PCA-Enhanced Adaptive NVAR Framework for High-Resolution Sea Surface Temperature Forecasting in the East Sea
arXiv:2606.12141v1 Announce Type: new Abstract: Accurate forecasting of sea surface temperature (SST) in regional seas such as the East Sea is crucial for monitoring marine ecosystems, assessing climate risks, managing fisheries, and conducting naval operations. Traditional numerical ocean models provide reliable predictions but are computationally expensive and often unsuitable for real-time forecasting. Many deep learning methods also struggle with high-dimensional spatiotemporal ocean data and experience error accumulation over longer forecasting periods. This study builds on our previously
The development of more sophisticated AI/ML techniques for spatiotemporal data analysis continues to advance, making real-time forecasting in complex maritime environments increasingly feasible.
Accurate and real-time sea surface temperature forecasting is critical for a range of strategic activities from national security to economic stability, impacting resource management and climate adaptation.
This research offers a method that can potentially improve the efficiency and accuracy of oceanic predictions compared to traditional models, enabling faster decision-making in marine operations.
- · Naval operations
- · Fishing industry
- · Climate scientists
- · Marine logistics
- · Traditional numerical ocean modeling reliance
Improved predictive capabilities for regional sea conditions.
Enhanced resource allocation and reduced operational risks in marine industries.
Potential for integration into autonomous marine systems for dynamic environmental adaptation.
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Read at arXiv cs.LG