Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model 1Center for Climate System Research (CCSR), University of Tokyo, Kashiwanoha, Japan
2Japanese Meteorological Agency (JMA), Tokyo, Japan
3Research Institute for Applied Mechanics (RIAM), Kyushu University, Fukuoka, Japan
*now at: Department of Atmospheric and Oceanic Science (AOSC), College Park, Maryland, USA
Received: 10 Aug 2009 – Published in Atmos. Chem. Phys. Discuss.: 11 Nov 2009 Abstract. 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).
Revised: 09 Feb 2010 – Accepted: 02 Mar 2010 – Published: 12 Mar 2010
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.
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.
Citation: Schutgens, N. A. J., Miyoshi, T., Takemura, T., and Nakajima, T.: Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model, Atmos. Chem. Phys., 10, 2561-2576, doi:10.5194/acp-10-2561-2010, 2010.