Graph-based Neural Weather Prediction for Limited Area Modeling
This work addresses the need for high-resolution weather forecasts in specific regions, which is incremental as it adapts existing methods to a new setting.
The authors tackled the problem of applying neural weather prediction to limited area modeling by adapting a graph-based approach and proposing a multi-scale hierarchical extension, validated on a local model for the Nordic region.
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.