Air quality modeling: From deterministic to stochastic approaches. (English) Zbl 1142.76480
Summary: The objective of this article is to investigate the topics related to uncertainties in air quality modeling. A first point is the evaluation of uncertainties for model outputs: Monte Carlo methods and sensitivity analysis are powerful methods for assessing the impact of uncertainties due to model inputs. A second point is devoted to ensemble modeling with multi-models approaches. According to the wide spread in the model outputs, using a unique model, tuned to a small set of observational data, is not relevant in this field. On the basis of ensemble simulations, improved forecasts are given by appropriate algorithms to combine the set of models. The results applied to air quality modeling at continental scale with the Polyphemus system illustrate these methods. The first estimates of uncertainties in inverse modeling experiments are also proposed.
MSC:
| 76N17 | Viscous-inviscid interaction for compressible fluids and gas dynamics |
Keywords:
uncertainty analysis; sensitivity analysis; ensemble forecast; data assimilation; air qualitySoftware:
PolyphemusReferences:
| [1] | Stockwell, W. R.; Kirchner, F.; Kuhn, M.; Seefeld, S., A new mechanism for regional atmospheric chemistry modeling, J. Geophys. Res., 102, D22, 25847-25879 (1997) |
| [2] | A. Pourchet, V. Mallet, D. Quélo, B. Sportisse, Some numerical issues in Chemistry-Transport Models — A comprehensive study with the Polyphemus/Polair3D platform, Tech. Rep. 26, CEREA, 2005; A. Pourchet, V. Mallet, D. Quélo, B. Sportisse, Some numerical issues in Chemistry-Transport Models — A comprehensive study with the Polyphemus/Polair3D platform, Tech. Rep. 26, CEREA, 2005 |
| [3] | V. Mallet, Estimation de l’incertitude et prévision d’ensemble avec un modèle de chimie-transport-Application à la simulation numérique de la qualité de l’air, Ph.D. Thesis, École nationale des ponts et chaussées, 2005; V. Mallet, Estimation de l’incertitude et prévision d’ensemble avec un modèle de chimie-transport-Application à la simulation numérique de la qualité de l’air, Ph.D. Thesis, École nationale des ponts et chaussées, 2005 |
| [4] | Hanna, S. R.; Chang, J. C.; Fernau, M. E., Monte Carlo estimates of uncertainties in predictions by a photochemical grid model (UAM-IV) due to uncertainties in input variables, Atmos. Environ., 32, 21, 3619-3628 (1998) |
| [5] | Hanna, S. R.; Lu, Z.; Frey, H. C.; Wheeler, N.; Vukovich, J.; Arunachalam, S.; Fernau, M.; Hansen, D. A., Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain, Atmos. Environ., 35, 5, 891-903 (2001) |
| [6] | M.A. Tatang, Direct incorporation of uncertainty in chemical and environmental engineering systems, Ph.D. Thesis, Massachusetts Institute of Technology, 1995; M.A. Tatang, Direct incorporation of uncertainty in chemical and environmental engineering systems, Ph.D. Thesis, Massachusetts Institute of Technology, 1995 |
| [7] | J. Boutahar, Méthodes de réduction et de propagation d’incertitudes: Application à un modéle de chimie-transport pour la modélisation et la simulation des impacts, Ph.D. Thesis, École nationale des ponts et chaussées, 2004; J. Boutahar, Méthodes de réduction et de propagation d’incertitudes: Application à un modéle de chimie-transport pour la modélisation et la simulation des impacts, Ph.D. Thesis, École nationale des ponts et chaussées, 2004 |
| [8] | Mallet, V.; Sportisse, B., 3-D chemistry-transport model Polair: Numerical issues, validation and automatic-differentiation strategy, Atmos. Chem. Phys. Discuss., 4, 1371-1392 (2004) |
| [9] | Djouad, R.; Audiffren, N.; Sportisse, B., A sensitivity analysis study for RADM2 mechanism using automatic differentiation, Atmos. Environ., 37, 22, 3029-3038 (2003) |
| [10] | Mallet, V.; Sportisse, B., A comprehensive study of ozone sensitivity with respect to emissions over Europe with a chemistry-transport model, J. Geophys. Res., 110, D22 (2005) |
| [11] | Hoeting, J. A.; Madigan, D.; Raftery, A. E.; Volinsky, C. T., Bayesian model averaging: A tutorial, Stat. Sci., 14, 4, 382-417 (1999) · Zbl 1059.62525 |
| [12] | Mallet, V.; Quélo, D.; Sportisse, B.; Ahmed de Biasi, M.; Debry, É.; Korsakissok, I.; Wu, L.; Roustan, Y.; Sartelet, K.; Tombette, M.; Foudhil, H., Technical Note: The air quality modeling system Polyphemus, Atmos. Chem. Phys., 7, 20 (2007) |
| [13] | Mallet, V.; Sportisse, B., Uncertainty in a chemistry-transport model due to physical parameterizations and numerical approximations: An ensemble approach applied to ozone modeling, J. Geophys. Res., 111, D1 (2006) |
| [14] | Galmarini, S.; Bianconi, R.; Klug, W.; Mikkelsen, T.; Addis, R.; Andronopoulos, S.; Astrup, P.; Baklanov, A.; Bartniki, J.; Bartzis, J. C., Ensemble dispersion forecasting —- part I: Concept, approach and indicators, Atmos. Environ., 38, 28, 4607-4617 (2004) |
| [15] | Krishnamurti, T. N.; Kishtawal, C. M.; Zhang, Z.; LaRow, D. B.T.; Williford, E., Multimodel ensemble forecasts for weather and seasonal climate, J. Clim., 13, 4196-4216 (2000) |
| [16] | Cesa-Bianchi, N.; Long, P. M.; Warmuth, M. K., Worst-case quadratic loss bounds for prediction using linear functions and gradient descent, IEEE Trans. Neural Net., 7, 3, 604-619 (1996) |
| [17] | Elbern, H.; Schmidt, H., Ozone episode analysis by four-dimensional variational chemistry data assimilation, J. Geophys. Res., 106, D4, 3569-3590 (2001) |
| [18] | Wang, Z.; Navon, I.; Le Dimet, F.-X.; Zou, X., The second order adjoint analysis: Theory and applications, Meteorol. Atmos. Phys., 50, 3-20 (1992) |
| [19] | Quélo, D.; Sportisse, B.; Isnard, O., Data assimilation for short range atmospheric dispersion of radionuclides: A case study of second-order sensitivity, J. Environ. Radioact., 84, 3, 393-408 (2005) |
| [20] | Quélo, D.; Mallet, V.; Sportisse, B., Inverse modeling of \(NO_x\) emissions at regional scale over northern France: Preliminary investigation of the second-order sensitivity, J. Geophys. Res., 110, D24 (2005) |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
