Assimilation of satellite data in 3-D CTMs using sub-optimal Kalman filter

We describe assimilation of global satellite observations of chemically important species in the global, three-dimensional chemistry-transport models. A sequential assimilation approach based on the sub-optimal Kalman filter was developed and implemented for use with general global chemistry-transport models. This method allows fast assimilation and mapping of satellite observations and provides estimates of analysis errors. Additionally, a number of diagnostic parameters are provided that allow assessment of uncertanties of the utilized model, such as biases and model error growth rate. The develped methodology has been used with a number of global CTMs (ROSE, MOZART2, MATCH) and applied to assimilation of various observations from several satellite-based instruments (ozone from UARS, CO from MAPS, CO from MOPITT, CO from IMG, aerosol extinction ratios from AVHRR & PATHFINDER). We present and discuss assimilation results from some of these studies.