Analysing spatio-temporal patterns of the global NO2-distribution [NO subscript 2 -distribution] retrieved from GOME satellite observations using a generalized additive model

Abstract

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 [NO subscript 2 -distribution] derived from GOME satellite observations (1996 to 2001) at global scale. We focused on identifying correlations between NO2 [NO subscript 2] 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 [NO subscript 2] distribution and local wind fields, however, is difficult – if not impossible. So, rather than following a model-based 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 [NO subscript 2] and wind directly from the data.

The NO2 [NO subscript 2] observations showed to be affected by wind-dominated processes over large areas. We estimated the extent of areas affected by specific NO2 [NO subscript 2] 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 [NO subscript 2] data set, with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 [NO subscript 2] distribution at a global scale.