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URN: urn:nbn:de:kobv:517-opus-44999
URL: http://opus.kobv.de/ubp/volltexte/2010/4499/

Hayn, Michael ; Beirle, Steffen ; Hamprecht, Fred A. ; Platt, Ulrich ; Menze, Björn H. ; Wagner, Thomas

Analysing spatio-temporal patterns of the global NO2-distribution retrieved from GOME satellite observations using a generalized additive model

Dokument 1.pdf (4.656 KB) (SHA-1:6744a1174ff858cda6479f6f8ea5e7c84e9ce5fc)

Kurzfassung in Englisch

With the increasing availability of observational data from different sources at a global level, joint analysis of these data is becoming especially attractive. For such an analysis – oftentimes with little prior knowledge about local and global interactions between the different observational variables at hand – an exploratory, data-driven analysis of the data may be of particular relevance. In the present work we used generalized additive models (GAM) in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We focused on identifying correlations between NO2 and local wind fields, a quantity which is of particular interest in the analysis of spatio-temporal interactions. Formulating general functional, parametric relationships between the observed NO2 distribution and local wind fields, however, is difficult – if not impossible. So, rather than following a modelbased analysis testing the data for predefined hypotheses (assuming, for example, sinusoidal seasonal trends), we used a GAM with non-parametric model terms to learn this functional relationship between NO2 and wind directly from the data. The NO2 observations showed to be affected by winddominated processes over large areas. We estimated the extent of areas affected by specific NO2 emission sources, and were able to highlight likely atmospheric transport “pathways”. General temporal trends which were also part of our model – weekly, seasonal and linear changes – showed to be in good agreement with previous studies and alternative ways of analysing the time series. Overall, using a non-parametric model provided favorable means for a rapid inspection of this large spatio-temporal NO2 data set, with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 distribution at a global scale.

Freie Schlagwörter (Englisch): Tropospheric nitrogen-dioxide , Air-pollution , Anthropogenic sources , NO2 , Validation
Institut: Institut für Geowissenschaften
DDC-Sachgruppe: Geowissenschaften
Dokumentart: c Postprint
Schriftenreihe: Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe, ISSN 1866-8372
Bandnummer: paper 131
Quelle: Atmospheric chemistry and physics 9 (2009), S. 6459 - 6477
Sprache: Englisch
Erstellungsjahr: 2009
Publikationsdatum: 16.07.2010
The article was originally published by COPERNICUS PUBLICATIONS:
Atmospheric Chemistry and Physics. - 9 (2009), S. 6459-6477
ISSN 1680-7316
Lizenz: Dieses Werk ist unter einer Creative Commons-Lizenz lizenziert.
Lizenz-Logo  Creative Commons - Attribution 3.0 unported

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