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，为大气建模开辟了全新的可能性。在气候变化时代，获取此类模型生成的高分辨率预报正变得愈发重要。虽然现有神经气象预测方法大多聚焦于全球预报，但如何将这些技术应用于有限区域建模仍是一个重要课题。本研究将基于图的神经气象预测方法适配至有限区域场景，并提出一种多尺度层级模型扩展方案。通过针对北欧地区的本地模型实验，验证了该方法的有效性。