When machine learning deciphers the 'language' of atmospheric air masses

Mid-latitude circulation dynamics is often described in terms of weather regimes. Each pattern is given by a given combination of several synoptic objects (cyclones and anticyclones). Such intrication makes it arduous to quantify recurrence and intensity of climate extremes. Here we apply Latent Dirichlet Allocation (LDA), used for topic modeling in linguistics, to build a weather dictionary: we define daily maps of a gridded target observable as documents, and the grid-points composing the map as words. LDA provides a representation of documents in terms of a combination of spatial patterns named motifs, which are latent patterns inferred from the set of snapshots. For atmospheric data, we find that motifs correspond to pure synoptic objects (cyclones and anticyclones), that can be seen as building blocks of weather regimes. We show that LDA weights provide a natural way to characterize the impact of climate change on the recurrence of patterns associated with extreme events. 


Zoom link:  https://us06web.zoom.us/j/98286706234?pwd=bm1JUFVYcTJkaVl1VU55L0FiWDRIUT09


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Sandro Meloni

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