Physical Knowledge Analytic Framework for Sea Surface Temperature Prediction

• Published in: IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-16
• Main Authors: Meng, Yuxin, Gao, Feng, Rigall, Eric, Dong, Junyu, Du, Qian
• Format: Article
• Language: English
• Subjects: Biological system modeling; Data models; Deep learning; Feature extraction; Generative adversarial network (GAN); Long short term memory; numerical model; Numerical models; Oceans; physical knowledge; Predictive models; Sea surface temperature; sea surface temperature (SST); Semantics; spatial-temporal prediction
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3469238

Recently, the methods that combine the merits of the numerical model and the deep learning to improve the prediction accuracy of the sea surface temperature (SST) have received considerable attention. Existing methods usually apply the output of the numerical model as the physical knowledge to guide the training of the deep learning models. However, the physical knowledge in the observed data has not been fully exploited. With the development of observational instruments and techniques, an increasing amount of observational data has been collected. These data can be utilized for the exploration of physical knowledge. Toward this end, we propose novel scheme for SST prediction, which applies generative adversarial networks (GANs) to analyze the physical knowledge in the historical data. In particular, two GAN models are trained with numerical model data and observed data separately. Afterward, the physical knowledge is extracted from the observed data which is not contained in the data generated by the numerical model by comparing the learned physical feature from the two pre-trained GAN models. Finally, to validate the relevance of the physical knowledge which we have discovered, the extracted features are added into the numerical model data which are called newly corrected data. Besides, we train two spatial-temporal models over the newly corrected dataset and the original numerical model data for SST prediction, respectively. The experimental results show that the newly corrected dataset performs better than using the original numerical model for SST prediction.