ArticleScience advances2023
A self-attention-based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Improved Skills for Predicting Sub-Seasonal to Seasonal Global Sea Surface Temperature.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Global distribution and amplitudes of internal solitary waves revealed by SWOT.Science advances · 2026Article
- Deep learning reveals enhanced ENSO predictability under historical anthropogenic forcing.Science advances · 2026Article
- Data-driven global ocean model resolving atmospherically forced ocean dynamics.Science advances · 2026Article
- Predicting upwelling dynamics in the South Sea of Java, Indonesia: A deep learning approach with ConvLSTM and 3D-CNN.MethodsX · 2026Article
- Tropical basin interactions reduce spring predictability barrier of ENSO in a deep learning model.Science advances · 2026Article
- AI for atmosphere-ocean sciences: advancements, challenges and ways forward.National science review · 2026Review
- Integrating Artificial Intelligence and Biotechnology to Enhance Cold Stress Resilience in Legumes.Plants (Basel, Switzerland) · 2025Review
- Projection of ENSO using observation-informed deep learning.Nature communications · 2025Article
- Data-driven global ocean modeling for seasonal to decadal prediction.Science advances · 2025Article
- A Review of Nanowire Devices Applied in Simulating Neuromorphic Computing.Nanomaterials (Basel, Switzerland) · 2025Review
- Predicting Atlantic and Benguela Niño events with deep learning.Science advances · 2025Article
- Advancing forecasting capabilities: A contrastive learning model for forecasting tropical cyclone rapid intensification.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Reconstructing the Tropical Pacific Upper Ocean Using Online Data Assimilation With a Deep Learning Model.Journal of advances in modeling earth systems · 2024Article
- Article
- Explained predictions of strong eastern Pacific El Niño events using deep learning.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large biases and uncertainties remain in real-time predictions of El Niño-Southern Oscillation (ENSO) using process-based dynamical models; recent advances in data-driven deep learning algorithms provide a promising mean to achieve superior skill in the tropical Pacific sea surface temperature (SST) modeling. Here, a specific self-attention-based neural network model is developed for ENSO predictions based on the much sought-after Transformer model, named 3D-Geoformer, which is used to predict three-dimensional (3D) upper-ocean temperature anomalies and wind stress anomalies. This purely data-driven and time-space attention-enhanced model achieves surprisingly high correlation skills for Niño 3.4 SST anomaly predictions made 18 months in advance and initiated beginning in boreal spring. Further, sensitivity experiments demonstrate that the 3D-Geoformer model can depict the evolution of upper-ocean temperature and the coupled ocean-atmosphere dynamics following the Bjerknes feedback mechanism during ENSO cycles. Such successful realizations of the self-attention-based model in ENSO predictions indicate its great potential for multidimensional spatiotemporal modeling in geoscience.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.