ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-10-2561-2010 Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model Schutgens N. A. J. 1 Miyoshi T. 2 4 Takemura T. 3 Nakajima T. 1 Center for Climate System Research (CCSR), University of Tokyo, Kashiwanoha, Japan Japanese Meteorological Agency (JMA), Tokyo, Japan Research Institute for Applied Mechanics (RIAM), Kyushu University, Fukuoka, Japan now at: Department of Atmospheric and Oceanic Science (AOSC), College Park, Maryland, USA 12 03 2010 10 5 2561 2576 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/10/2561/2010/acp-10-2561-2010.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/10/2561/2010/acp-10-2561-2010.pdf

We present a global aerosol assimilation system based on an Ensemble Kalman filter, which we believe leads to a significant improvement in aerosol fields. The ensemble allows realistic, spatially and temporally variable model covariances (unlike other assimilation schemes). As the analyzed variables are mixing ratios (prognostic variables of the aerosol transport model), there is no need for the extra assumptions required by previous assimilation schemes analyzing aerosol optical thickness (AOT). <br><br> We describe the implementation of this assimilation system and in particular the construction of the ensemble. This ensemble should represent our estimate of current model uncertainties. Consequently, we construct the ensemble around randomly modified emission scenarios. <br><br> The system is tested with AERONET observations of AOT and Angström exponent (AE). Particular care is taken in prescribing the observational errors. The assimilated fields (AOT and AE) are validated through independent AERONET, SKYNET and MODIS Aqua observations. We show that, in general, assimilation of AOT observations leads to improved modelling of global AOT, while assimilation of AE only improves modelling when the AOT is high.

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