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In this paper, we introduce the usage of a newly developed spectraldecomposition technique – combined maximum covariance analysis (CMCA) – inthe spatiotemporal comparison of four satellite data sets and ground-basedobservations of aerosol optical depth (AOD). This technique is based oncommonly used principal component analysis (PCA) and maximum covarianceanalysis (MCA). By decomposing the cross-covariance matrix between the jointsatellite data field and Aerosol Robotic Network (AERONET) station data,both parallel comparison across different satellite data sets and theevaluation of the satellite data against the AERONET measurements aresimultaneously realized. We show that this new method not only confirms theseasonal and interannual variability of aerosol optical depth, aerosol-source regions and events represented by different satellite data sets, butalso identifies the strengths and weaknesses of each data set in capturingthe variability associated with sources, events or aerosol types.Furthermore, by examining the spread of the spatial modes of differentsatellite fields, regions with the largest uncertainties in aerosolobservation are identified. We also present two regional case studies thatrespectively demonstrate the capability of the CMCA technique in assessingthe representation of an extreme event in different data sets, and inevaluating the performance of different data sets on seasonal and interannualtimescales. Global results indicate that different data sets agreequalitatively for major aerosol-source regions. Discrepancies are mostlyfound over the Sahel, India, eastern and southeastern Asia. Results for easternEurope suggest that the intense wildfire event in Russia  during summer 2010was less well-represented by SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and OMI (Ozone Monitoring Instrument), which might be due tomisclassification of smoke plumes as clouds. Analysis for the Indiansubcontinent shows that here SeaWiFS agrees best with AERONET in terms ofseasonality for both the Gangetic Basin and southern India, while oninterannual timescales it has the poorest agreement

atmospheric measurement techniques

Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis