Abstract
ABSTRACTA four-dimensional variational data assimilation system (4D-Var) is developed to retrieve carbon monoxide (CO) fluxes at regional scale, using an air quality network. The air quality stations that monitor CO are proximity stations located close to industrial, urban or traffic sources. The mismatch between the coarsely discretised Eulerian transport model and the observations, inferred to be mainly due to representativeness errors in this context, lead to a bias (average simulated concentrations minus observed concentrations) of the same order of magnitude as the concentrations. 4D-Var leads to a mild improvement in the bias because it does not adequately handle the representativeness issue. For this reason, a simple statistical subgrid model is introduced and is coupled to 4D-Var. In addition to CO fluxes, the optimisation seeks to jointly retrieve influence coefficients, which quantify each station's representativeness. The method leads to a much better representation of the CO concentration variability, with a significant improvement of statistical indicators. The resulting increase in the total inventory estimate is close to the one obtained from remote sensing data assimilation. This methodology and experiments suggest that information useful at coarse scales can be better extracted from atmospheric constituent observations strongly impacted by representativeness errors.
Highlights
IntroductionObservations are infrequent in time and, for ground-measurements, sparse in space
In tracer transport studies, observations are infrequent in time and, for ground-measurements, sparse in space
The BDQA (Base de Donnees de la Qualitede l’Air, details available at http://www.atmonet.org) is a database listing the concentrations of several air quality pollutants over France
Summary
Observations are infrequent in time and, for ground-measurements, sparse in space. By minimising the sum of these two terms, 4D-Var makes an optimal compromise while enforcing the fact that the simulated concentrations are obtained from a given numerical transport model Iterative descent algorithms, such as conjugate gradient or quasi-Newton methods, are often used to minimise the cost function and to provide the optimal control parameters. Petron et al, 2002; Arellano and Hess, 2006; Stavrakou and Muller, 2006; Fortems-Cheiney et al, 2009; Kopacz et al, 2010) These studies make use of ground-based instruments that measure concentrations or they make use of satellite instruments to infer satellite-derived retrieval of CO.
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