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| Titel |
Using self-organising maps to explore ozone profile validation results – SCIAMACHY limb compared to ground-based lidar observations |
| VerfasserIn |
J. A. E. van Gijsel, R. Zurita-Milla, P. Stammes, S. Godin-Beekmann, T. Leblanc, M. Marchand, I. S. McDermid, K. Stebel, W. Steinbrecht, D. P. J. Swart |
| 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 ; 8, no. 5 ; Nr. 8, no. 5 (2015-05-06), S.1951-1963 |
| Datensatznummer |
250116354
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| Publikation (Nr.) |
 copernicus.org/amt-8-1951-2015.pdf |
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| Zusammenfassung |
| Traditional validation of atmospheric profiles is based on the
intercomparison of two or more data sets in predefined ranges or classes of a
given observational characteristic such as latitude or solar zenith angle.
In this study we trained a self-organising map (SOM) with a full time series
of relative difference profiles of SCIAMACHY limb v5.02 and lidar ozone
profiles from seven observation sites. Each individual observation
characteristic was then mapped to the obtained SOM to investigate to which
degree variation in this characteristic is explanatory for the variation
seen in the SOM map. For the studied data sets, altitude-dependent relations
for the global data set were found between the difference profiles and
studied variables. From the lowest altitude studied (18 km) ascending, the
most influencing factors were found to be longitude, followed by solar
zenith angle and latitude, sensor age and again solar zenith angle together
with the day of the year at the highest altitudes studied here (up to 45 km).
After accounting for both latitude and longitude, residual partial
correlations with a reduced magnitude are seen for various factors. However,
(partial) correlations cannot point out which (combination) of the factors
drives the observed differences between the ground-based and satellite ozone
profiles as most of the factors are inter-related. Clustering into three
classes showed that there are also some local dependencies, with for
instance one cluster having a much stronger correlation with the sensor age
(days since launch) between 36 and 42 km. The proposed SOM-based approach
provides a powerful tool for the exploration of differences between data sets
without being limited to a priori defined data subsets. |
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