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| Titel |
Predicting ozone profile shape from satellite UV spectra |
| VerfasserIn |
Jian Xu, Diego Loyola, Fabian Romahn, Adrian Doicu |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250152128
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| Publikation (Nr.) |
 EGU/EGU2017-16928.pdf |
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| Zusammenfassung |
| Identifying ozone profile shape is a critical yet challenging job for the accurate
reconstruction of vertical distributions of atmospheric ozone that is relevant to climate
change and air quality. Motivated by the need to develop an approach to reliably and
efficiently estimate vertical information of ozone and inspired by the success of
machine learning techniques, this work proposes a new algorithm for deriving ozone
profile shapes from ultraviolet (UV) absorption spectra that are recorded by satellite
instruments, e.g. GOME series and the future Sentinel missions. The proposed
algorithm formulates this particular inverse problem in a classification framework
rather than a conventional inversion one and places an emphasis on effectively
characterizing various profile shapes based on machine learning techniques. Furthermore, a
comparison of the ozone profiles from real GOME-2 data estimated by our algorithm
and the classical retrieval algorithm (Optimal Estimation Method) is performed. |
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