ENSO analysis and prediction using deep learning: A review

Wang, Gai-Ge; Cheng, Honglei; Zhang, Yiming; Yu, Hui Jing

doi:10.1016/j.neucom.2022.11.078

Cited by 19 publications

(9 citation statements)

References 130 publications

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“…However, the result in Fig. 7 from April to May exceeds the correlations achieved in other studies and reinforces the capability of deep learning predictive models to improve ENSO predictions during spring (Gupta et al 2020;Wang et al 2022). Key processes contributing to the SPB include the seasonal shift in the position of the Intertropical Convergence Zone and the associated changes in oceanic and atmospheric circulation patterns (Duan and Wei 2013).…”

Section: Resultssupporting

confidence: 65%

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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Section: Resultssupporting

confidence: 65%

Deep learning with autoencoders and LSTM for ENSO forecasting

Ibebuchi,

Richman

2024

Clim Dyn

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“…1). Unlike traditional machine learning, deep learning does not require manual extraction of features, but rather the training of models from large amounts of data (Table 1) [14]. Different deep learning networks are used for different cases and data types.…”

Section: Overview Of Deep Learningmentioning

confidence: 99%

Deep Learning in Dental Radiographic Imaging

Kim

2024

J Korean Acad Pediatr Dent

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Deep learning algorithms are becoming more prevalent in dental research because they are utilized in everyday activities. However, dental researchers and clinicians find it challenging to interpret deep learning studies. This review aimed to provide an overview of the general concept of deep learning and current deep learning research in dental radiographic image analysis. In addition, the process of implementing deep learning research is described. Deep-learning-based algorithmic models perform well in classification, object detection, and segmentation tasks, making it possible to automatically diagnose oral lesions and anatomical structures. The deep learning model can enhance the decision-making process for researchers and clinicians. This review may be useful to dental researchers who are currently evaluating and assessing deep learning studies in the field of dentistry.

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“…El Niño-Southern Oscillation (ENSO) is a critically important phenomenon that is defined based on anomaly warming and fluctuation of atmospheric pressure over the tropical Pacific Ocean that occurs every 2-7 years 1,2 . El Niño and La Niña devastate the ecology of the coastal zones, affect the global atmospheric circulation, and can contribute to severe weather conditions over the globe 3 .…”

Section: Introductionmentioning

confidence: 99%

Multimodal ENSO Forecast

Naisipour,

Saeedpanah,

Adib

2024

Preprint

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Predicting the onset of the El Niño Southern Oscillation (ENSO) in the current rapidly changing climate could help save thousands of lives annually. Since the variability of this phenomenon is increasing, its prediction is becoming more challenging in the post-2000 era. Hence, we present a novel Multimodal ENSO Forecast (MEF) method for predicting ENSO up to two years for the post-2000 condition. The model receives a Sea Surface Temperature (SST) anomaly video, a heat content (HC) anomaly video, and an augmented time series to predict the Niño 3.4 Index. We utilize a multimodal neural network to elicit all the embedded spatio-temporal information in the input data. The model consists of a 3D Convolutional Neural Network (3DCNN) that deals with short-term videos and a Time Series Informer (TSI) that finds the base signal in long-term time series. Finally, an adaptive ensemble module (AEM) delivers an ensemble prediction based on uncertainty analysis. We successfully tested the model against observational data and the state-of-the-art CNN model for a long and challenging period from 2000 to 2020, ensuring its validity and reliability as a reliable tool for predicting ENSO in the upcoming Earth’s climate.

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ENSO analysis and prediction using deep learning: A review

Cited by 19 publications

References 130 publications

Deep learning with autoencoders and LSTM for ENSO forecasting

Deep learning with autoencoders and LSTM for ENSO forecasting

Deep Learning in Dental Radiographic Imaging

Multimodal ENSO Forecast

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