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Abstract:
Use of data assimilation techniques such as optimal interpolation or the
Kalman filter in global chemistry transport models (CTM) is becoming
more common. However: owing to high computational requirements, it is
often difficult to apply these techniques to multidimensional models
containing extensive photochemical schemes. We present a sequential
assimilation approach developed for use with general global chemistry
transport models. It allows fast assimilation and mapping of satellite
observations and provides estimates of analysis errors. The suggested
data assimilation scheme evolved from the one described by Levelt ct nl.
[1998]. It is a variant of the suboptimal Kalman filter and is based on
ideas described by Menard ct nl. [2000] and Menard and Chang [2000]. One
of the most important features of the developed scheme is its ability to
routinely estimate variance of the analysis and to predict variance
evolution in the model. The developed technique (or its variants) has
been successfully interfaced with a number of different global models
and used for assimilation of several types of measurements. including
aerosol extinction ratios. Some of these experiments are described by
Lamarque et al. [1999] and W. D. Collins et al. (Forecasting aerosols
using a chemical transport model with assimilation of satellite aerosol
retrievals: Methodology for INDOEX, submitted to Journal of Geophysical
Research, 2000, hereinafter referred to as Collins et al., submitted
manuscript, 2000). We illustrate the method using assimilation of ozone
observations made by the Upper Atmosphere Research Satellite/Microwave
Limb Sounder in the three-dimensional chemistry transport model ROSE
[Research for Ozone in the Stratosphere and its Evolution; Rose and
Brasseur, 1989].
