PINA-Net: Physics-Constrained Nonautoregressive Network for Spatiotemporal Meteorological Forecasting in Complex Maritime Environments

Abstract

Accurate regional weather forecasting in complex maritime environments is challenging due to intricate atmospheric dynamics. While deep learning presents a promising alternative to numerical weather prediction (NWP), current paradigms face an inherent tradeoff. Autoregressive (AR) models suffer from recursive error accumulation and spectral decay, whereas pure computer vision approaches neglect physical laws, yielding dynamically inconsistent predictions. To address these limitations, we propose the physics-informed non-AR network (PINA-Net) to reconcile visual sharpness with physical consistency. Our framework synergizes a lightweight 3-D spatiotemporal encoder with coordinate attention and a cumulative residual strategy, enabling the one-shot generation of high-fidelity sequences without error propagation. Crucially, we integrate a physics-constrained loss function that embeds partial differential equations (PDEs) for mass conservation and divergence directly into the optimization process. Extensive experiments on a high-resolution meteorological dataset demonstrate that PINA-Net significantly outperforms state-of-the-art baselines. The model achieves superior numerical accuracy and structural similarity while effectively suppressing nonphysical artifacts in wind vector fields. Furthermore, by evaluating predictive uncertainty, the framework provides reliable probabilistic boundaries, offering a robust solution adhering to the intrinsic kinematic constraints of the atmosphere.