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| Journal Article | PreJuSER-17059 |
; ;
2010
EGU
Katlenburg-Lindau
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Please use a persistent id in citations: http://hdl.handle.net/2128/9959 doi:10.5194/acp-10-10435-2010
Abstract: Modelling of aerosol particles with chemical transport models is still based mainly on static emission databases while episodic emissions cannot be treated sufficiently. To overcome this situation, a coupling of chemical mass concentration modelling with satellite-based measurements relying on physical and optical principles has been developed. This study deals with the observation operator for a component-wise assimilation of satellite measurements. It treats aerosol particles classified into water soluble, water insoluble, soot, sea salt and mineral dust containing aerosol particles in the atmospheric boundary layer as separately assimilated aerosol components. It builds on a mapping of aerosol classes used both in observation and model space taking their optical and chemical properties into account. Refractive indices for primary organic carbon particles, anthropogenic particles, and secondary organic species have been defined based on a literature review. Together with a treatment of different size distributions in observations and model state, this allows transforming the background from mass concentrations into aerosol optical depths. A two-dimensional, variational assimilation is applied for component-wise aerosol optical depths. Error covariance matrices are defined based on a validation against AERONET sun photometer measurements. Analysis fields are assessed threefold: (1) through validation against AERONET especially in Saharan dust outbreak situations, (2) through comparison with the British Black Smoke and Sulphur Dioxide Network for soot-containing particles, and (3) through comparison with measurements of the water soluble components SO4, NH4, and NO3 conducted by the EMEP (European Monitoring and Evaluation Programme) network. Separately, for the water soluble, the soot and the mineral dust aerosol components a bias reduction and subsequent a root mean square error reduction is observed in the analysis for a test period from July to November 2003. Additionally, examples of an improved analysis during wildfire and dust outbreak situations are shown.
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