新模型PTSTnet实现两年以上厄尔尼诺精准预测，且过程可解释。
Interpretable Cross-Sphere Multiscale Deep Learning Predicts ENSO Skilfully Beyond 2 Years
- 融合物理规律的神经网络框架，捕捉跨尺度气候动态。
- 在24个月以上预报中超越现有最佳模型，准确率显著提升。
- 适合气候建模、气象预测及可解释AI研究者参考。
厄尔尼诺-南方涛动（ENSO）对全球气候与社会有重大影响，但超过一年的实时预测仍具挑战性。动力模型存在较大偏差与不确定性，深度学习则面临可解释性差和多尺度动态捕捉难的问题。本文提出PTSTnet，一种融合物理机制与跨尺度时空学习的可解释神经网络模型。该模型在24个月以上的预报中显著优于现有最优基准，提供海洋-大气相互作用中误差传播的物理洞察。通过从稀疏数据中学习具有物理一致性的特征表示，有效应对海洋-大气过程中的多尺度、多物理难题，从而内在提升长期预测能力。本研究为可解释性神经海洋建模提供了重要进展。
原文摘要 · Abstract (English)
El Niño-Southern Oscillation (ENSO) exerts global climate and societal impacts, but real-time prediction with lead times beyond one year remains challenging. Dynamical models suffer from large biases and uncertainties, while deep learning struggles with interpretability and multi-scale dynamics. Here, we introduce PTSTnet, an interpretable model that unifies dynamical processes and cross-scale spatiotemporal learning in an innovative neural-network framework with physics-encoding learning. PTSTnet produces interpretable predictions significantly outperforming state-of-the-art benchmarks with lead times beyond 24 months, providing physical insights into error propagation in ocean-atmosphere interactions. PTSTnet learns feature representations with physical consistency from sparse data to tackle inherent multi-scale and multi-physics challenges underlying ocean-atmosphere processes, thereby inherently enhancing long-term prediction skill. Our successful realizations mark substantial steps forward in interpretable insights into innovative neural ocean modelling.
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