Abstract

Due to the considerable computational demands of physics-based numerical weather prediction, especially when modeling fine-grained spatio-temporal atmospheric phenomena, deep learning methods offer an advantageous approach by leveraging specialized computing devices to accelerate training and significantly reduce computational costs. Consequently, the application of deep learning methods has presented a novel solution in the field of weather forecasting. In this context, we introduce a groundbreaking deep learning-based weather prediction architecture known as Hierarchical U-Net (HU-Net) with re-parameterization techniques. The HU-Net comprises two essential components: a feature extraction module and a U-Net module with re-parameterization techniques. The feature extraction module consists of two branches. First, the global pattern extraction employs adaptive Fourier neural operators and self-attention, well-known for capturing long-term dependencies in the data. Second, the local pattern extraction utilizes convolution operations as fundamental building blocks, highly proficient in modeling local correlations. Moreover, a feature fusion block dynamically combines dual-scale information. The U-Net module adopts RepBlock with re-parameterization techniques as the fundamental building block, enabling efficient and rapid inference. In extensive experiments carried out on the large-scale weather benchmark dataset WeatherBench at a resolution of 1.40625 \(^\circ \) , the results demonstrate that our proposed HU-Net outperforms other baseline models in both prediction accuracy and inference time.

中文翻译：

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具有重新参数化技术的时空天气预报分层U-net

摘要

由于基于物理的数值天气预报需要大量计算，特别是在对细粒度时空大气现象进行建模时，深度学习方法提供了一种有利的方法，利用专用计算设备来加速训练并显着降低计算成本。因此，深度学习方法的应用为天气预报领域提供了一种新颖的解决方案。在此背景下，我们引入了一种突破性的基于深度学习的天气预报架构，称为分层 U-Net (HU-Net)，具有重新参数化技术。HU-Net 包含两个基本组件：特征提取模块和具有重新参数化技术的 U-Net 模块。特征提取模块由两个分支组成。首先，全局模式提取采用自适应傅里叶神经算子和自注意力，以捕获数据中的长期依赖性而闻名。其次，局部模式提取利用卷积运算作为基本构建块，非常擅长对局部相关性进行建模。此外，特征融合块动态地组合双尺度信息。U-Net模块采用具有重参数化技术的RepBlock作为基本构建块，实现高效、快速的推理。在分辨率为 1.40625 \(^\circ \)的大型天气基准数据集WeatherBench上进行的大量实验中，结果表明我们提出的 HU-Net 在预测精度和推理时间方面都优于其他基线模型。