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We present a global aerosol assimilation system based on an Ensemble Kalmanfilter, which we believe leads to a significant improvement in aerosolfields. The ensemble allows realistic, spatially and temporally variablemodel covariances (unlike other assimilation schemes). As the analyzedvariables are mixing ratios (prognostic variables of the aerosol transportmodel), there is no need for the extra assumptions required by previousassimilation schemes analyzing aerosol optical thickness (AOT).  We describe the implementation of this assimilation system and in particularthe construction of the ensemble. This ensemble should represent our estimateof current model uncertainties. Consequently, we construct the ensemblearound randomly modified emission scenarios.  The system is tested with AERONET observations of AOT and Angströmexponent (AE). Particular care is taken in prescribing the observationalerrors. The assimilated fields (AOT and AE) are validated throughindependent AERONET, SKYNET and MODIS Aqua observations. We show that,in general, assimilation of AOT observations leads to improved modelling ofglobal AOT, while assimilation of AE only improves modelling when theAOT is high

Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model