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This study tests a novel methodology to add value to satellite data sets. Thismethodology, data fusion, is similar to data assimilation, except that thebackground model-based field is replaced by a satellite data set, in this caseAIRS (Atmospheric Infrared Sounder) carbon monoxide (CO) measurements. Theobservational information comes from CO measurements with lower spatialcoverage than AIRS, namely, from TES (Tropospheric Emission Spectrometer) andMLS (Microwave Limb Sounder). We show that combining these data sets with datafusion uses the higher spectral resolution of TES to extend AIRS COobservational sensitivity to the lower troposphere, a region especiallyimportant for air quality studies. We also show that combined CO measurementsfrom AIRS and MLS provide enhanced information in the UTLS (uppertroposphere/lower stratosphere) region compared to each product individually.The combined AIRS–TES and AIRS–MLS CO products are validated against DACOM(differential absorption mid-IR diode laser spectrometer) in situ COmeasurements from the INTEX-B (Intercontinental Chemical TransportExperiment: MILAGRO and Pacific phases) field campaign and in situ data fromHIPPO (HIAPER Pole-to-Pole Observations) flights. The data fusion resultsshow improved sensitivities in the lower and upper troposphere (20–30%and above 20%, respectively) as compared with AIRS-only version 5 COretrievals, and improved daily coverage compared with TES and MLS CO data

Global carbon monoxide products from combined AIRS, TES and MLS measurements on A-train satellites