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To answer fundamental questions about aerosols in our changing climate, wemust quantify both the current state of aerosols and how they are changing.Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensorshave provided quantitative information about global aerosol optical depth(AOD) for more than a decade, this period is still too short to create anaerosol climate data record (CDR). The Visible Infrared Imaging RadiometerSuite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, withadditional copies planned for future satellites. Can the MODIS aerosol datarecord be continued with VIIRS to create a consistent CDR? When compared toground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) hassimilar validation statistics as the MODIS Collection 6 (M_C6) product.However, the V_EDR and M_C6 are offset in regards to global AODmagnitudes, and tend to provide different maps of 0.55 μm AOD and0.55/0.86 μm-based Ångström Exponent (AE). One reason isthat the retrieval algorithms are different. Using the Intermediate FileFormat (IFF) for both MODIS and VIIRS data, we have tested whether we canapply a single MODIS-like (ML) dark-target algorithm on both sensors thatleads to product convergence. Except for catering the radiative transfer andaerosol lookup tables to each sensor's specific wavelength bands, the MLalgorithm is the same for both. We run the ML algorithm on both sensorsbetween March 2012 and May 2014, and compare monthly mean AOD time serieswith each other and with M_C6 and V_EDR products. Focusing on theMarch–April–May (MAM) 2013 period, we compared additional statistics thatinclude global and gridded 1° × 1° AOD and AE,histograms, sampling frequencies, and collocations with ground-based AERONET.Over land, use of the ML algorithm clearly reduces the differences betweenthe MODIS and VIIRS-based AOD. However, although global offsets are nearzero, some regional biases remain, especially in cloud fields and overbrighter surface targets. Over ocean, use of the ML algorithm actuallyincreases the offset between VIIRS and MODIS-based AOD (to ~ 0.025),while reducing the differences between AE. We characterize algorithmretrievability through statistics of retrieval fraction. In spite ofdifferences between retrieved AOD magnitudes, the ML algorithm will lead tosimilar decisions about "whether to retrieve" on each sensor. Finally, wediscuss how issues of calibration, as well as instrument spatial resolutionmay be contributing to the statistics and the ability to create a consistentMODIS → VIIRS aerosol CDR

Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance