Data-driven rolling model for global wave height

Abstract. Significant Wave Height (SWH) is crucial for many human activities, such as marine navigation, offshore operations, and coastal management. Traditionally, SWH is modeled using numerical wave models, which, while accurate, are computationally intensive and constrained by incomplete physical representations of wave spectral evolution. This study introduces a simple global deep learning-based model for SWH, which uses the current SWH field and the wind field at the next time step as inputs to predict the SWH field at the next time step. This approach mirrors the rolling prediction strategy of numerical wave models. After training on a re-analysis dataset, the errors of the model diverge lightly with time when given a good initial field because no spectral information is used. However, after diverging for ~200 hours, the errors stabilize, remaining comparable to those of state-of-the-art numerical wave models. Additionally, the error divergence can be mitigated through the assimilation of altimeter measurements. This deep learning model can not only serve as an efficient surrogate for traditional numerical wave models but also provide a baseline for statistical modeling of global SWH due to its simplicity in inputs and outputs.