The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere. In the time of climate change, having access to high-resolution forecasts from models like these is also becoming increasingly vital. While most existing Neural Weather Prediction (NeurWP) methods focus on global forecasting, an important question is how these techniques can be applied to limited area modeling. In this work we adapt the graph-based NeurWP approach to the limited area setting and propose a multi-scale hierarchical model extension. Our approach is validated by experiments with a local model for the Nordic region.

翻译：高精度机器学习方法在天气预报领域的兴起，正为大气建模创造革命性的新可能。在气候变化时代，获取此类模型提供的高分辨率预报正变得日益重要。尽管现有大多数神经天气预报（NeurWP）方法侧重于全球预报，但一个关键问题是如何将这些技术应用于有限区域建模。在本研究中，我们将基于图的NeurWP方法适配至有限区域场景，并提出了一种多尺度层次模型扩展方案。通过针对北欧地区的局部模型实验，验证了本方法的有效性。