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Data assimilation is the process of combining real-world observations with a modelled geophysical field. The increasing abundance of satellite retrievals of atmospheric trace gases makes chemical data assimilation an increasingly viable method for deriving more accurate analysed fields and initial conditions for air quality forecasts. We implemented a three-dimensional optimal interpolation (OI) scheme to assimilate retrievals of NO2 tropospheric columns from the Ozone Monitoring Instrument into the Danish Eulerian Hemispheric Model (DEHM, version V2009.0), a three-dimensional, regional-scale, offline chemistry-transport model. The background error covariance matrix, B, was estimated based on differences in the NO2 concentration field between paired simulations using different meteorological inputs. Background error correlations were modelled as non-separable, horizontally homogeneous and isotropic. Parameters were estimated for each month and for each hour to allow for seasonal and diurnal patterns in NO2 concentrations. Three experiments were run to compare the effects of observation thinning and the choice of observation errors. Model performance was assessed by comparing the analysed fields to an independent set of observations: ground-based measurements from European air-quality monitoring stations. The analysed NO2 and O3 concentrations were more accurate than those from a reference simulation without assimilation, with increased temporal correlation for both species. Thinning of satellite data and the use of constant observation errors yielded a better balance between the observed increments and the prescribed error covariances, with no appreciable degradation in the surface concentrations due to the observation thinning. Forecasts were also considered and these showed rather limited influence from the initial conditions once the effects of the diurnal cycle are accounted for. The simple OI scheme was effective and computationally feasible in this context, where only a single species was assimilated, adjusting the three-dimensional field for this compound. Limitations of the assimilation scheme are discussed.

The chemical composition of the Earth’s atmosphere has major ramifications for not only human health, but also biodiversity and the climate; hence there are scientific, environmental and societal interests in accurate estimates of atmospheric chemical composition and in understanding the governing chemical and physical dynamics.Concentrations of atmospheric trace gases such as ozone, carbon monoxide and nitrogen dioxide vary substantially in space and time, and this variation can be investigated by various methods including direct measurements, remote-sensing measurements and atmospheric chemistry-transport models (CTMs). Each of these methods has their limitations: direct measurements provide only data at point locations and may not be representative of a wider area, remotely-sensed data from polar-orbiting satellites cannot investigate diurnal variation, andCTM simulations are often associated with higher uncertainties. It is possible, however, to combine information from measurements and models to more accurately estimate the state of the atmosphere using a statistically consistent framework known as “data assimilation”.In this study, three data assimilation schemes are implemented and evaluated. The data assimilation schemes are coupled to the Danish Eulerian Hemispheric Model (DEHM), a large-scale three-dimensional off-line CTM, and the data ingested were retrievals of atmospheric composition from polar-orbiting satellites. The three assimilation techniques applied were: a three-dimensional optimal interpolation procedure (OI), an Ensemble Kalman Filter(EnKF), and a three-dimensional variational scheme (3D-var). The three assimilation procedures are described and tested. A multi-faceted approach is taken for the verification, using independent measurements from surface air-quality monitoring stations, satellite retrievals of atmospheric chemical composition and comparison with idealised simulations.The 3D-var and EnKF schemes are capable of performing multi-species adjustments, meaning that observations of different chemical components can be assimilated simultaneously.Furthermore, observations of one chemical species can be used to adjust concentrations of other (unobserved) species. Most of the methodology used in data assimilation for CTMs is based on developments within the field of numerical weather prediction, where multiparameter assimilation schemes are the norm. The verification of the 3D-var and EnKF schemes are expanded to assess the potential benefits of joint multi-species adjustments (c.f.adjusting individual species independently) or direct adjustment of unobserved species.