Skip to Main content Skip to Navigation
New interface
Journal articles

Learning a weather dictionary of atmospheric patterns using Latent Dirichlet Allocation

Abstract : 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.
Complete list of metadata
Contributor : Faranda Davide Connect in order to contact the contributor
Submitted on : Friday, June 11, 2021 - 3:36:24 PM
Last modification on : Friday, October 21, 2022 - 3:50:20 AM
Long-term archiving on: : Sunday, September 12, 2021 - 8:00:37 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Lucas Fery, Berengere Dubrulle, Berengere Podvin, Flavio Pons, Davide Faranda. Learning a weather dictionary of atmospheric patterns using Latent Dirichlet Allocation. Geophysical Research Letters, 2022, 49, pp.e2021GL096184. ⟨10.1029/2021GL096184⟩. ⟨hal-03258523⟩



Record views


Files downloads