigate the various effects of Australian smoke using a global climate model to clarify how the Earth's atmosphere, including its circulation systems, adjusted to the extraordinary amount of Australian smoke.
Bernd Heinold, Holger Baars, Boris Barja, Matthew Christensen, Anne Kubin, Kevin Ohneiser, Kerstin Schepanski, Nick Schutgens, Fabian Senf, Roland Schrödner, Diego Villanueva, and Ina Tegen
Atmos. Chem. Phys., 22, 9969–9985, https://doi.org/10.5194/acp-22-9969-2022, https://doi.org/10.5194/acp-22-9969-2022, 2022
Short summary
Short summary
The extreme 2019–2020 Australian wildfires produced massive smoke plumes lofted into the lower stratosphere by pyrocumulonimbus convection. Most climate models do not adequately simulate the injection height of such intense fires. By combining aerosol-climate modeling with prescribed pyroconvective smoke injection and lidar observations, this study shows the importance of the representation of the most extreme wildfire events for estimating the atmospheric energy budget.
Michael Weger, Holger Baars, Henriette Gebauer, Maik Merkel, Alfred Wiedensohler, and Bernd Heinold
Geosci. Model Dev., 15, 3315–3345, https://doi.org/10.5194/gmd-15-3315-2022, https://doi.org/10.5194/gmd-15-3315-2022, 2022
Short summary
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.
Tobias Peter Bauer, Peter Holtermann, Bernd Heinold, Hagen Radtke, Oswald Knoth, and Knut Klingbeil
Geosci. Model Dev., 14, 4843–4863, https://doi.org/10.5194/gmd-14-4843-2021, https://doi.org/10.5194/gmd-14-4843-2021, 2021
Short summary
Short summary
We present the coupled atmosphere–ocean model system ICONGETM. The added value and potential of using the latest coupling technologies are discussed in detail. An exchange grid handles the different coastlines from the unstructured atmosphere and the structured ocean grids. Due to a high level of automated processing, ICONGETM requires only minimal user input. The application to a coastal upwelling scenario demonstrates significantly improved model results compared to uncoupled simulations.
Christof G. Beer, Johannes Hendricks, Mattia Righi, Bernd Heinold, Ina Tegen, Silke Groß, Daniel Sauer, Adrian Walser, and Bernadett Weinzierl
Geosci. Model Dev., 13, 4287–4303, https://doi.org/10.5194/gmd-13-4287-2020, https://doi.org/10.5194/gmd-13-4287-2020, 2020
Short summary
Short summary
Mineral dust aerosol plays an important role in the climate system. Previously, dust emissions have often been represented in global models by prescribed monthly-mean emission fields representative of a specific year. We now apply an online calculation of wind-driven dust emissions. This results in an improved agreement with observations, due to a better representation of the highly variable dust emissions. Increasing the model resolution led to an additional performance gain.
Cited articles
Appel, K. W., Napelenok, S. L., Foley, K. M., Pye, H. O. T., Hogrefe, C., Luecken, D. J., Bash, J. O., Roselle, S. J., Pleim, J. E., Foroutan, H., Hutzell, W. T., Pouliot, G. A., Sarwar, G., Fahey, K. M., Gantt, B., Gilliam, R. C., Heath, N. K., Kang, D., Mathur, R., Schwede, D. B., Spero, T. L., Wong, D. C., and Young, J. O.: Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1, Geosci. Model Dev., 10, 1703–1732, https://doi.org/10.5194/gmd-10-1703-2017, 2017. a
Baik, J.-J., Park, S.-B., and Kim, J.-J.: Urban flow and dispersion simulation using a CFD model coupled to a mesoscale model, J. Appl. Meteorol. Clim., 48, 1667–1681,
https://doi.org/10.1175/2009JAMC2066.1, 2009. a
Baumann-Stanzer, K., Andronopoulos, S., Armand, P., Berbekar, E., Efthimiou,
G., Fuka, V., Gariazzo, C., Gašparac, G., Harms, F., Hellsten, A.,
Jurcacova, K., Petrov, A., Rákai, A., Stenzel, S., Tavares, R., Tinarelli,
G., and Trini Castelli, S.: COST ES1006 Model evaluation case studies:
Approach and results, available at:
http://www.elizas.eu/images/Documents/Model Evaluation Case Studies_web.pdf (last access: 2 March 2021),
2015. a
Benavides, J., Snyder, M., Guevara, M., Soret, A., Pérez García-Pando, C., Amato, F., Querol, X., and Jorba, O.: CALIOPE-Urban v1.0: coupling R-LINE with a mesoscale air quality modelling system for urban air quality forecasts over Barcelona city (Spain), Geosci. Model Dev., 12, 2811–2835, https://doi.org/10.5194/gmd-12-2811-2019, 2019. a
Birmili, W., Rehn, J., Vogel, A., Boehlke, C., Weber, K., and Rasch, F.:
Micro-scale variability of urban particle number and mass concentrations in
Leipzig, Germany, Meteorol. Z., 22, 155–165,
https://doi.org/10.1127/0941-2948/2013/0394, 2013. a
Brandt, A. and Livne, O. E.: Multigrid Techniques, Society for Industrial and
Applied Mathematics, Philadelphia, Pennsylvania, USA, https://doi.org/10.1137/1.9781611970753, 2011. a
Brown, M.: QUIC: A fast, high-resolution 3D building-aware urban transport and dispersion modeling system, AWMA Environmental Manager, April issue,
28–31, https://doi.org/10.2172/1134791, 2014. a
Bröker, O. and Grote, M.: Sparse approximate inverse smoothers for geometric
and algebraic multigrid, Appl. Numer. Math., 41, 61–80,
https://doi.org/10.1016/S0168-9274(01)00110-6, 2001. a
Calhoun, D. and LeVeque, R. J.: A Cartesian grid finite-volume method for the
advection-diffusion equation in irregular geometries, J.
Comput. Phys., 157, 143–180, https://doi.org/10.1006/jcph.1999.6369,
2000. a, b
Carlino, G., Pallavidino, L., Prandi, R., Avidano, A., Matteucci, G. L., Ricchiuti, F., Bajardi, P., and Bolognini, L.: Micro-scale modelling of urban air quality to
forecast NO2 critical levels in traffic hot-spots, available at: https://www.simularia.it/download/Simularia_Elise_per_AQC2016.pdf (last access: 2 March 2021), 2016. a
Chang, S. Y.: High Resolution Air Quality Modeling for Improved
Characterization of Exposures and Health Risk to Traffic-Related Air
Pollutants (TRAPs) in Urban Areas, PhD thesis, University of
North Carolina, Chapel Hill, USA, available at:
https://cdr.lib.unc.edu/concern/dissertations/q811kk98b (last access:
8 October 2020), 2016. a
Chorin, A.: Numerical solution of the Navier-Stokes equations, Math.
Comput., 22, 745–762, https://doi.org/10.1090/S0025-5718-1968-0242392-2,
1968. a
Croitoru, C. and Nastase, I.: A state of the art regarding urban air quality
prediction models, E3S Web of Conferences, 32, 01010,
https://doi.org/10.1051/e3sconf/20183201010, 2018. a
Deardorff, J. W.: A numerical study of three-dimensional turbulent channel flow at large Reynolds numbers, J. Fluid Mech., 41, 453–480,
https://doi.org/10.1017/S0022112070000691, 1970. a
Doms, G., Förstner, J., Heise, E., Herzog, H., Mironov, D., Raschendorfer, M.,
Reinhardt, T., Ritter, B., Schrodin, R., Schulz, J.-P., and Vogel, G.: A
description of the nonhydrostatic regional COSMO model. Part II: Physical
parameterization, Deutscher Wetterdienst, Offenbach, available at:
http://www.cosmo-model.org (last access: 28 December 2020), 2013. a
Drew, D.: Mathematical modeling of two-phase flow, Annu. Rev. Fluid
Mech., 15, 261–291, https://doi.org/10.1146/annurev.fl.15.010183.001401, 1983. a, b, c
Efstathiou, G. A., Beare, R. J., Osborne, S., and Lock, A. P.: Grey zone
simulations of the morning convective boundary layer development, J. Geophys. Res.-Atmos., 121, 4769–4782,
https://doi.org/10.1002/2016JD024860, 2016. a
Fallah-Shorshani, M., Shekarrizfard, M., and Hatzopoulou, M.: Integrating a
street-canyon model with a regional Gaussian dispersion model for improved
characterisation of near-road air pollution, Atmos. Environ., 153,
21–31, https://doi.org/10.1016/j.atmosenv.2017.01.006, 2017. a
Fernández, G., Rezzano Tizze, N., D'Angelo, M., and Mendina, M.: Numerical
simulation of different pollution sources in an urban environment, E3S Web of Conferences, 128, 10005, https://doi.org/10.1051/e3sconf/201912810005, 2019. a
Flemming, J., Huijnen, V., Arteta, J., Bechtold, P., Beljaars, A., Blechschmidt, A.-M., Diamantakis, M., Engelen, R. J., Gaudel, A., Inness, A., Jones, L., Josse, B., Katragkou, E., Marecal, V., Peuch, V.-H., Richter, A., Schultz, M. G., Stein, O., and Tsikerdekis, A.: Tropospheric chemistry in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 8, 975–1003, https://doi.org/10.5194/gmd-8-975-2015, 2015. a
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock,
W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975,
https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a
Haga, C. J. B.: Numerical methods for basin-scale poroelastic modelling, PhD thesis, University of Oslo, Oslo, Norway, available at:
http://urn.nb.no/URN:NBN:no-29117 (last access: 8 October 2020), 2011. a
Hanna, S. and Chang, J.: Acceptance criteria for urban dispersion model
evaluation, Meteorol. Atmos. Phys., 116, 133–146,
https://doi.org/10.1007/s00703-011-0177-1, 2012. a
Hanna, S. R., Brown, M. J., Camelli, F. E., Chan, S. T., Coirier, W. J.,
Hansen, O. R., Huber, A. H., Kim, S., and Reynolds, R. M.: Detailed
simulations of atmospheric flow and dispersion in urban downtown areas by
computational fluid dynamics (CFD) models – an application of five CFD models
to Manhattan, B. Am. Meteorol. Soc., 87, 1713–1726, https://doi.org/10.1175/BAMS-87-12-1713, 2006. a
Harrison, R.: Urban atmospheric chemistry: a very special case for study, NPJ
Clim. Atmos. Sci., 1, 20175–20180, https://doi.org/10.1038/s41612-017-0010-8, 2018. a
Haupt, S. E., Kosovic, B., Shaw, W., Berg, L. K., Churchfield, M., Cline, J.,
Draxl, C., Ennis, B., Koo, E., Kotamarthi, R., Mazzaro, L., Mirocha, J.,
Moriarty, P., Muñoz-Esparza, D., Quon, E., Rai, R. K., Robinson, M., and
Sever, G.: On bridging a modeling scale gap: Mesoscale to microscale coupling
for wind energy, B. Am. Meteorol. Soc., 100,
2533–2550, https://doi.org/10.1175/BAMS-D-18-0033.1, 2019. a, b
Hicken, J., Ham, F., Militzer, J., and Koksal, M.: A shift transformation for
fully conservative methods: turbulence simulation on complex, unstructured
grids, J. Comput. Phys., 208, 704–734,
https://doi.org/10.1016/j.jcp.2005.03.002, 2005. a
Jensen, S. S., Ketzel, M., Becker, T., Christensen, J., Brandt, J., Plejdrup,
M., Winther, M., Nielsen, O.-K., Hertel, O., and Ellermann, T.: High
resolution multi-scale air quality modelling for all streets in Denmark,
Transportation Res. D-Tr. E., 52, 322–339,
https://doi.org/10.1016/j.trd.2017.02.019, 2017. a
Jähn, M., Knoth, O., König, M., and Vogelsberg, U.: ASAM v2.7: a compressible atmospheric model with a Cartesian cut cell approach, Geosci. Model Dev., 8, 317–340, https://doi.org/10.5194/gmd-8-317-2015, 2015. a
Kadaverugu, R., Sharma, A., Matli, C., and Biniwale, R.: High resolution urban air quality modeling by coupling CFD and mesoscale models: a review,
Asia-Pacific J. Atmos. Sci., 55, 539–556,
https://doi.org/10.1007/s13143-019-00110-3, 2019. a
Kanda, M., Inagaki, A., Miyamoto, T., Gryschka, M., and Raasch, S.: A new
aerodynamic parametrization for real urban surfaces, Bound.-Lay.
Meteorol., 148, 357–377, https://doi.org/10.1007/s10546-013-9818-x, 2013. a
Karam, M., Sutherland, J., Hansen, M., and Saad, T.: A Framework for Analyzing the Temporal Accuracy of Pressure Projection Methods, AIAA Aviation 2019 Forum, Dallas, Texas, 17–21 June 2019, AIAA 2019-3634 https://doi.org/10.2514/6.2019-3634, 2019. a
Kemm, F., Gaburro, E., Thein, F., and Dumbser, M.: A simple diffuse interface
approach for compressible flows around moving solids of arbitrary shape based on a reduced Baer-Nunziato model, Comput. Fluids, 204,
104536–104561, https://doi.org/10.1016/j.compfluid.2020.104536, 2020. a, b
Kim, Y., Sartelet, K., Raut, J.-C., and Chazette, P.: Influence of an urban
canopy model and PBL schemes on vertical mixing for air quality modeling over Greater Paris, Atmos. Environ., 107, 289–306,
https://doi.org/10.1016/j.atmosenv.2015.02.011, 2015. a
Korhonen, A., Lehtomäki, H., Rumrich, I., Karvosenoja, N., Paunu, V.,
Kupiainen, K., Sofiev, M., Palamarchuk, Y., Kukkonen, J., Kangas, L.,
Karppinen, A., and Hänninen, O.: Influence of spatial resolution on
population PM2.5 exposure and health impacts, Air Qual. Atmos.
Hlth., 12, 705–718, https://doi.org/10.1007/s11869-019-00690-z, 2019. a
Kurppa, M., Roldin, P., Strömberg, J., Balling, A., Karttunen, S., Kuuluvainen, H., Niemi, J. V., Pirjola, L., Rönkkö, T., Timonen, H., Hellsten, A., and Järvi, L.: Sensitivity of spatial aerosol particle distributions to the boundary conditions in the PALM model system 6.0, Geosci. Model Dev., 13, 5663–5685, https://doi.org/10.5194/gmd-13-5663-2020, 2020. a
Larsson, J., Lien, F. S., and Yee, E.: Conditional semicoarsening multigrid
algorithm for the Poisson equation on anisotropic grids, J.
Comput. Phys., 208, 368–383, https://doi.org/10.1016/j.jcp.2005.02.020,
2005. a
Lee, M., Leitl, B., and Patnaik, G.: Model and application-specific validation data for LES-based transport and diffusion models, Proceedings of the Eighth Conference on Coastal Atmospheric and Oceanic Prediction and Processes, Phoenix, Arizona, 11–15 January, J17.1, 2009. a
Llorente, I. M. and Melson, N. D.: Behavior of plane relaxation methods as
multigrid smoothers, Electron. T. Numer. Ana., 10, 92–114, 2000. a
Louis, J. A.: A parametric model of vertical eddy fluxes in the atmosphere,
Bound.-Lay. Meteorol., 17, 187–202, https://doi.org/10.1007/BF00117978, 1979. a
Maronga, B., Gross, G., Raasch, S., Banzhaf, S., Forkel, R., Heldens, W.,
Kanani-Sühring, F., Matzarakis, A., Mauder, M., Pavlik, D., Pfafferott,
J., Schubert, S., Seckmeyer, G., Sieker, H., and Winderlich, K.: Development of a new urban climate model based on the model PALM–Project overview, planned work, and first achievements, Meteorol. Z., 28,
105–119, https://doi.org/10.1127/metz/2019/0909, 2019. a
Martilli, A., Clappier, A., and Rotach, M.: An urban surface exchange
parameterisation for mesoscale models, Bound.-Lay. Meteorol., 104,
261–304, https://doi.org/10.1023/A:1016099921195, 2002. a
Miller, M. J. and Thorpe, A. J.: Radiation conditions for the lateral
boundaries of limited-area numerical models, Q. J. Roy. Meteor. Soc., 107, 615–628, https://doi.org/10.1002/qj.49710745310, 1981. a
Mittal, R. and Iaccarino, G.: Immersed boundary methods, Annu. Rev. Fluid Mech., 37, 239–261, https://doi.org/10.1146/annurev.fluid.37.061903.175743,
2005. a
Mohr, M. and Wienands, R.: Cell-centred multigrid revisited, Comput.
Visual. Sci., 7, 129–140, https://doi.org/10.1007/S00791-004-0137-0,
2004. a
Noh, Y., C. W. H. S. and Raasch, S.: Improvement of the K-profile Model for the
Planetary Boundary Layer based on Large Eddy Simulation Data, Bound.-Lay.
Meteorol., 107, 401–427, https://doi.org/10.1023/A:1022146015946, 2003. a
Paas, B., Schmidt, T., Markova, S., Maras, I., Ziefle, M., and Schneider, C.:
Small-scale variability of particulate matter and perception of air quality
in an inner-city recreational area in Aachen, Germany, Meteorol.
Z., 25, 305–317, https://doi.org/10.1127/metz/2016/0704, 2016. a
Rakai, A. and Gergely, K.: Microscale obstacle resolving air quality model
evaluation with the Michelstadt case, Sci. World J., 2013,
781748, https://doi.org/10.1155/2013/781748, 2013. a
Rieger, D., Bangert, M., Bischoff-Gauss, I., Förstner, J., Lundgren, K., Reinert, D., Schröter, J., Vogel, H., Zängl, G., Ruhnke, R., and Vogel, B.: ICON–ART 1.0 – a new online-coupled model system from the global to regional scale, Geosci. Model Dev., 8, 1659–1676, https://doi.org/10.5194/gmd-8-1659-2015, 2015. a
Schatzmann, M., Leitl, B., Harms, F., and Hertwig, D.: Field data versus wind
tunnel data: The art of validating urban flow and dispersion models,
Proceedings of the 9th Asia-Pacific Conference on Wind Energy, Auckland, New Zealand, 3–8 December 2017, https://doi.org/10.17608/k6.auckland.5630923.v1, 2017. a
Schubert, S., Grossman-Clarke, S., and Martilli, A.: A double-canyon radiation scheme for multi-layer urban canopy models, Bound.-Lay. Meteorol., 145, 439–468, https://doi.org/10.1007/s10546-012-9728-3, 2012. a
Schumann, U.: Subgrid scale model for finite difference simulations of
turbulent flows in plane channels and annuli, J. Comput.
Phys., 18, 376–404, https://doi.org/10.1016/0021-9991(75)90093-5, 1975. a
Sedlacek, M.: Sparse approximate inverses for preconditioning, smoothing, and
regularization, PhD thesis, Technical University of Munich,
Munich, Germany, available at: https://d-nb.info/1029819246/34 (last access:
8 October 2020), 2012. a
Shu, C.-W.: Essentially non-oscillatory and weighted essentially
non-oscillatory schemes for hyperbolic conservation laws, Lecture Notes in
Mathematics, Springer, Berlin, Heidelberg, 1998. a
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Springer Science and Business Media LLC, Berlin, Germany, https://doi.org/10.1007/978-94-009-3027-8, 1988. a
Sweby, P. K.: High Resolution Schemes Using Flux Limiters for Hyperbolic
Conservation Laws, SIAM J. Numer. Anal., 21, 995–1011,
https://doi.org/10.1137/0721062, 1984. a
Tang, W.-P. and Wan, J.: Sparse approximate inverse smoother for multigrid,
SIAM J. Matrix Anal. A., 21, 1236–1252, https://doi.org/10.1137/S0895479899339342, 2000 a
Wang, J., Mao, J., Zhang, Y., Cheng, T., Yu, Q., Tan, J., and Ma, W.:
Simulating the effects of urban parameterizations on the passage of a cold
front during a pollution episode in megacity Shanghai, Atmosphere-Basel, 10, 79, https://doi.org/10.3390/atmos10020079, 2019. a
Weger, M., Knoth, O., and Heinold, B.: CAIRDIO City-Scale Air Dispersion Model with Diffusive Obstacles [computer program], Zenodo, https://doi.org/10.5281/zenodo.4486984, 2020. a
Wolf, T., Pettersson, L. H., and Esau, I.: A very high-resolution assessment and modelling of urban air quality, Atmos. Chem. Phys., 20, 625–647, https://doi.org/10.5194/acp-20-625-2020, 2020. a
Wolke, R., Knoth, O., Hellmuth, O., Schröder, W., and Renner, E.: The parallel model system LM-MUSCAT for chemistry-transport simulations: Coupling scheme, Parallelization and Applications, Adv. Par. Com., 13, 363–369,
https://doi.org/10.1016/S0927-5452(04)80048-0, 2004. a
Wolke, R., Schröder, W., Schrödner, R., and Renner, E.: Influence of grid
resolution and meteorological forcing on simulated European air quality: A
sensitivity study with the modeling system COSMO-MUSCAT, Atmos.
Environ., 53, 110–130, https://doi.org/10.1016/j.atmosenv.2012.02.085, 2012. a
Wu, X.: Inflow turbulence generation methods, Ann. Rev. Fluid Mech., 49, 23–49, https://doi.org/10.1146/annurev-fluid-010816-060322, 2017. a
Xie, Z. and Castro, I. P.: LES and RANS for turbulent flow over arrays of
wall-mounted obstacles, Flow, Turbulence and Combustion, 76, 291,
https://doi.org/10.1007/s10494-006-9018-6, 2006. a, b
Yavneh, I.: On Red-Black SOR Smoothing in Multigrid, SIAM J. Sci. Comput., 17, 180–192, https://doi.org/10.1137/0917013, 1996. a
Short summary
A new numerical air-quality transport model for cities is presented, in which buildings are described diffusively. The used diffusive-obstacles approach helps to reduce the computational costs for high-resolution simulations as the grid spacing can be more coarse than in traditional approaches. The research which led to this model development was primarily motivated by the need for a computationally feasible downscaling tool for urban wind and pollution fields from meteorological model output.
A new numerical air-quality transport model for cities is presented, in which buildings are...
