  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.
