TES carbon monoxide validation with DACOM aircraft measurements during INTEX-B 2006

[1] Validation of Tropospheric Emission Spectrometer (TES) tropospheric CO profiles with in situ CO measurements from the Differential Absorption CO Measurement (DACOM) instrument during the Intercontinental Chemical Transport Experiment (INTEX)-B campaigns in March to May 2006 are presented. For each identified DACOM CO profile, one to three TES CO profiles are selected closest in location to the small area that the DACOM profile covers. The time differences between the comparison profiles are within 2 hours. The DACOM CO vertical profiles are adjusted by applying nearest coincident TES averaging kernels and the a priori profiles. This step accounts for the effect of the vertical resolution of the TES CO retrievals and removes the influence of the a priori assumptions in the comparisons. Comparison statistics for data taken near Houston in March 2006 show good agreement between TES and the adjusted DACOM CO profiles in the lower and middle troposphere with a correlation coefficient of 0.87. On average, the TES CO volume mixing ratio profile is 0–10% lower than the adjusted DACOM CO profile from the lower to middle troposphere. This is within the 10–20% standard deviations of the TES or DACOM CO profiles taken in the Houston area. The comparisons of TES and DACOM CO profiles near Hawaii and Anchorage in April to May 2006 are not as good. In these regions the aircraft DACOM CO profiles are characterized by plumes or enhanced CO layers, consistent with known features in the tracer fields due to transpacific transport of polluted air parcels originating from East Asia. Although TES observations over the Pacific region also show localized regions of enhanced CO, the coincidence criteria for obtaining good comparisons with aircraft measurements are challenging. The meaning of validation comparisons in profile portions where TES retrievals have little sensitivity is addressed. Examinations of characteristic parameters in TES retrievals are important in data applications.