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Machine learning prediction of the Madden-Julian oscillation

Earth Sciences

Machine learning prediction of the Madden-Julian oscillation

R. Silini, M. Barreiro, et al.

Discover how researchers Riccardo Silini, Marcelo Barreiro, and Cristina Masoller are pushing the boundaries of weather forecasting! By harnessing the power of artificial neural networks, they achieved a remarkable MJO prediction skill of up to 60 days, challenging traditional climate models. This groundbreaking study reveals the potential of AI in understanding tropical atmospheric variability.

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Playback language: English
Abstract
Forecasting extreme weather events on sub-seasonal timescales (10 days to 3 months) is challenging due to the complex nature of atmospheric phenomena. This paper explores the use of artificial neural networks (ANNs) – specifically, a feed-forward neural network (FFNN) and an autoregressive recurrent neural network (AR-RNN) – to predict the Madden-Julian Oscillation (MJO), a dominant mode of tropical atmospheric variability. The ANNs achieved a competitive MJO prediction skill of approximately 26-27 days on average, comparable to state-of-the-art climate models. However, for specific initial phases and seasons, the prediction skill extended to 60 days or more. While the ANNs accurately predicted the MJO phase, they underestimated its amplitude.
Publisher
npj Climate and Atmospheric Science
Published On
Nov 25, 2021
Authors
Riccardo Silini, Marcelo Barreiro, Cristina Masoller
Tags
Madden-Julian Oscillation
artificial neural networks
weather forecasting
tropical atmospheric variability
predictive modeling

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