Assimilation of satellite observations of long-lived chemical species in global chemistry transport models

Use of data assimilation techniques such as optimal interpolation or the Ka lman filter in global chemistry transport models (CTM) is becoming more com mon. However: owing to high computational requirements, it is often difficu lt to apply these techniques to multidimensional models containing extensiv e photochemical schemes. We present a sequential assimilation approach deve loped for use with general global chemistry transport models. It allows fas t assimilation and mapping of satellite observations and provides estimates of analysis errors. The suggested data assimilation scheme evolved from th e 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 M enard and Chang [2000]. One of the most important features of the developed scheme is its ability to routinely estimate variance of the analysis and t o predict variance evolution in the model. The developed technique (or its variants) has been successfully interfaced with a number of different globa l models and used for assimilation of several types of measurements. includ ing aerosol extinction ratios. Some of these experiments are described by L amarque et al. [1999] and W. D. Collins et al. (Forecasting aerosols using a chemical transport model with assimilation of satellite aerosol retrieval s: Methodology for INDOEX, submitted to Journal of Geophysical Research, 20 00, 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 Stra tosphere and its Evolution; Rose and Brasseur, 1989].