Improving Seasonal Forecast Using Probabilistic Deep Learning

Improving Seasonal Forecast Using Probabilistic Deep Learning

Abstract The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast lim...

Full description

Bibliographic Details
Main Authors: Baoxiang Pan, Gemma J. Anderson, André Goncalves, Donald D. Lucas, Céline J. W. Bonfils, Jiwoo Lee
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2022-03-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
seasonal forecast
deep learning
variational inference
Online Access:https://doi.org/10.1029/2021MS002766

Internet

https://doi.org/10.1029/2021MS002766

Similar Items