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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 10 | Copyright
Atmos. Meas. Tech., 8, 4083-4110, 2015
https://doi.org/10.5194/amt-8-4083-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 07 Oct 2015

Research article | 07 Oct 2015

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

R. C. Levy1, L. A. Munchak1,2, S. Mattoo1,2, F. Patadia1,3, L. A. Remer4, and R. E. Holz5 R. C. Levy et al.
  • 1NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 2SSAI, Lanham, MD, USA
  • 3GESTAR, Morgan State University, Columbia, MD, USA
  • 4UMBC/JCET, Baltimore, MD, USA
  • 5SSEC, University of Wisconsin, Madison, WI, USA

Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March–April–May (MAM) 2013 period, we compared additional statistics that include 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 between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ~ 0.025), while reducing the differences between AE. We characterize algorithm retrievability through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.

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Aerosol optical depth (AOD) is an essential climate variable, so we seek to create a long-term AOD record. From MODIS, we have 15+ years, which we want to continue with VIIRS. Accounting for instrumental difference, we have developed a MODIS-like algorithm for VIIRS, and applied it to overlapping 2-year time period. In general, the two data sets are similar, except for VIIRS being high-biased over ocean. We discuss the impacts of calibration, resolution, and sampling on the results.
Aerosol optical depth (AOD) is an essential climate variable, so we seek to create a long-term...
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