Advancing Limited-area Numerical Weather Prediction with Graph-based AI: Leveraging Global and Regional Reanalysis Data for the Greater Alpine Region

Accurate weather prediction in complex topographical regions remains a significant challenge for numerical weather prediction (NWP). While global NWP models have advanced considerably, limited-area models still struggle to capture the fine-scale atmospheric processes critical for regions with complex topography, such as the Alps. Recent AI-based modelling approaches are transforming this landscape, with graph-based AI models showing great potential in capturing complex dynamics and offering flexibility across diverse data modalities. Most of these models, however, are developed for global applications and trained on global datasets like the ERA5 reanalysis. Despite the extensive temporal coverage of ERA5 encompassing diverse weather conditions, its limited spatial resolution is constraining its ability to resolve finer-scale atmospheric processes critical for limited-area modelling.

Building on these developments, we aim to leverage the power of graph-based AI models and the valuable information provided by ERA5 for limited-area modelling. Our approach utilizes the Anemoi framework to train specialized models for the Greater Alpine Region, centred over Austria, using a high-resolution regional reanalysis ensemble dataset. Specifically, we employ the Austrian ReAnalysis (ARA) dataset with its superior 2.5 km spatial resolution and 3-hourly temporal resolution in reanalysis to better capture localized weather phenomena over regions with complex topography regions that global models typically fail to capture accurately.

Our methodology encompasses multiple AI modelling approaches to determine the most effective forecasting strategy. These include global models focusing on our local study domain with a stretched-grid structure and limited-area models forced with external boundary conditions. Furthermore, we conduct transfer learning experiments that harmonise information from the long temporal record of ERA5 with the high spatial resolution of the ARA dataset, creating a more robust prediction system by mitigating issues in model training from the comparatively limited temporal extent of ARA.
To evaluate their effectiveness, we assess the performance of these different modelling approaches, comparing them against traditional physics-based models and validating the results against point-based observations. Additionally, we provide detailed examinations of these findings through case studies of impactful weather events in Austria, highlighting real-world applications of our approach.

The insights from this research will guide the next steps towards harnessing both large-scale and localised information with an AI-based approach, ultimately advancing the accuracy and relevance of limited-area models in operational weather forecasting.