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| Titel |
An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals |
| VerfasserIn |
E. J. Hyer, J. S. Reid, J. Zhang |
| 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 ; 4, no. 3 ; Nr. 4, no. 3 (2011-03-01), S.379-408 |
| Datensatznummer |
250001791
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| Publikation (Nr.) |
 copernicus.org/amt-4-379-2011.pdf |
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| Zusammenfassung |
| MODIS Collection 5 retrieved aerosol optical depth (AOD) over land
(MOD04/MYD04) was evaluated using 4 years of matching AERONET observations,
to assess its suitability for aerosol data assimilation in numerical weather
prediction models. Examination of errors revealed important sources of
variation in random errors (e.g., atmospheric path length, scattering angle
"hot spot"), and systematic biases (e.g., snow and cloud contamination,
surface albedo bias). A set of quality assurance (QA) filters was developed
to avoid conditions with potential for significant AOD error. An empirical
correction for surface boundary condition using the MODIS 16-day albedo
product captured 25% of the variability in the site mean bias at low AOD.
A correction for regional microphysical bias using the AERONET fine/coarse
partitioning information increased the global correlation between MODIS and
AERONET from r2 = 0.62–0.65 to r2 = 0.71–0.73. Application of these
filters and corrections improved the global fraction of MODIS AOD within
(0.05 ± 20%) of AERONET to 77%, up from 67% using only built-in
MODIS QA. The compliant fraction in individual regions was improved by as
much as 20% (South America). An aggregated Level 3 product for use in a
data assimilation system is described, along with a prognostic error model
to estimate uncertainties on a per-observation basis. The new filtered and
corrected Level 3 product has improved performance over built-in MODIS QA
with less than a 15% reduction in overall data available for data
assimilation. |
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