Meteorology and climatology are fields of great relevance, not only from a purely scientific point of view, but also as they affect everyday life. Hence, gaining a deeper understanding on these areas is necessary. In this project, to do so, the novel functional networks approach is going to be followed to understand the spatial and temporal propagation of three meteorological variables: temperature, difference between temperature, and dew point and wind speed. To do so, first their associated time series are going to be extracted from airport meteorological reports for 249 airports from 2019 to 2022. Then, after time series are reconstructed, causality relations are going to be checked between time series of the same variable at different spatial locations, using Granger causality. Finally, complex networks are going to be built where nodes are the spatial locations, and an edge exists between two nodes if a causality is found between them. An analysis of the results will allow us to make hypothesis about the physical phenomena underlying the found causalities and determine strong points and limitations of the methodology. Finally, further work for this project, both from a methodological and applicational point of view, will be commented.
Supervisor: Massimiliano Zanin
Jury: Jose Javier Ramasco, Sandro Meloni, Massimiliano Zanin
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