El Nino Prediction Based on Weather Forecast and Geographical Time-series Data
This paper proposes a novel framework for enhancing the prediction accuracy and lead time of El Niño events, crucial for mitigating their global climatic, economic, and societal impacts. Traditional prediction models often rely on oceanic and atmospheric indices, which may lack the granularity or dynamic interplay captured by comprehensive meteorological and geographical datasets. Our framework integrates real-time global weather forecast data with anomalies, subsurface ocean heat content, and atmospheric pressure across various temporal and spatial resolutions. Leveraging a hybrid deep learning architecture that combines a Convolutional Neural Network (CNN) for spatial feature extraction...
| Authors | Viet Trinh, Ha-Vy Luu, Quoc-Khiem Nguyen-Pham et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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