Analysing spatio-temporal patterns of the global NO2-distribution retrieved from GOME satellite observations using a generalized additive model
Item
Title (Dublin Core)
Analysing spatio-temporal patterns of the global NO2-distribution retrieved from GOME satellite observations using a generalized additive model
Description (Dublin Core)
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. <br><br> In the present work we used generalized additive models (GAM) in an exemplary study of spatio-temporal patterns in the tropospheric NO<sub>2</sub>-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We focused on identifying correlations between NO<sub>2</sub> 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 NO<sub>2</sub> 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 NO<sub>2</sub> and wind directly from the data. <br><br> The NO<sub>2</sub> observations showed to be affected by wind-dominated processes over large areas. We estimated the extent of areas affected by specific NO<sub>2</sub> 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 NO<sub>2</sub> data set, with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO<sub>2</sub> distribution at a global scale.
Creator (Dublin Core)
Hayn, M.
Beirle, S.
Hamprecht, F. A.
Platt, U.
Menze, B. H.
Wagner, T.
Date (Dublin Core)
2018-01-15
Type (Dublin Core)
Text
Format (Dublin Core)
application/pdf
Identifier (Dublin Core)
10.5194/acp-9-6459-2009
https://acp.copernicus.org/articles/9/6459/2009/
Source (Dublin Core)
eISSN: 1680-7324
Language (Dublin Core)
eng
