PhysTC: A Physics-Enhanced Dataset and Architecture for High-Precision Tropical Cyclone Forecasting

A critical observability gap arises in multiscale chaotic systems when coarse observations smooth out high-frequency extremes. For tropical cyclones, this makes standard reanalysis products insufficient for high-precision intensity prediction, as spectral truncation can mask peak winds by over $30\%$. In this work, we introduce Physics-enhanced Tropical Cyclone forecasting (PhysTC), a physics-enhanced benchmark framework for forecasting tropical cyclone track and intensity from globally consistent coarse observations. We curate a harmonized multi-basin dataset spanning 1950--2023, incorporating scale-robust physical predictors to mitigate resolution-induced bias. Building on this dataset, we propose the Physics-enhanced Tropical Cyclone Network (PhysTCN), a model that integrates synoptic spatial structure, storm-following temporal history, and physical consistency constraints to infer intensity-relevant dynamics beyond direct grid-space readout. PhysTCN significantly outperforms state-of-the-art baselines, with particularly strong gains in recovering extreme wind speeds that are underestimated in reanalysis data. This work provides a scalable framework for physically guided forecasting in data-sparse, cross-scale dynamical systems.