Spatiotemporal Analysis

Abstract

Several aspects of spatiotemporal analysis of trace gases have been discussed, including visualization, validation, and different spatiotemporal analysis methods, such as missing data handling, atmospheric transport modeling, inverse modeling, machine learning methods, etc. Each one of them explores the characteristics of atmospheric trace gases like CO<inf>2</inf>,CH<inf>4</inf>, NO<inf>2</inf>, etc., in different application domains and help to under-stand the global and local atmospheric processes worldwide. Satellite-borne trace gas data, combined with various ground-based monitoring networks, are the foundation that enables a broad spectrum of their spatiotemporal analysis. Different investigations around the globe have been mentioned here in order to show traditional methods for the spatiotemporal analysis of trace gases and investigate the recent extensions created with data fusion approaches in the future. Though the discussion is not exhaustive, it gives the initial pointers for further exploration. © Springer Nature Switzerland AG 2022.