for Atmospheric Chartography) on board ENVISAT are assimilated into the Global Modeling

and Assimilation Office (GMAO) constituent assimilation system for the period April 1-October 31, 2004.

SCIAMACHY is a near infrared spectrometer with nearly constant sensitivity to CO in the

Troposphere. As such, retrievals should contain significant information on variability of CO in the boundary layer,

where concentrations are significantly higher.

Carbon monoxide is retrieved from the 2260-2385 nm wavelength band (channel 8), in which the CO line

is relatively weak, and is superimposed by stronger water vapor and temperature lines. This results in

a large fraction of retrievals that are rejected as poor fits to the pre-selected vertical profiles. In

addition, a significant portion of the retrievals are cloud contaminated and the retrievals use a correction

factor from the ratio of retrieved to {\it a priori} methane column. The effect of this correction factor

on the retrieval error statistics is not yet well understood.

In spite of these limitations, initial assimilation results have demonstrated that significant

improvements to estimated CO fields can be made using SCIAMACHY observations. In the present work

we focus on the effect of the variable observation density on spatial variations in the

analysis error, and on improved estimates of observation errors for the cloud contaminated

observations. The former is determined through comparisions with in-situ data from CMDL and MOZAIC,

and we show the relationship between observation density and reduction in the CO field errors. The

cloud contaminated observation errors are estimated by tuning these observation errors separately from

the cloud free observations.

The implications for using SCIAMACHY or other near infrared measurements for source inversion are

are discussed.