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dc.contributor.authorSpurr, R.
dc.contributor.authorLoyola, D.
dc.contributor.authorThomas, W.
dc.contributor.authorBalzer, W.
dc.contributor.authorMikusch, E.
dc.contributor.authorAberle, B.
dc.contributor.authorSlijkhuis, S.
dc.contributor.authorRuppert, T.
dc.contributor.authorVan Roozendael, M.
dc.contributor.authorLambert, J.-C.
dc.contributor.authorSoebijanta, T.
dc.contributor.editor
dc.date2005
dc.date.accessioned2016-12-07T10:36:05Z
dc.date.available2016-12-07T10:36:05Z
dc.identifier.urihttps://orfeo.kbr.be/handle/internal/4605
dc.descriptionThe global ozone monitoring experiment (GOME) was launched in April 1995, and the GOME data processor (GDP) retrieval algorithm has processed operational total ozone amounts since July 1995. GDP level 1-to-2 is based on the two-step differential optical absorption spectroscopy (DOAS) approach, involving slant column fitting followed by air mass factor (AMF) conversions to vertical column amounts. We present a major upgrade of this algorithm to version 3.0. GDP 3.0 was implemented in July 2002, and the 9-year GOME data record from July 1995 to December 2004 has been processed using this algorithm. The key component in GDP 3.0 is an iterative approach to AMF calculation, in which AMFs and corresponding vertical column densities are adjusted to reflect the true ozone distribution as represented by the fitted DOAS effective slant column. A neural network ensemble is used to optimize the fast and accurate parametrization of AMFs. We describe results of a recent validation exercise for the operational version of the total ozone algorithm; in particular, seasonal and meridian errors are reduced by a factor of 2. On a global basis, GDP 3.0 ozone total column results lie between -2% and +4% of ground-based values for moderate solar zenith angles lower than 70°. A larger variability of about +5% and -8% is observed for higher solar zenith angles up to 90°.
dc.languageeng
dc.titleGOME level 1-to-2 data processor version 3.0: a major upgrade of the GOME/ERS-2 total ozone retrieval algorithm
dc.typeArticle
dc.subject.frascatiEarth and related Environmental sciences
dc.audienceScientific
dc.subject.freeAlgorithms
dc.subject.freeLight absorption
dc.subject.freeNeural networks
dc.subject.freeOptimization
dc.subject.freeOzone
dc.subject.freeSpectroscopy
dc.subject.freeData processor
dc.subject.freeOzone monitoring
dc.subject.freeRetrieval algorithm
dc.subject.freeOptical data processing
dc.subject.freeozone
dc.subject.freeair pollutant
dc.subject.freealgorithm
dc.subject.freearticle
dc.subject.freeartificial intelligence
dc.subject.freeautomated pattern recognition
dc.subject.freecomputer program
dc.subject.freeenvironmental monitoring
dc.subject.freeevaluation
dc.subject.freeinformation retrieval
dc.subject.freeinstrumentation
dc.subject.freemethodology
dc.subject.freespectroscopy
dc.subject.freeAir Pollutants
dc.subject.freeAlgorithms
dc.subject.freeArtificial Intelligence
dc.subject.freeEnvironmental Monitoring
dc.subject.freeInformation Storage and Retrieval
dc.subject.freeOzone
dc.subject.freePattern Recognition, Automated
dc.subject.freeSoftware
dc.subject.freeSpectrum Analysis
dc.source.titleApplied Optics
dc.source.volume44
dc.source.issue33
dc.source.page7196-7209
Orfeo.peerreviewedYes
dc.identifier.doi10.1364/AO.44.007196
dc.identifier.scopus2-s2.0-29144476425


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