Abstract
The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems (d 3 as) that efficiently integrate the observational data and the models. In this paper we discuss fundamental aspects of nonlinear ensemble data assimilation applied to atmospheric chemical transport models. We formulate autoregressive models for the background errors and show how these models are capable of capturing flow dependent correlations. Total energy singular vectors describe the directions of maximum errors growth and are used to initialize the ensembles. We highlight the challenges encountered in the computation of singular vectors in the presence of stiff chemistry and propose solutions to overcome them. Results for a large scale simulation of air pollution in East Asia illustrate the potential of nonlinear ensemble techniques to assimilate chemical observations.
This work was supported by the National Science Foundation through the award NSF ITR AP&IM 0205198 managed by Dr. Frederica Darema.
Chapter PDF
Similar content being viewed by others
Keywords
References
Buehner, M.: Ensemble-derived stationary and flow-dependent background error covariances: Evaluation in a quasi-operational NWP setting. Q.J.R.M.S (2004) (accepted)
Carmichael, G.R.: STEM – A second generation atmospheric chemical and transport model (2003), http://www.cgrer.uiowa.edu
Carmichael, G.R., et al.: Regional-scale chemical transport modeling in support of the analysis of observations obtained during the trace-p experiment. J. Geophys. Res., 10649–10671 (2004)
Carter, W.P.L.: Implementation of the SAPRC-99 chemical mechanism into the Models-3 framework. Technical report, United States Environmental Protection Agency (January 2000)
Constantinescu, E.M., Sandu, A., Carmichael, G.R., Chai, T.: Autoregressive models of background errors for chemical data assimilation (2005) (in preparation)
Daley, R.: Atmospheric Data Analysis. Cambridge University Press, Cambridge (1991)
Elbern, H., Schmidt, H., Ebel, A.: Variational data assimilation for tropospheric chemistry modeling. J. Geophys. Res. 102(D13), 15,967–15,985 (1997)
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99(C5), 10,143–10,162 (1994)
Evensen, G.: The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn. 53 (2003)
Fisher, M., Lary, D.J.: Lagrangian four-dimensional variational data assimilation of chemical species. Q.J.R.M.S. 121, 1681–1704 (1995)
Hamill, T.M., Whitaker, J.S.: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev. 129, 2776–2790 (2001)
Hasselmann, K.F.: Stochastic climate models. Part I. Theory. Tellus 28, 473–484 (1976)
Houtekamer, P.L., Mitchell, H.L.: Data assimilation using an Ensemble Kalman Filter Technique. Mon. Wea. Rev. 126(3), 796–811 (1998)
Houtekamer, P.L., Mitchell, H.L., Pellerin, G., Buehner, M., Charron, M., Spacek, L., Hansen, B.: Atmospheric data assimilation with the ensemble Kalman filter: Results with real observations. Mon. Wea. Rev (2003) (accepted)
Jazwinski, A.H.: Stochastic Processes and Filtering Theory. Academic Press, London (1970)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME, Ser. D: J. Basic Eng. 83, 95–108 (1960)
Lehoucq, R., Maschhoff, K., Sorensen, D., Yang, C.: ARPACK Software (Parallel and Serial), http://www.caam.rice.edu/software/ARPACK
Liao, W., Sandu, A., Carmichael, G.R., Chai, T.: Total energy singular vector analysis of atmospheric chemical transport models (2005) (submitted)
Menard, R., Cohn, S.E., Chang, L.P., Lyster, P.M.: Stratospheric assimilation of chemical tracer observations using a Kalman filter. Part I: Formulation. Mon. Wea. Rev. 128, 2654–2671 (2000)
Menut, L., Vautard, R., Beekmann, M., Honoré, C.: Sensitivity of photochemical pollution using the adjoint of a simplified chemistry-transport model. J. Geophys. Res. 105-D12(15), 15,379–15,402 (2000)
Riishojgaard, L.P.: A direct way of specifying flow-dependent background error correlations for meteorological analysis systems. Tellus A 50(1), 42–42 (1998)
Sandu, A., Daescu, D., Carmichael, G.R.: Direct and adjoint sensitivity analysis of chemical kinetic systems with KPP: I – theory and software tools. Atm. Env. 37, 5,083–5,096 (2003)
Sandu, A., Daescu, D.N., Carmichael, G.R., Chai, T.: Adjoint sensitivity analysis of regional air quality models. J. Comp. Phys. (2004) (accepted)
van Loon, M., Builtjes, P.J.H., Segers, A.J.: Data assimilation of ozone in the atmospheric transport chemistry model LOTOS. Env. Model. Soft. 15, 703–709 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sandu, A. et al. (2005). Ensemble–Based Data Assimilation for Atmospheric Chemical Transport Models. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3515. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428848_84
Download citation
DOI: https://doi.org/10.1007/11428848_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26043-1
Online ISBN: 978-3-540-32114-9
eBook Packages: Computer ScienceComputer Science (R0)