Long Lead ENSO Forecast Using an Adaptive Graph Convolutional Recurrent Neural Network

Jonnalagadda, Jahnavi; Hashemi, Mahdi

doi:10.3390/engproc2023039005

Cited by 1 publication

(2 citation statements)

References 30 publications

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“…The most accurate result at lag 12, when considering all months in the evaluation period (Fig. 5), is shown to be associated with predictions from the months of May to August when they were relatively superior lead times (Liu et al 2022(Liu et al , 2023Kim et al 2022;Mu et al 2021;Chen et al 2023b;Chen et al 2023;Jonnalagadda and Hashemi 2023). Ham et al (2019) applied a convolutional LSTM to achieve skillful ENSO forecasts for lead times of up to one and a half years.…”

Section: Resultsmentioning

confidence: 97%

“…In recent years there has been a burgeoning in the application of Artificial Neural Networks (ANN) in climate science, including for ENSO prediction (Kim et al 2022;Liu et al 2022;Zhou and Zhang 2023;Jonnalagadda and Hashemi 2023). ANNs have shown potential in capturing non-linear relationships intrinsic to ENSO (Ham et al 2019;Zhao and Sun 2022;Zhang et al 2022).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning with autoencoders and LSTM for ENSO forecasting

Ibebuchi,

Richman

2024

Clim Dyn

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El Niño Southern Oscillation (ENSO) is the prominent recurrent climatic pattern in the tropical Pacific Ocean with global impacts on regional climates. This study utilizes deep learning to predict the Niño 3.4 index by encoding non-linear sea surface temperature patterns in the tropical Pacific using an autoencoder neural network. The resulting encoded patterns identify crucial centers of action in the Pacific that serve as predictors of the ENSO mode. These patterns are utilized as predictors for forecasting the Niño 3.4 index with a lead time of at least 6 months using the Long Short-Term Memory (LSTM) deep learning model. The analysis uncovers multiple non-linear dipole patterns in the tropical Pacific, with anomalies that are both regionalized and latitudinally oriented that should support a single inter-tropical convergence zone for modeling efforts. Leveraging these encoded patterns as predictors, the LSTM - trained on monthly data from 1950 to 2007 and tested from 2008 to 2022 - shows fidelity in predicting the Niño 3.4 index. The encoded patterns captured the annual cycle of ENSO with a 0.94 correlation between the actual and predicted Niño 3.4 index for lag 12 and 0.91 for lags 6 and 18. Additionally, the 6-month lag predictions excel in detecting extreme ENSO events, achieving an 85% hit rate, outperforming the 70% hit rate at lag 12 and 55% hit rate at lag 18. The prediction accuracy peaks from November to March, with correlations ranging from 0.94 to 0.96. The average correlations in the boreal spring were as large as 0.84, indicating the method has the capability to decrease the spring predictability barrier.

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