Regional scale ozone data assimilation using an ensemble Kalman filter and the CHIMERE chemical transport model

Abstract

Abstract. An ensemble Kalman filter (EnKF) has been coupled to the CHIMERE chemical transport model in order to assimilate ozone ground-based measurements on a regional scale. The number of ensembles is reduced to 20, which allows for future operational use of the system for air quality analysis and forecast. Observation sites of the European ozone monitoring network have been classified using criteria on ozone temporal variability, based on previous work by Flemming et al. (2005). This leads to the choice of specific subsets of suburban, rural and remote sites for data assimilation and for evaluation of the reference run and the assimilation system. For a 10-day experiment during an ozone pollution event over Western Europe, data assimilation allows for a significant improvement in ozone fields: the RMSE is reduced by about a third with respect to the reference run, and the hourly correlation coefficient is increased from 0.75 to 0.87. Several sensitivity tests focus on an a posteriori diagnostic estimation of errors associated with the background estimate and with the spatial representativeness of observations. A strong diurnal cycle of both these errors with an amplitude up to a factor of 2 is made evident. Therefore, the hourly ozone background error and the observation error variances are corrected online in separate assimilation experiments. These adjusted background and observational error variances provide a better uncertainty estimate, as verified by using statistics based on the reduced centered random variable. Over the studied 10-day period the overall EnKF performance over evaluation stations is found relatively unaffected by different formulations of observation and simulation errors, probably due to the large density of observation sites. From these sensitivity tests, an optimal configuration was chosen for an assimilation experiment extended over a three-month summer period. It shows a similarly good performance as the 10-day experiment.

Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer

“…Gaubert et al, 2014), improved understanding and parameterisation of physical processes, better evaluation of models against data and the construction of model ensembles.…”

Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0

“…As a consequence, multivariate chemical DA with 3D-Var schemes has not been yet documented in the literature. In EnKF systems, the forecast model is used to propagate and estimate the background error covariance, but ad-hoc adjustments are necessary to avoid the collapse of the ensemble variance and obtain realistic covariance matrices for 1 h forecasts (Gaubert et al, 2014;Constantinescu et al, 2007a). As a result, costly algorithms such as EnKF or 3D-Var hardly give better results than more simple OI for chemical reanalyses (Rouil and the MACC team, 2014).…”

Limitations of ozone data assimilation with adjustment of NO<sub><i>x</i></sub> emissions: mixed effects on NO<sub>2</sub> forecasts over Beijing and surrounding areas

“…The role of CDA in optimizing initial and boundary conditions has been explored in several applications to improve forecasts of ozone and aerosol (Gaubert et al, 2014;Pagowski et al, 2014). Nevertheless, significant challenges persist in CDA.…”

“…One of the major challenges in CDA is that the impact of the initial conditions on the forecast of air pollutants such as ozone decreases with simulation time (Gaubert et al, 2014;sions with large uncertainties and strong impacts on air quality modeling, identified as the crucial sources of uncertainties and considered to be the key control variables (Beekmann and Derognat, 2003;Hanna et al, 2001), have been integrated into the CDA. The importance of emissions as control variables in the CDA has also been documented recently (Carmichael et al, 2008;Koohkan et al, 2013;Zhang et al, 2012).…”