A Supercomputer-Based Modeling System for Short-Term Prediction of Urban Surface Air Quality

Alexander V. Starchenko; Evgeniy A. Danilkin; Sergei Prokhanov; Lubov Kizhner; Elena Shelmina

doi:10.14529/jsfi220102

Authors

Alexander V. Starchenko National Research Tomsk State University, Tomsk, Russian Federation https://orcid.org/0000-0002-2388-1395
Evgeniy A. Danilkin National Research Tomsk State University, Tomsk, Russian Federation https://orcid.org/0000-0001-8694-2821
Sergei A. Prokhanov National Research Tomsk State University, Tomsk, Russian Federation https://orcid.org/0000-0002-4478-2249
Lubov I. Kizhner National Research Tomsk State University, Tomsk, Russian Federation
Elena A. Shelmina Tomsk State University of Control Systems and Radioelectronics, National Research Tomsk State University, Tomsk, Russian Federation https://orcid.org/0000-0002-6278-5961

DOI:

https://doi.org/10.14529/jsfi220102

Keywords:

parallel computations, numerical weather prediction, mesoscale models, urban air quality, MPI

Abstract

This paper proposes a mathematical model and an effective supercomputer-based numerical method for short-term prediction of extreme meteorological conditions and atmospheric air quality over limited stretches of land encompassing large population centers. The mathematical model includes a pollutant transport model with a reduced chemical mechanism and a non-hydrostatic mesoscale meteorological model with a modern moisture microphysics parametrization scheme. The numerical method relies on the use of the finite volume method and semi-implicit difference schemes of the second order of approximation, which are solved using the TDMA method with a linear dependence of the number of arithmetic operations on the size of the grid. This property of the numerical method ensures high efficiency when parallelized: not less than 70% when using up to 256 computing cores with a horizontal grid size of 0.5–1.0 km. Development of parallel programs was carried out using the Message Passing Interface parallel programming technology, two-dimensional decomposition of the grid area along horizontal (west to east and south to north) directions, and introduction of additional fictitious grid nodes along the perimeter of the decomposition subdomains.

References

Sokhi, R., Baklanov, A., Schlünzen, H.: Mesoscale Modelling for Meteorological and Air Pollution Applications. Anthem Press, New York (2018)

Dabdub, D., Seinfeld, J.H.: Parallel Computation in Atmospheric Chemical Modeling. Parallel Computing 22, 111–130 (1996). https://doi.org/10.1016/0167-8191(95)00063-1

Baklanov, A.A., Korsholm, U.S., Mahura, A.G., et al.: Physic a land chemical weather forecasting as a joint problem: two-way interacting integrated modelling. American Meteorological Society, Seattle (2011)

Nuterman, R., Korsholm, U., Zakey, A., et al.: New developments in Enviro-HIRLAM online integrated modelling system. Geophysical Research Abstracts 15 (2013)

Hood, C., MacKenzie, I., Stocker, J., et al.: Air quality simulations for London using a coupled regional-to-local modelling system. Atmospheric Chemistry and Physics 18, 11221–11245 (2018). https://doi.org/10.5194/acp-18-11221-2018

Strunk, A., Ebel, A., Elbern, H.: A nested application of four-dimensional variational assimilation of tropospheric chemical data. International Journal of Environment and Pollution 46(1–2), 43–60 (2011). https://doi.org/10.1504/IJEP.2011.042607

Skamarock, W.C., Klemp, J.B., Dudhia, J., et al.: A Description of the Advanced Research WRF Model Version 4. National Center for Atmospheric Research, Colorado (2021). https://doi.org/10.5065/1dfh-6p97

Stockwell, W.R., Kirchner, F., Kuhn, M., Seefeld, S.: A new mechanism for regional atmospheric chemistry modeling. Journal of Geophysical Research Atmospheres 102(22), 25847–25879 (1997). https://doi.org/10.1029/97jd00849

Carpenter, K.: Note on the paper ‘Radiation conditions for the lateral boundaries of limitedarea numerical models’. Quarterly Journal of the Royal Meteorological Society 108(457), 717–719 (1982). https://doi.org/10.1002/qj.49710845714

Wesley, M.L.: Parameterisation of surface resistances to gaseous dry deposition in regionalscale numerical models. Atmospheric Environment 23(6), 1293–1304 (1989). https://doi.org/10.1016/0004-6981(89)90153-4

Department of Natural Resources and Environmental Protection of the Tomsk Region. https://depnature.tomsk.gov.ru/2019-god, accessed: 2021-10-29

Hurley, P.J.: TAPM V4. Part 1: Technical Description. CSIRO Marine and Atmospheric Research, Aspendale (2008). https://doi.org/10.4225/08/585c175bc5884

Stockwell, W.R., Goliff, W.S.: Comment on “Simulation of a reacting pollutant puff, using an adaptive grid algorithm” by R.K. Srivastava et al. Journal of Geophysical Research Atmospheres 107(22), 4643–4650 (2002). https://doi.org/10.1029/2002JD002164

Starchenko, A.V., Bart, A.A., Kizhner, L.I., Danilkin, E.A.: Mesoscale meteorological model TSUNM3 for the study and forecast of meteorological parameters of the atmospheric surface layer over a major population center. Tomsk State University Journal of Mathematics and Mechanics 66, 35–55 (2020). https://doi.org/10.17223/19988621/66/2

Starchenko, A., Prokhanov, S., Danilkin, E., Lechinsky, D.: Numerical Forecast of Local Meteorological Conditions on a Supercomputer. In: Voevodin, V., Sobolev, S. (eds.) Supercomputing. RuSCDays 2020. Communications in Computer and Information Science, vol. 1331, pp. 273–284. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64616-5_24

Pielke, R.A.: Mesoscale Meteorological modelling. Academic Press, Orlando (1984)

Hong, S., Lim, J.: The WRF single-moment 6-class microphysics scheme (WSM6). Journal of the Korean Meteorological Society 42(2), 129–151 (2006)

Andrén, A.: Evaluation of a Turbulence Closure Scheme Suitable for Air-Pollution Applications. Journal of Applied Meteorology and Climatology 29(3), 224–239(1990). https://doi.org/10.1175/1520-0450(1990)029<0224:EOATCS>2.0.CO;2

Yamada, T.: Simulations of Nocturnal Drainage Flows by a q2l Turbulence Closure Model. Journal of Atmospheric Sciences 40(1), 91–106 (1983). https://doi.org/10.1175/1520-0469(1983)040<0091:SONDFB>2.0.CO;2

Smagorinsky, J.: General Circulation Experiments With the Primitive Equations: Part I. The Basic Experiment. Monthly Weather Review 91(2), 99–164 (1963). https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2

Mahrer, Y., Pielke, R.A.: A numerical study of the airflow over irregular terrain. Beitr. Phys. Atmosph. 50, 98–113 (1977)

Stephens, G.: Radiation Profiles in Extended Water Clouds. Part II: Parameterization Schemes. Journal of the Atmospheric Sciences 35(11), 2123–2132 (1978). https://doi.org/10.1175/1520-0469(1978)035<2123:RPIEWC>2.0.CO;2

Monin, A.S., Obukhov, A.M.: Basic laws of turbulent mixing in the surface layer of the atmosphere. Tr. Akad. Nauk SSSR Geofiz. 24, 163–187 (1954)

Dyer, A.J., Hicks, B.B.: Flux-gradient relationships in the constant flux layer. Quarterly Journal of the Royal Meteorological Society 96(410), 715–721 (1970). https://doi.org/10.1002/qj.49709641012

Tolstykh, M., Goyman, G., Fadeev, R., Shashkin, V.: Structure and Algorithms of SL-AV Atmosphere Model Parallel Program Complex. Lobachevskii Journal of Mathematics 39(4), 587–595 (2018). https://doi.org/10.1134/S1995080218040145

Mazumder, S.: Numerical Methods for Partial Differential Equations. Finite Difference and Finite Volume Methods. Academic Press (2016)

Van Leer, B.: Towards the ultimate conervative difference scheme. II. Monotonicity and conservation combined in a second order scheme. Journal of Computational Physics 14(4), 361–370 (1974). https://doi.org/10.1016/0021-9991(74)90019-9

Patankar, S.: Numerical Heat Transfer and Flow. CRC Press (2009). https://doi.org/10.1201/9781482234213

Ortega, J.O.: Introduction to Parallel and Vector Solution of Linear System. Plenum Press New York, Charlottesville (1988)

Starchenko, A.V., Danilkin, E.A., Prohanov, S.A., Leshchinskiy, D.V.: Parallel implementation of a numerical method for solving transport equations for the mesoscale meteorological model TSUNM3. Journal of Physics: Conference Series 1715(1), 012073 (2020). https://doi.org/10.1088/1742-6596/1715/1/012073

Bruneau, C.-H., Khadra, K.: Highly parallel computing of a multigrid solver for 3D Navier-Stokes equations. Journal of Computational Science 17(1), 35–46 (2016). https://doi.org/10.1016/j.jocs.2016.09.005

Wang, Y., Baboulin, M., Dongarra, J., et al.: A Parallel Solver for Incompressible Fluid Flows. Procedia Computer Science 18, 439–448 (2013). https://doi.org/10.1016/j.procs.2013.05.207