A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean
文献类型：期刊论文
| 作者 | Ma, Yuanzhe1; Xie, Bowen1,4; Feng, Zhongkun1,4; Sun, Guimin3,4; Zhang, Cong2; Yang, Shuguo1 |
| 刊名 | JOURNAL OF OCEANOLOGY AND LIMNOLOGY
 |
| 出版日期 | 2025-05-23 |
| 页码 | 17 |
| 关键词 | sea surface temperature (SST) deep learning hybrid model prediction tropical Pacific Ocean |
| ISSN号 | 2096-5508 |
| DOI | 10.1007/s00343-025-4333-8 |
| 通讯作者 | Xie, Bowen(xiebw@mails.qust.edu.cn) |
| 英文摘要 | Sea surface temperature (SST) is an important ocean variable affecting climate change. It plays an important role in the interactions between the ocean and the atmosphere, and it also has an effect on the transport of heat, freshwater, and carbon. Therefore, accurate SST prediction is necessary for understanding climate change and protecting ocean ecosystems. In this study, we proposed a hybrid model to predict SST in the tropical Pacific Ocean based on two single deep-learning models. Results indicate that the proposed hybrid model shows superior prediction accuracy at all lead times compared to the single model. Specifically, during El Ni & ntilde;o periods, the root mean square error, mean absolute error, and Pearson correlation coefficient of the hybrid model forecasts were approximately 0.54 degrees C, 0.40 degrees C, and 0.98, respectively, while during La Ni & ntilde;a periods, these metrics were 0.55 degrees C, 0.39 degrees C, and 0.98, respectively. Notably, the hybrid model was able to capture the spatial distribution of SSTs during the El Ni & ntilde;o-Southern Oscillation (ENSO) events more accurately relative to a single model. Moreover, the prediction results of the hybrid model in different ocean regions exhibited lower prediction errors and higher correlations. The ablation experiments showed that sea surface wind (SSW) had different effects on SST at different times. By combining SST and SSW data, the model can make more-accurate predictions under different climatic conditions. The proposed hybrid model is able to predict SSTs quickly and accurately with better robustness during ENSO. |
| WOS关键词 | SEA-SURFACE TEMPERATURE ; PREDICTABILITY ; ALGORITHM ; NETWORK ; LONG ; ENSO |
| 资助项目 | Chinese Academy of Sciences |
| WOS研究方向 | Marine & Freshwater Biology ; Oceanography |
| 语种 | 英语 |
| WOS记录号 | WOS:001493382800001 |
| 出版者 | SCIENCE PRESS |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/202091]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Xie, Bowen |
| 作者单位 | 1.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao 266061, Peoples R China 2.Marine Sci Res Inst Shandong Prov, Qingdao 266104, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Yuanzhe,Xie, Bowen,Feng, Zhongkun,et al. A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean[J]. JOURNAL OF OCEANOLOGY AND LIMNOLOGY,2025:17. |
| APA | Ma, Yuanzhe,Xie, Bowen,Feng, Zhongkun,Sun, Guimin,Zhang, Cong,&Yang, Shuguo.(2025).A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean.JOURNAL OF OCEANOLOGY AND LIMNOLOGY,17. |
| MLA | Ma, Yuanzhe,et al."A deep learning-based hybrid model for improved SST prediction in the tropical Pacific Ocean".JOURNAL OF OCEANOLOGY AND LIMNOLOGY (2025):17. |
入库方式： OAI收割
来源：海洋研究所
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