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| DOI | 10.1007/s00382-024-07119-z |
| ENSONet: a data-driven ENSO forecasting model with concise spatial location learning parameters and temporal embedding | |
| 发表日期 | 2024 |
| ISSN | 0930-7575 |
| EISSN | 1432-0894 |
| 英文摘要 | The El Nino-Southern Oscillation (ENSO) is a highly notable climate phenomenon with significant implications for global weather patterns and climate change. Accurately predicting ENSO holds substantial scientific and economic value. However, due to the intricate relationship between the evolution of the oceans and the atmosphere across spatial and temporal scales, currently, the most advanced physically based dynamical models struggle to deliver skillful predictions beyond a 1-year. Deep learning models often prioritize complex module stacking, neglecting the incorporation of crucial spatial and temporal information and providing inaccurate predictions over long distances. To overcome these challenges, ENSONet is proposed in this study. It identifies the Nino high correlation region and temporal relationships by designing concise spatial location learning parameters and temporal embedding. The progressive prediction architecture employs multiple learning to enhance long-term prediction accuracy and effective distance. Additionally, novel prediction-relevant regions are discovered from ocean features using spatial and temporal attention modules, and intricate prediction patterns are learned by finely modeling spatio-temporal relationships. Extensive experiments on real-world datasets demonstrate that ENSONet identifies regions directly associated with the Nino index and uncovers new regions of interest through continuous learning. By successfully predicting changes in ENSO from 1984 to 2023, it showcases its proficiency in learning complex predictive patterns. In conclusion, ENSONet not only expands the prediction horizon to the 18th month but also demonstrates a remarkable enhancement in prediction accuracy, with an average improvement of 28.99%, thus achieving state-of-the-art performance. |
| 英文关键词 | ENSO prediction; Deep learning; Spatial and temporal data |
| 语种 | 英语 |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| WOS类目 | Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001173235200001 |
| 来源期刊 | CLIMATE DYNAMICS
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/298941 |
| 作者单位 | Tsinghua University |
| 推荐引用方式 GB/T 7714 | . ENSONet: a data-driven ENSO forecasting model with concise spatial location learning parameters and temporal embedding[J],2024. |
| APA | (2024).ENSONet: a data-driven ENSO forecasting model with concise spatial location learning parameters and temporal embedding.CLIMATE DYNAMICS. |
| MLA | "ENSONet: a data-driven ENSO forecasting model with concise spatial location learning parameters and temporal embedding".CLIMATE DYNAMICS (2024). |
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