Machine learning prediction of the Madden-Julian oscillation
Abstract The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales...
Enregistré dans:
Auteurs principaux: | Riccardo Silini, Marcelo Barreiro, Cristina Masoller |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/fececcd91ff74ab4ac7e491179b51cad |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Publisher Correction: An oceanic pathway for Madden–Julian Oscillation influence on Maritime Continent Tropical Cyclones
par: Karthik Balaguru, et autres
Publié: (2021) -
Dynamic Characteristics of the Circulation and Diurnal Spatial Cycle of Outgoing Longwave Radiation in the Different Phases of the Madden–Julian Oscillation during the Formation of the South Atlantic Convergence Zone
par: Liviany P. Viana, et autres
Publié: (2021) -
Oscillations in deep-open-cells during winter Mediterranean cyclones
par: Huan Liu, et autres
Publié: (2021) -
Flow dependence of wintertime subseasonal prediction skill over Europe
par: C. Ardilouze, et autres
Publié: (2021) -
NAO predictability from external forcing in the late 20th century
par: Jeremy M. Klavans, et autres
Publié: (2021)