Arbitrary-Scale Downscaling of Tidal Current Data Using Implicit Continuous Representation

Abstract

Numerical models have long been used to understand geoscientific phenomena, including tidal currents, crucial for renewable energy production and coastal engineering. However, their computational cost hinders generating data of varying resolutions. As an alternative, deep learning-based downscaling methods have gained traction due to their faster inference speeds. However, most of them are limited to only inference fixed scale and overlook important characteristics of target geoscientific data. In this paper, we propose a novel downscaling framework for tidal current data, addressing its unique characteristics, which are dissimilar to images: heterogeneity and local dependency. Moreover, our framework can generate any arbitrary-scale output utilizing a continuous representation model. Our proposed framework demonstrates significantly improved flow velocity predictions by 93.21% (MSE) and 63.85% (MAE) compared to the LIIF while achieving a remarkable 33.2% reduction in FLOPs. The code will be available on GitHub: https://github.com/dslisleedh/LIIFNM.

Keywords

• Arbitrary-scale downscaling
• image super-resolution
• implicit neural representation
• oceanic tidal current data