2005
DOI: 10.1256/qj.05.110
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An ensemble Kalman filter for short‐term forecasting of tropospheric ozone concentrations

Abstract: SUMMARYAn air-quality forecasting system based on the pair 'NWP model MM5-chemistry transport model CAMx' is proposed. A version of the ensemble Kalman Filter has been developed. The model-error covariance matrix is parametrized with the help of a covariance function and represented by an ensemble formed as a random selection from leading eigenvectors. The performance of the system is tested on the case of an ozone episode in June 2001. As a source of observations, the AirBase database has been used. Starting … Show more

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Cited by 30 publications
(18 citation statements)
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“…RegCMCAMx4 further replaces the O'Brien (1970) method for calculating the coefficients of vertical turbulent diffusion (which is required by CAMx) with the newer Byun (1999) scheme (as used in CMAQ model), which provides better agreement of model results with measurements, as shown by Eben et al (2005) in a CAMx application over the same region and at similar horizontal resolution like in this study.…”
Section: Atmosmentioning
confidence: 90%
“…RegCMCAMx4 further replaces the O'Brien (1970) method for calculating the coefficients of vertical turbulent diffusion (which is required by CAMx) with the newer Byun (1999) scheme (as used in CMAQ model), which provides better agreement of model results with measurements, as shown by Eben et al (2005) in a CAMx application over the same region and at similar horizontal resolution like in this study.…”
Section: Atmosmentioning
confidence: 90%
“…The same issue causes loss of spread in ensembles generated by perturbations of initial conditions. The sample covariance matrices generated by the ensemble become ill-conditioned, and covariance inflation or similar methods have to be used to avoid divergence of ensemble filters (Eben et al, 2005;Constantinescu et al, 2007).…”
Section: Data Assimilation Of Chemical Speciesmentioning
confidence: 99%
“…Kalman filters have been used with atmospheric chemistry models by Ménard et al (2000). The numerical cost of this algorithm was addressed by the use of the ensemble Kalman filter and variants thereof in Segers et al (2000), Eben et al (2005), Hanea et al (2007), Wu et al (2008), and Sekiyama et al (2011). In order to address rank deficiencies and sampling issues, localisation and inflation have been used in this context (Constantinescu et al, 2007a, b;Schutgens et al, 2010).…”
Section: Introductionmentioning
confidence: 99%