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| Titel |
Global tropospheric ozone column retrievals from OMI data by means of neural networks |
| VerfasserIn |
A. Noia, P. Sellitto, F. Frate, J. Laat |
| 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 ; 6, no. 4 ; Nr. 6, no. 4 (2013-04-09), S.895-915 |
| Datensatznummer |
250017867
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| Publikation (Nr.) |
 copernicus.org/amt-6-895-2013.pdf |
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| Zusammenfassung |
| In this paper, a new neural network (NN) algorithm to retrieve the
tropospheric ozone column from Ozone Monitoring Instrument (OMI) Level 1b
data is presented. Such an algorithm further develops previous studies in order
to improve the following: (i) the geographical coverage of the NN, by extending its
training set to ozonesonde data from midlatitudes, tropics and poles; (ii)
the definition of the output product, by using tropopause pressure
information from reanalysis data; and (iii) the retrieval accuracy, by using
ancillary data (NCEP tropopause pressure and temperature profile, monthly
mean tropospheric ozone column from a satellite climatology) to better
constrain the tropospheric ozone retrievals from OMI radiances. The results
indicate that the algorithm is able to retrieve the tropospheric ozone column
with a root mean square error (RMSE) of about 5–6 DU in all the latitude
bands. The design of the new NN algorithm is extensively discussed,
validation results against independent ozone soundings and
chemistry/transport model (CTM) simulations are shown, and other
characteristics of the algorithm – i.e., its capability to detect
non-climatological tropospheric ozone situations and its sensitivity to the
tropopause pressure – are discussed. |
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