Year: 2026
Type: article
Abstract: Sea surface temperature (SST) serves as a critical indicator of oceanic thermodynamic processes and climate variability, exerting essential influence on ocean fronts, typhoon tracks, and monsoon evolution. Nevertheless, owing to the highly nonlinear and complex multi-scale characteristics of SST, achieving accurate spatiotemporal forecasting remains a formidable challenge. To address this issue, we proposed an enhanced Transformer architecture that incorporates a Coordinate Attention (CA) module and an Adaptive Fusion (AD) module, enabling the joint extraction and integration of temporal and spatial features. The proposed model is evaluated through SST prediction experiments over a localized region of the South China Sea with lead times of 1, 7, 15, and 30 days. Results indicate that our approach consistently outperforms baseline models across multiple evaluation metrics. Moreover, generalization experiments conducted on datasets from regions with diverse latitudes and climate regimes further demonstrate the model’s robustness and adaptability in terms of both accuracy and stability.
DOI: 10.3390/jmse14020120
Authors: Naihua Ji, Yue Dai, Menglei Xia, Shuai Guo, Tianhui Qiu, Lu Yu
Institutions: Qingdao University of Technology, PLA Navy Submarine Academy
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $3,137
Awards: G2004234398, G2738460470
OpenAlex ID: W7118836705