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Abstract.A new model system for mapping and forecasting nitrogen deposition to the Baltic Sea has been developed. The system is based on the Lagrangian variable scale transport-chemistry model ACDEP (Atmospheric Chemistry and Deposition model), and aims at delivering deposition estimates to be used as input to marine ecosystem models. The system is tested by comparison of model results to measurements from monitoring stations around the Baltic Sea. The comparison shows that observed annual mean ambient air concentrations and wet depositions are well reproduced by the model. Diurnal mean concentrations of NH x (sum of NH 3 and NH + 4 ) and NO 2 are fairly well reproduced, whereas concentrations of total nitrate (sum of HNO 3 and NO − 3 ) are somewhat overestimated. Wet depositions of nitrate and ammonia are fairly well described for annual mean values, whereas the discrepancy is high for the monthly mean values and the wet depositions are rather poorly described concerning the diurnal mean values. The model calculations show that the annual atmospheric nitrogen deposition has a pronounced south-north gradient with depositions in the range about 1.0 T N km −2 in the south and 0.2 T N km −2 in the north. The results show that in 1999 the maximum diurnal mean deposition to the Danish waters appeared during the summer in the algae growth season. For the northern parts of the Baltic the highest depositions were distributed over most of the year. Total deposition to the Baltic Sea was for the year 1999 estimated to 318 kT N for an area of 464 406 km 2 equivalent to an average deposition of 684 kg N/km 2 .

The frequency matching method defines a closed form expression for a complex prior that quantifies the higher order statistics of a proposed solution model to an inverse problem. While existing solution methods to inverse problems are capable of sampling the solution space while taking into account arbitrarily complex a priori information defined by sample algorithms, it is not possible to directly compute the maximum a posteriori model, as the prior probability of a solution model cannot be expressed. We demonstrate how the frequency matching method enables us to compute the maximum a posteriori solution model to an inverse problem by using a priori information based on multiple point statistics learned from training images. We demonstrate the applicability of the suggested method on a synthetic tomographic crosshole inverse problem.

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Abstract. Data assimilation is the process of combining realworld 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 NO 2 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 NO 2 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 NO 2 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: groundbased measurements from European air-quality monitoring stations. The analysed NO 2 and O 3 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.