Improving Numerical Dispersion Modelling in Built Environments with Data Assimilation Using the Iterative Ensemble Kalman Smoother
Tóm tắt
Air-pollution modelling at the local scale requires accurate meteorological inputs such as from the velocity field. These meteorological fields are generally simulated with microscale models (here Code_Saturne), which are forced with boundary conditions provided by larger scale models or observations. Local atmospheric simulations are very sensitive to the boundary conditions, whose accurate estimation is difficult but crucial. When observations of the wind speed and turbulence or pollutant concentration are available inside the domain, they provide supplementary information via data assimilation, to enhance the simulation accuracy by modifying the boundary conditions. Among the existing data assimilation methods, the iterative ensemble Kalman smoother (IEnKS) is adapted to urban-scale simulations. This method has already been found to increase the accuracy of wind-resource assessment. Here we assess the ability of the IEnKS method to improve scalar-dispersion modelling—an important component of air-quality modelling—by assimilating perturbed measurements inside the urban canopy. To test the data assimilation method in urban conditions, we use the observations provided by the Mock Urban Setting Test field campaign and consider cases with neutral and stable conditions, and the boundary conditions consisting of the horizontal velocity components and turbulence. We prove the capacity of the IEnKS method to assimilate observations of velocity as well as pollutant concentration. In both cases, the accuracy of pollutant concentration estimates is enhanced by 40–60%. We also show that assimilating both types of observations allows further improvements of turbulence predictions by the model.
Tài liệu tham khảo
Albriet B, Sartelet K, Lacour S, Carissimo B, Seigneur C (2010) Modelling aerosol number distributions from a vehicle exhaust with an aerosol CFD model. Atmos Environ 44(8):1126–1137
Archambeau F, Méchitoua N, Sakiz M (2004) Code Saturne: a finite volume code for the computation of turbulent incompressible flows-industrial applications. Int J Finite Volumes 1:1–62
Asch M, Bocquet M, Nodet M (2016) Data assimilation: methods, algorithms, and applications. Society for Industrial and Applied Mathematics, Philadelphia
Auroux D, Blum J (2008) A nudging-based data assimilation method: the Back and Forth Nudging (BFN) algorithm. Nonlinear Process Geophys 15:305–319
Bahlali M (2018) Adaptation de la modélisation hybride eulérienne/lagrangienne stochastique de Code\_Saturne à la dispersion atmosphérique de polluants à l’échelle micro-météorologique et comparaison à la méthode eulérienne. Ph.D. thesis, Université Paris-Est. http://www.theses.fr/2018PESC1047/document
Bahlali ML, Dupont E, Carissimo B (2019) Atmospheric dispersion using a lagrangian stochastic approach: application to an idealized urban area under neutral and stable meteorological conditions. J Wind Eng Ind Aerodyn 193(103):976
Balgovind R, Dalcher A, Ghil M, Kalnay E, Balgovind R, Dalcher A, Ghil M, Kalnay E (1983) A stochastic-dynamic model for the spatial structure of forecast error statistics. Mon Weather Rev 111(4):701–722
Benamrane Y, Wybo JL, Armand P (2013) Chernobyl and Fukushima nuclear accidents: what has changed in the use of atmospheric dispersion modeling? J Environ Radioact 126:239–252
Bertino L, Evensen G, Wackernagel H (2003) Sequential data assimilation techniques in oceanography. Int Stat Rev 71(2):223–241
Biltoft CA (2001) Customer report for mock urban setting test. DPG Document Number 8-CO-160-000-052 Prepared for the Defence Threat Reduction Agency
Björck Å (1996) Numerical methods for least squares problems. Society for Industrial and Applied Mathematics, Philadelphia
Blocken B, Tominaga Y, Stathopoulos T (2013) CFD simulation of micro-scale pollutant dispersion in the built environment. Build Environ 64:225–230
Bocquet M, Sakov P (2014) An iterative ensemble Kalman smoother. Q J R Meteorol Soc 140:1521–1535
Cohn SE (1997) An introduction to estimation theory (gtspecial issueltdata assimilation in meteology and oceanography: theory and practice). J Meteorol Soc Jpn Ser II 75(1B):257–288
Davoine X, Bocquet M (2007) Inverse modelling-based reconstruction of the Chernobyl source term available for long-range transport. Atmos Chem Phys 7(6):1549–1564
Defforge CL, Carissimo B, Bocquet M, Armand P, Bresson R (2018) Data assimilation at local scale to improve CFD simulations of atmospheric dispersion: application to 1D shallow-water equations and method comparisons. Int J Environ Pollut 64(1/3):90–109
Defforge CL, Carissimo B, Bocquet M, Bresson R, Armand P (2019) Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother. J Wind Eng Ind Aerodyn 189:243–257
Garratt J (1994) Review: the atmospheric boundary layer. Earth Sci Rev 37(1–2):89–134
Hanna SR, Brown MJ, Camelli FE, Chan ST, Coirier WJ, Hansen OR, Huber AH, Kim S, Reynolds RM, Hanna SR, Brown MJ, Camelli FE, Chan ST, Coirier WJ, Hansen OR, Huber AH, Kim S, Reynolds RM (2006) Detailed simulations of atmospheric flow and dispersion in downtown Manhattan: an application of five computational fluid dynamics models. Bull Am Meteorol Soc 87(12):1713–1726
Holmes N, Morawska L (2006) A review of dispersion modelling and its application to the dispersion of particles: an overview of different dispersion models available. Atmos Environ 40(30):5902–5928
Kalnay E (2003) Atmospheric modeling, data assimilation, and predictability. Cambridge University Press, Cambridge
Kovalets IV, Tsiouri V, Andronopoulos S, Bartzis JG (2009) Improvement of source and wind field input of atmospheric dispersion model by assimilation of concentration measurements: Method and applications in idealized settings. Appl Math Model 33(8):3511–3521
Krysta M, Bocquet M, Sportisse B, Isnard O (2006) Data assimilation for short-range dispersion of radionuclides: an application to wind tunnel data. Atmos Environ 40(38):7267–7279
Kumar P, Ketzel M, Vardoulakis S, Pirjola L, Britter R (2011) Dynamics and dispersion modelling of nanoparticles from road traffic in the urban atmospheric environment—a review. J Aerosol Sci 42(9):580–603
Kumar P, Feiz AA, Singh SK, Ngae P, Turbelin G (2015) Reconstruction of an atmospheric tracer source in an urban-like environment. J Geophys Res Atmos 120(24):12589–12604
Liu Y, Haussaire JM, Bocquet M, Roustan Y, Saunier O, Mathieu A (2017) Uncertainty quantification of pollutant source retrieval: comparison of Bayesian methods with application to the Chernobyl and Fukushima Daiichi accidental releases of radionuclides. Q J R Meteorol Soc 143(708):2886–2901
Milliez M (2006) Modélisation micro-météorologique en milieu urbain: dispersion des polluants et prise en compte des effets radiatifs. Ph.D. thesis, ENPC. https://pastel.archives-ouvertes.fr/pastel-00004042/document
Milliez M, Carissimo B (2007) Numerical simulations of pollutant dispersion in an idealized urban area, for different meteorological conditions. Boundary-Layer Meteorol 122(2):321–342
Milliez M, Carissimo B (2008) Computational fluid dynamical modelling of concentration fluctuations in an idealized urban area. Boundary-Layer Meteorol 127(2):241–259
Mons V, Margheri L, Chassaing JC, Sagaut P (2017) Data assimilation-based reconstruction of urban pollutant release characteristics. J Wind Eng Ind Aerodyn 169:232–250
Robins A (2003) Wind tunnel dispersion modelling some recent and not so recent achievements. J Wind Eng Ind Aerodyn 91(12–15):1777–1790
Sakov P, Oliver DS, Bertino L (2012) An iterative EnKF for strongly nonlinear systems. Mon Weather Rev 140:1988–2004
Sousa J, García-Sánchez C, Gorlé C (2018) Improving urban flow predictions through data assimilation. Build Environ 132(February):282–290
Srebric J, Vukovic V, He G (2008) CFD boundary conditions for contaminant dispersion, heat transfer and airflow simulations around human occupants in indoor environments. Build Environ 43(3):294–303
Stull RB (1988) An introduction to boundary layer meteorology. Springer, Dordrecht
Winiarek V (2014) Dispersion atmosphérique et modélisation inverse pour la reconstruction de sources accidentelles de polluants. Ph.D. thesis, Université Paris-Est. https://pastel.archives-ouvertes.fr/tel-01004505/document
Winiarek V, Bocquet M, Saunier O, Mathieu A (2012) Estimation of errors in the inverse modeling of accidental release of atmospheric pollutant: application to the reconstruction of the cesium-137 and iodine-131 source terms from the Fukushima Daiichi power plant. J Geophys Res Atmos 117(D5):D05122
Yee E, Biltoft CA (2004) Concentration fluctuation measurements in a plume dispersing through a regular array of obstacles. Boundary-Layer Meteorol 111(3):363–415