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. But 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 Baseline model while achieving a remarkable 33.2% reduction in FLOPs.

翻译：数值模型长期以来被用于理解地球科学现象，包括对可再生能源生产与海岸工程至关重要的潮汐流。然而，其计算成本阻碍了生成不同分辨率的数据。作为替代方案，基于深度学习的降尺度方法因其更快的推理速度而受到关注。但大多数方法仅能推断固定尺度，并忽视了目标地球科学数据的重要特性。本文针对潮汐流数据提出一种新颖的降尺度框架，处理其与图像不同的独特特征：异质性和局部依赖性。此外，我们的框架利用连续表示模型可生成任意尺度的输出。与基线模型相比，所提框架在流速预测上显著提升了93.21%（均方误差）和63.85%（平均绝对误差），同时实现了33.2%的计算量减少。