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Spatiotemporal large-scale networks shaped by air mass movements

Abstract : The movement of atmospheric air masses can be seen as a continuous and generally complex flow of gases and particles hovering over our planet. It can however be locally simplified by considering three-dimensional trajectories of air masses connecting distant areas of the globe during a given period of time. In this paper, we present a mathematical framework to construct spatial and spatiotemporal networks where the nodes are the subsets of a partition of a geographical area and the links between these nodes are inferred from sampled trajectories of air masses passing over and across the nodes. We propose different estimators of link intensities relying on different bio-physical hypotheses and covering adjustable time periods. This approach leads to a new class of spatiotemporal networks characterized by adjacency matrices giving, e.g., the probability of connection between distant areas during a chosen period of time. To illustrate the effectiveness of this approach, we applied it to characterize tropospheric connectivity in two real geographical contexts: the watersheds of the French region Provence-Alpes-Côte d'Azur and the coastline of the Mediterranean Sea. The analysis of the constructed networks allowed identifying a marked seasonal pattern in air mass movements in the two study areas. The networks constructed from air mass trajectories can be used to investigate issues, e.g., in aerobiology and epidemiology of airborne plant pathogens. Similar networks could be estimated from other types of trajectories, such as animal trajectories, to characterize connectivity between different components of the landscape where the animals live.
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Maria Choufany, Davide Martinetti, Rachid Senoussi, Cindy E. Morris, Samuel Soubeyrand. Spatiotemporal large-scale networks shaped by air mass movements. 2019. ⟨hal-02790095⟩



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