Deep Neural Network for Sea Surface Temperature Prediction: Conclusions & Future Work

Table of Links

• Abstract and Intro
• Background
• Proposed Method
• Experimental Results and Analysis
• Conclusions and Future Work
• References

V. CONCLUSIONS AND FUTURE WORK

In this paper, we present a SST prediction approach based on physical knowledge correction, which utilizes historical observed data to refine and adjust the physical component in the numerical model data. Specifically, a prior network was employed to extract physical knowledge from the observed data. Subsequently, we generated physics-enhanced SST by applying the pretrained prior network over numerical model data. Finally, the generated data were used to train the ConvLSTM network for SST prediction. Additionally, the physical knowledge-based enhanced data were leveraged to train the ConvLSTM network, which further improved the prediction performance. The proposed method achieved the best performance compared to six state-of-the-art methods. Although the physical part of the numerical model data has been corrected by our proposed method, the prediction performance could be further improved if an interpretable model is employed. In the future, we plan to extract more pertinent knowledge from the deep networks, and then design interpretable models more suitable for practical applications.