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| Titel |
Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance |
| VerfasserIn |
R. C. Levy, L. A. Munchak, S. Mattoo, F. Patadia, L. A. Remer, R. E. Holz |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 10 ; Nr. 8, no. 10 (2015-10-07), S.4083-4110 |
| Datensatznummer |
250116626
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| Publikation (Nr.) |
 copernicus.org/amt-8-4083-2015.pdf |
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| Zusammenfassung |
| 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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