Articles | Volume 21, issue 14
https://doi.org/10.5194/acp-21-11099-2021
© 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/acp-21-11099-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jul 2021
Research article |
| 22 Jul 2021
| 22 Jul 2021
Downscaling system for modeling of atmospheric composition on regional, urban and street scales
Niels Bohr Institute, University of Copenhagen, Copenhagen, 2100,
Denmark
Alexander Mahura
Institute for Atmospheric and Earth System Research, University of
Helsinki, Helsinki, 00560, Finland
Alexander Baklanov
Science and Innovation Department, World Meteorological Organization, Geneva 2,
1211, Switzerland
Niels Bohr Institute, University of Copenhagen, Copenhagen, 2100,
Denmark
Bjarne Amstrup
Research Department, Danish Meteorological Institute, Copenhagen, 2100,
Denmark
Ashraf Zakey
Egyptian Meteorological Authority, Cairo, 11784, Egypt
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Cited articles
Aloyan, A., Baklanov, A., and Penenko, V.: Fictitious regions in numerical
simulation of quarry ventilation, Soviet Meteorology and Hydrology,
7, 32–37, 1982.
Amstrup B., Baklanov, A., Feddersen, H., Lorenzen, T., Mahura, A., Nuterman,
R., Weismann, J., Caian, M., Dumitrache, R., Rada, C., and Craciunescu, V.:
Core-downstream processing chain test cases. Evaluation of current R-ENS
individual and ensemble forecasts in the Copenhagen and Bucharest areas, in
EU FP7 MACC Report D_OINT_2.4.1-2, edited by:
Baklanov, A. and Mahura, A., Danish Meteorological Institute, Copenhagen,
36 pp., 2010a.
Amstrup B., Baklanov, A., Lorenzen, T., Mahura, A., Nuterman, R., Weismann,
J., Banciu, D., Tascu, S., Pietrisi, M., Dumitrache, R., and Caian, M.:
Core-downstream processing chain test cases. Setup of the two downscaling
model configurations, in EU FP7 MACC Report D_OINT_2.5.1-2, edited by: Baklanov, A. and Nuterman, R.,
Danish Meteorological Institute, Copenhagen, 18 pp., 2010b.
Bai, L., Wang, J., Ma, X., and Lu, X.: Air Pollution Forecasts: An Overview,
Int. J. Environ. Res. Pu., 15, 780, https://doi.org/10.3390/ijerph15040780,
2018.
Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D., Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas, E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta, J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos, N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E., Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects, Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, 2014.
Berkowicz, R., Britter, R., and Di Sabatino, S.: Optimisation of Modelling
Methods for Traffic Pollution in Streets (TRAPOS), National Environment
Research Institute, Roskilde, Denmark, 114 pp., 2004.
Berkowicz, R., Winther, M., and Ketzel, M.: Traffic pollution modelling and
emission data, Environ. Modell. Softw., 21, 454–460,
https://doi.org/10.1016/j.envsoft.2004.06.013, 2006.
Chao, E. L., Rosen, J. A., Hu, P. S., Schmitt, R., Sprung, M. J., Nguyen, L.
X., Riley, D., Parker, K., Young, L., Zhang, J., Beningo, S., Chambers, M.,
Ford, C., Notis, K., Liu, M., and Smith-Pickel, S.: National Transportation
Statistics, Bureau of Transportation Statistics, U.S. Department of
Transportation, Washington, DC, USA, 469 pp., 2018.
Ching, J.: A perspective on urban canopy layer modeling for weather, climate
and air quality applications, Urban Climate, 3, 13–39,
https://doi.org/10.1016/j.uclim.2013.02.001, 2013.
Craft, T. J., Launder, B. E., and Suga, K.: Development and application of a cubic
eddy-viscosity model of turbulence, Int. J. Heat Fluid Fl., 17,
108–115, https://doi.org/10.1016/0142-727X(95)00079-6, 1996.
Cuxart, J., Bougeault, P., and Redelsperger, J.-L.: A turbulence scheme
allowing for mesoscale and large-eddy simulations, Quart. J. Roy. Meteor.
Soc., 126, 1–30, https://doi.org/10.1002/qj.49712656202, 2000.
Ellermann, T., Nøjgaard, J., Nordstrøm, C., Brandt, J., Christensen,
J., Ketzel, M., and Jensen, S.: The Danish Air Quality Monitoring
Program. Annual Summary for 2011, DCE – Danish Centre for Environment and
Energy, Aarhus: Aarhus University, 63 pp., available at: https://www2.dmu.dk/Pub/SR37.pdf (last access: 20 June 2021), 2012.
Falasca, S. and Curci, G.: High-resolution air quality modeling: Sensitivity
tests to horizontal resolution and urban canopy with WRF-CHIMERE,
Atmos. Environ., 187, 241–254,
https://doi.org/10.1016/j.atmosenv.2018.05.048, 2018.
Flatman, A., Rosenkranz, B., Evers, K., Bartels, P., Kokkendorff, S.,
Knudsen, T., and Nielsen, T.: Quality assessment report to the Danish
Elevation Model (DK-DEM), Agency for Data Supply and Efficiency, Copenhagen,
26 pp., 2016.
Franke J., Hellsten, A., Schlünzen, H., Carissimo, B. (Eds.): Best
Practice Guideline for the CFD Simulation of Flows in the Urban Environment:
COST Action 732 Quality Assurance and Improvement of Microscale
Meteorological Models, COST Office, Hamburg, Germany, ISBN: 3-00-018312-4, 2007.
Gery, M., Whitten, G., Killus, J., and Dodge, M.: A photochemical kinetics
mechanism for urban and regional scale computer modelling, J. Geophys. Res.,
94, 12925–12956, https://doi.org/10.1029/JD094iD10p12925, 1989.
Gettelman, A., Callaghan, P., Larson, V. E., Zarzycki, C. M., Bacmeister,
J. T., Lauritzen, P. H., Bogenschutz, P. A., and Neale, R. B.: Regional climate
simulations with the Community Earth System Model, J. Adv.
Model. Earth Sy., 10, 1245–1265,
https://doi.org/10.1002/2017MS001227, 2018.
González, C. M., Ynoue, R. Y., Vara-Vela, A., Rojas, N. Y., and
Aristizábal, B. H.: High-resolution air quality modeling in a
medium-sized city in the tropical Andes: Assessment of local and global
emissions in understanding ozone and PM10 dynamics, Atmos. Pollut.
Res., 9, 934–948, https://doi.org/10.1016/j.apr.2018.03.003, 2018.
Grasso, O.: Interim evaluation of Copernicus, Final report, Imelpgriuimerie
centrale, Luxembourg, 224 pp., https://doi.org/10.2873/373088, 2017.
Gustafsson, N., Berre, L., Hörnquist, S., Huang, X.-Y., Lindskog, M.,
Navascués, B., Mogensen, K. S., and Thorsteinsson, S.: Three-dimensional
variational data assimilation for a limited area model. Part I: General
formulation and the background error constraint, Tellus A, 53, 425–446,
https://doi.org/10.3402/tellusa.v53i4.12198, 2001.
Hollingsworth, A., Engelen, R. J., Textor, C., Benedetti, A., Boucher, O.,
Chevallier, F., Dethof, A., Elbern, H., Eskes, H., Flemming, J., Granier,
C., Kaiser, J. W., Morcrette, J.-J., Rayner, P., Peuch, V.-H., Rouil, L.,
Schultz, M. G., Simmons, A. J., and The GEMS Consortium: Towards a
monitoring and forecasting system for atmospheric composition: The GEMS
Project, B. Am. Meteorol. Soc., 89, 1147–1164,
https://doi.org/10.1175/2008BAMS2355.1, 2008.
Ilin, V.: Methods of Incomplete
Factorization for Solving the Alge- braic Systems [in Russian], Fizmatlit, Moscow, 286 pp., 1995.
Khan, B., Banzhaf, S., Chan, E. C., Forkel, R., Kanani-Sühring, F., Ketelsen, K., Kurppa, M., Maronga, B., Mauder, M., Raasch, S., Russo, E., Schaap, M., and Sühring, M.: Development of an atmospheric chemistry model coupled to the PALM model system 6.0: implementation and first applications, Geosci. Model Dev., 14, 1171–1193, https://doi.org/10.5194/gmd-14-1171-2021, 2021.
Korsholm, U., Baklanov, A., and Sørensen, J.: Status and Evaluation of
Enviro-HIRLAM: Differences Between Online and Offline Models, in Integrated
Systems of Meso-Meteorological and Chemical Transport Models, edited by:
Baklanov, A., Mahura, A., and Sokhi, R., Springer, Berlin, 61–74, 2011.
Kuenen, J., van der Gon, H., Visschedijk, A., van der Brugh, H., Finardi,
S., Radice, P., d'Allura, A., Beevers, S., Theloke, J., Uzbasich, M.,
Honoreì, C., and Perrussel, O.: A Base Year (2005) MEGAPOLI European Gridded
Emission Inventory (Final Version), MEGAPOLI Scientific Report 10–17, DMI,
Copenhagen, 39 pp., ISBN 978-87-993898-8-9, 2010.
Kukkonen, J., Olsson, T., Schultz, D. M., Baklanov, A., Klein, T., Miranda, A. I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V.-H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lehtinen, K. E. J., Karatzas, K., San José, R., Astitha, M., Kallos, G., Schaap, M., Reimer, E., Jakobs, H., and Eben, K.: A review of operational, regional-scale, chemical weather forecasting models in Europe, Atmos. Chem. Phys., 12, 1–87, https://doi.org/10.5194/acp-12-1-2012, 2012.
Kwak, K.-H., Baik, J.-J., Ryu, Y.-H., and Lee, S.-H.: Urban air quality
simulation in a high-rise building area using a CFD model coupled with
mesoscale meteorological and chemistry-transport models, Atmos.
Environ., 100, 167–177,
https://doi.org/10.1016/j.atmosenv.2014.10.059, 2015.
Launder, B. E. and Spalding, D. B.: The numerical computation of turbulent
flows, Comput. Method. Appl. M., 3,
269–289, https://doi.org/10.1016/0045-7825(74)90029-2, 1974.
Lauwaet, D., Viaene, P., Brisson, E., van Noije, T., Strunk, A., van Looy,
S., Maiheu, B., Veldeman, N., Blyth, L., de Ridder, K., and Janssen, S.:
Impact of nesting resolution jump on dynamical downscaling ozone
concentrations over Belgium, Atmos. Environ., 67, 46–52,
https://doi.org/10.1016/j.atmosenv.2012.10.034, 2013.
Lindskog, M., Gustafsson, N., Navascués, B., Mogensen, K. S., Huang,
X.-Y., Yang, X., Andræ, U., Berre, L., Thorsteinsson, S., and Rantakokko,
J.: Three-dimensional variational data assimilation for a limited area
model. Part II: Observation handling and assimilation experiments, Tellus A, 53, 447–468, https://doi.org/10.3402/tellusa.v53i4.14578, 2001.
Lynch, P. and Huang, X.-Y.: Initialization of the HIRLAM Model Using a
Digital Filter, Mon. Weather Rev., 120, 1019–1034,
https://doi.org/10.1175/1520-0493(1992)120<1019:IOTHMU>2.0.CO;2, 1992.
Madronich, S. and Focke, S.: The role of solar radiation in atmospheric
chemistry, in: Handbook of Environmental Chemistry, edited by: Boule P.,
Springer-Verlag, New York, 1–26, 1999.
Mahura, A., Leroyer, S., Baklanov, A., Mestayer, P., Korsholm, U., and
Calmet, I.: Temporal and Spatial Variability of Fluxes in Urbanized Areas,
in: Urban Climate and Bioclimate, University of Lodz, Lodz, Poland, 219–232, ISBN:978-83-7525-243-9, 2008a.
Mahura, A., Petersen, C., Baklanov, A., and Amstrup, B.: Evaluation of
Building Effect Parameterization Module for Urbanized Numerical Weather
Prediction Modelling, in: Urban Climate and Bioclimate, University of Lodz, Lodz, Poland, 371–380,
ISBN 978-83-7525-243-9, 2008b.
Martilli, A., Clappier, A., and Rotach, M.: An urban surface exchange
parameterization for mesoscale models, Bound.-Lay. Meteorol., 104,
261–304, https://doi.org/10.1023/A:1016099921195, 2002.
Noilhan, J. and Planton, S.: A simple parameterization of land surface
processes for meteorological models, Mon. Weather Rev., 117, 536–549,
https://doi.org/10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2, 1989.
Nuterman, R., Baklanov, A., and Starchenko, A.: Modeling of aerodynamics and
pollution dispersion from traffic in the urban sublayer, Math. Models
Comput. Simul., 2, 738–752, https://doi.org/10.1134/S2070048210060098, 2010.
Nuterman, R., Starchenko, A. V., and Baklanov, A.: Numerical model of urban
aerodynamics and pollution dispersion. Int. J. of Environment and Pollution,
44, 385–393, https://doi.org/10.1504/IJEP.2011.038440, 2011.
Patankar, S.: Numerical Heat Transfer and Fluid Flow (1st edn.), CRC Press, Boca Raton, FL, USA,
214 pp., 1980.
Pepe, N., Pirovano, G., Lonati, G., Balzarini, A., Toppetti, A., Riva, G. M.,
and Bedogni, M.: Development and application of a high-resolution hybrid
modelling system for the evaluation of urban air quality, Atmos.
Environ., 141, 297–311, https://doi.org/10.1016/j.atmosenv.2016.06.071,
2016.
Petersen, C., Kmit, M., Nielsen, N., Amstrup, B., and Huess, V.: Performance
of DMI-HIRLAM-T15 and DMI-HIRLAM-S05 and the storm surge model in winter
storms, Technical Report 05-13, DMI, Copenhagen, 31 pp., ISSN 1399-1388,
2005.
Peuch, V.-H., Engelen, R., Calnan, R., Lambert, J.-C., and de Rudder, A.,
and The MACC-II Consortium: Monitoring Atmospheric Composition and Climate
II – Interim Implementation, Final Report, ECMWF, Reading, UK, 137 pp., 2014.
Peuch, V.-H., Engelen, R., Calnan, R., Lambert, J.-C., and de Rudder, A.,
and The MACC-III Consortium: Monitoring Atmospheric Composition and Climate
3, Final Report, ECMWF, Reading, UK, 148 pp., 2016.
Ramboll Environ: User's Guide,
Comprehensive air quality model with extensions, Version 6.40, Ramboll Environ, Novato, California, 283 pp., 2016 (data available at: https://camx-wp.azurewebsites.net/getmedia/CAMx_v6.40.src.161223.tgz, last access: 20 June 2021).
Richards, P. J. and Hoxey, R. P.: Appropriate boundary conditions for
computational wind engineering models using the k-ε turbulence
model, J. Wind Eng. Ind. Aerod., 46–47,
145–153, https://doi.org/10.1016/0167-6105(93)90124-7, 1993.
Sabatino, S. D., Buccolieri, R., Olesen, H., Rørdam, H., Ketzel, M.,
Berkowicz, R., Franke, J., Schatzmann, M., Schlünzen, K. H., Leitl, B.,
Britter, R., Borrego, C., Alexandre, C., Castelli, S. T., Reisin, T. G.,
Hellsten, A., Saloranta, J., Moussiopoulos, N., Barmpas, F., Brzozowski, K.,
Goricsán, I., Balczò, M., Bartzis, J. G., Efthimiou, G., Nuterman,
R., and Starchenko, A. V.: COST 732 in practice: the MUST model evaluation
exercise, Int. J. Environ. Pollut., 44,
403–418, https://doi.org/10.1504/IJEP.2011.038442, 2011.
Sander, S. P., Friedl, R. R., Golden, D. M., Kurylo, M. J., Moortgat, G. K.,
Keller-Rudek, H., Wine, P. H., Ravishankara, A. R., Kolb, C. E., Molina, M.
J., Finlayson-Pitts, B. J., Huie, R. E., and Orkin, V. L.: Chemical Kinetics
and Photochemical Data for Use in Atmospheric Studies, Evaluation Number 15,
JPL Publication, Jet Propulsion
Laboratory, National Aeronautics and Space Administration, Pasadena, CA, USA, 2006.
San José, R., Pérez, J. L., and Gonzalez-Barras, R. M.: Assessment of
mesoscale and microscale simulations of a NO2 episode supported by traffic
modelling at microscopic level, Sci. Total Environ., 752, 141992,
https://doi.org/10.1016/j.scitotenv.2020.141992, 2021.
Sass, B. H.: A research version of the STRACO cloud scheme, DMI Tech. Rep.,
02-10, Danish Meteorological Institute, Copenhagen, 25 pp., ISSN 1399-1388,
2002.
Savijärvi, H.: Fast radiation parameterization schemes for mesoscale and
short-range forecast models, J. Appl. Meteor., 29, 437–447,
https://doi.org/10.1175/1520-0450(1990)029<0437:FRPSFM>2.0.CO;2, 1990.
Schaap, M., Cuvelier, C., Hendriks, C., Bessagnet, B., Baldasano, J. M.,
Colette, A., Thunis, P., Karam, D., Fagerli, H., Graff, A., Kranenburg, R.,
Nyiri, A., Pay, M. T., Rouïl, L., Schulz, M., Simpson, D., Stern, R.,
Terrenoire, E., and Wind, P.: Performance of European chemistry transport
models as function of horizontal resolution, Atmos. Environ., 112,
90–105, https://doi.org/10.1016/j.atmosenv.2015.04.003, 2015.
Schatzmann, M., Olesen, H., and Franke, J. (Eds.): COST 732 Model Evaluation
Case Studies: Approach and Results, COST Office, Hamburg, Germany, ISBN 3-00-018312-4, 2010.
Schlünzen, K., Grawe, D., Bohnenstengel, S., Schlüter, I., and
Koppmann, R.: Joint modelling of obstacle induced and mesoscale changes –
Current limits and challenges, J. Wind Eng. Ind.
Aerod., 99, 217–225, https://doi.org/10.1016/j.jweia.2011.01.009,
2011.
Seinfeld, J. and Pandis, S.: Atmospheric Chemistry and Physics, From Air
Pollution to Climate Change (2nd edn.), John Wiley & Sons, New York, 1152
pp., 2016.
Simmons, A.: Monitoring Atmospheric Composition and Climate, Meteorology –
ECMWF Newsletter No. 123, 10–13, https://doi.org/10.21957/vqcvmlyg, 2010.
Slinn, S. and Slinn, W.: Predictions for particle deposition on natural
waters, Atmos. Environ., 24, 1013–1016,
https://doi.org/10.1016/0004-6981(80)90032-3, 1980.
Sokhi, R., Baklanov, A., and Schlünzen, K. (Eds.): Mesoscale Modelling
for Meteorological and Air Pollution Applications, Anthem Press, New York,
382 pp., available at: http://www.jstor.org/stable/j.ctv80cdh5 (last access: 20 June 2021), 2018.
Stockwell, W. R. and Goliff, W. S.: Comment on “Simulation of a reacting
pollutant puff using an adaptive grid algorithm” by R. K. Srivastava et al.,
J. Geophys. Res., 107, 4643,
https://doi.org/10.1029/2002JD002164, 2002.
Temel, O., Bricteux, L., and van Beeck, J.: Coupled WRF-OpenFOAM study of
wind flow over complex terrain, J. Wind Eng. Ind.
Aerod., 174, 152–169, https://doi.org/10.1016/j.jweia.2018.01.002,
2018.
Undén, P., Rontu, L., Järvinen, H., Lynch, P., Calvo,
J., Cats, G., Cuxart, J., Eerola, K., Fortelius, C., Garcia-Moya, J. A.,
Jones, C., Lenderlink, G., McDonald, A., Mc-Grath, R., Navascues, B.,
Nielsen, N. W., Øidegaard, V., Rodriguez, E., Rummukainen, M., Rõõm,
R., Sattler, K., Sass, B. H., Savijärvi, H., Schreur, B. W., Sigg, R., The,
H., and Tijm, A.: HIRLAM-5 Scientific Documentation, Tech. rep., The HIRLAM
project, Norrköping, Sweden, 2002 (data available at: http://hirlam.org, last access 20 June 2021).
Vabishchevich, P. N.: The Method of Fictitious Domains in Problems of
Mathematical Physics [in Russian], Moscow University, Moskva, 1991.
van Leer, B.: Towards the Ultimate Conservative Difference Scheme. II.
Monotonicity and Conservation Combined in a Second Order Scheme, J. Comput.
Phys., 14, 361–370, https://doi.org/10.1016/0021-9991(74)90019-9, 1974.
Veratti, G., Fabbi, S., Bigi, A., Lupascu, A., Tinarelli, G., Teggi, S.,
Brusasca, G., Butler, T. M., and Ghermandi, G.: Towards the coupling of a
chemical transport model with a micro-scale Lagrangian modelling system for
evaluation of urban NOx levels in a European hotspot, Atmos.
Environ., 223, 117285, https://doi.org/10.1016/j.atmosenv.2020.117285,
2020.
Wesely, M.: Parameterization of Surface Resistances to Gaseous Dry
Deposition in Regional-Scale Numerical Models, Atmos. Environ., 23,
1293–1304, https://doi.org/10.1016/0004-6981(89)90153-4, 1989.
Yarwood, G., Rao, S., Yocke, M., and Whitten, G.: Updates to the Carbon Bond
chemical mechanism: CB05, RT-04-00675, ENVIRON International Corporation,
Yocke & Company, Novato, CA, USA, 161 pp., 2005.
Short summary
The street air pollution is usually higher than the pollution at regional and urban scales. It mostly associated with both local emission sources and urban weather conditions. We present the downscaling system for regional, subregional-urban and street scales and evaluate it for acute air-pollution episode. Its evaluation showed a good prediction score across various spatiotemporal scales as well as feasibility of deterministic modelling approach for the operational street scale forecasting.
The street air pollution is usually higher than the pollution at regional and urban scales. It...
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