Anboyu Guo

Abstract Gradient-based eXplainable AI (XAI) methods that leverage automatic differentiation are widely used to reveal predictor-sensitive areas (or rather patterns) and to infer latent physical mechanisms in forecasts. However, these methods heavily rely on linear assumption when calculating gradients, which underrepresent the influence of large-amplitude features in strongly nonlinear Earth system dynamics.

Abstract Accurate and physically realistic ocean forecasting at high spatial and temporal resolution is essential for resolving fine-scale dynamical features, with profound implications for hazard prediction, maritime navigation, and sustainable ocean management. Conventional numerical models can produce sub-daily, eddy-resolving forecasts but require substantial computational resources and often struggle to maintain predictive skill at such fine scales.

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