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| Titel |
Quantification of uncertainty in aerosol optical thickness retrieval arising from aerosol microphysical model and other sources, applied to Ozone Monitoring Instrument (OMI) measurements |
| VerfasserIn |
A. Määttä, M. Laine, J. Tamminen, J. P. Veefkind |
| 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 ; 7, no. 5 ; Nr. 7, no. 5 (2014-05-08), S.1185-1199 |
| Datensatznummer |
250115751
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| Publikation (Nr.) |
 copernicus.org/amt-7-1185-2014.pdf |
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| Zusammenfassung |
| Satellite instruments are nowadays successfully utilised for measuring
atmospheric aerosol in many applications as well as in research. Therefore,
there is a growing need for rigorous error characterisation of the
measurements. Here, we introduce a methodology for quantifying the
uncertainty in the retrieval of aerosol optical thickness (AOT). In
particular, we concentrate on two aspects: uncertainty due to aerosol
microphysical model selection and uncertainty due to imperfect forward
modelling. We apply the introduced methodology for aerosol optical thickness
retrieval of the Ozone Monitoring Instrument (OMI) on board NASA's Earth
Observing System (EOS) Aura satellite, launched in 2004. We apply statistical
methodologies that improve the uncertainty estimates of the aerosol optical
thickness retrieval by propagating aerosol microphysical model selection and
forward model error more realistically. For the microphysical model selection
problem, we utilise Bayesian model selection and model averaging methods.
Gaussian processes are utilised to characterise the smooth systematic
discrepancies between the measured and modelled reflectances (i.e.
residuals). The spectral correlation is composed empirically by exploring a
set of residuals. The operational OMI multi-wavelength aerosol retrieval
algorithm OMAERO is used for cloud-free, over-land pixels of the OMI
instrument with the additional Bayesian model selection and model discrepancy
techniques introduced here. The method and improved uncertainty
characterisation is demonstrated by several examples with different aerosol
properties: weakly absorbing aerosols, forest fires over Greece and Russia,
and Sahara desert dust. The statistical methodology presented is general; it
is not restricted to this particular satellite retrieval application. |
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