Assimilation of photochemically active species and a case analysis of UARSdata

We present a short overview of applications of estimation theory in atmosph eric chemistry and discuss some common methods of gridding and mapping of i rregular satellite observations of chemical constituents. It is shown that these methods are unable to produce truly synoptic maps of short-lived phot ochemically active species due to insufficient temporal and spatial density of satellite observations. The only way to overcome this limitation is to supplement observations with prior independent information given, for insta nce, by atmospheric numerical models and/or climatologies. Objective approa ches to combining such prior information with observations are commonly ref erred to as data assimilation. Mathematical basis of data assimilation know n as optimal estimation equations is presented following Lorenc [1986]. Two particular techniques of data assimilation, the variational method and the extended Kalman filter, are briefly described, and their applications to t ime-dependent numerical photochemical models are discussed. We investigate validity of the linear approximation which is utilized in both methods, pre sent time evolution of the linearization and covariance matrices, and discu ss some of their properties. On the basis of ideas of Fisher and Lary [1995 ] we then employ a trajectory model and a photochemical box model for assim ilation and mapping of the Upper Atmosphere Research Satellite (UARS) measu rements of chemical species. The assimilation is performed using the variat ional technique and the extended Kalman filter, and results of bo th method s are presented and discussed.