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With the increasing availability of observational data from different sources ata global level, joint analysis of these data is becoming especially attractive.For such an analysis – oftentimes with little prior knowledge about local andglobal interactions between the different observational variables at hand – anexploratory, data-driven analysis of the data may be of particular relevance. In the present work we used generalized additive models (GAM) in an exemplarystudy of spatio-temporal patterns in the tropospheric NO2-distributionderived from GOME satellite observations (1996 to 2001) at global scale. Wefocused on identifying correlations between NO2 and local wind fields,a quantity which is of particular interest in the analysis of spatio-temporalinteractions. Formulating general functional, parametric relationships betweenthe observed NO2 distribution and local wind fields, however, is difficult– if not impossible. So, rather than following a model-based analysis testingthe data for predefined hypotheses (assuming, for example, sinusoidal seasonaltrends), we used a GAM with non-parametric model terms to learn this functionalrelationship between NO2 and wind directly from the data. The NO2 observations showed to be affected by wind-dominated processes overlarge areas. We estimated the extent of areas affected by specific NO2emission 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 withprevious studies and alternative ways of analysing the time series. Overall,using a non-parametric model provided favorable means for a rapid inspectionof this large spatio-temporal NO2 data set, with less bias than parametricapproaches, and allowing to visualize dynamical processes of the NO2distribution at a global scale

atmospheric chemistry and physics

Analysing spatio-temporal patterns of the global NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;-distribution retrieved from GOME satellite observations using a generalized additive model