Using self-organising maps to explore ozone profile validation results – SCIAMACHY limb compared to ground-based lidar observations - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Atmospheric Measurement Techniques Année : 2015

Using self-organising maps to explore ozone profile validation results – SCIAMACHY limb compared to ground-based lidar observations

Résumé

Traditional validation of atmospheric profiles is based on the intercomparison of two or more datasets in predefined ranges or classes of a given observational characteristic such as latitude or solar zenith angle. In this study we train a self organizing 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 is 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 datasets, altitude-dependent relations for the global dataset 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). 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. It was shown that the proposed approach provides a powerful tool for the exploring of differences between datasets without being limited to a-priori defined data subsets.
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Dates et versions

hal-00986167 , version 1 (06-05-2015)

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J. A. E. van Gijsel, R. Zurita-Milla, P. Stammes, Sophie Godin-Beekmann, Thierry Leblanc, et al.. Using self-organising maps to explore ozone profile validation results – SCIAMACHY limb compared to ground-based lidar observations. Atmospheric Measurement Techniques, 2015, 8, pp.1951-1963. ⟨10.5194/amt-8-1951-2015⟩. ⟨hal-00986167⟩
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