Speaker
Description
Training high-resolution data-driven weather prediction models from scratch is computationally expensive and requires long regional datasets. We explore whether a pretrained regional machine-learning model can be transferred to a new geographic domain.
A graph neural network-based limited-area weather model trained over Switzerland is transferred to the Eastern Alps and Northern Adriatic in two ways: directly, without additional training, and through fine-tuning on five years of regional analysis data. This allows us to test both zero-shot generalization and target-domain adaptation across different grids, terrain, and climate conditions.
The pretrained model already produces meaningful forecasts when applied directly to the new domain. Fine-tuning further improves performance for most variables, while precipitation skill remains encouraging in both configurations.
The results show that transfer learning can reduce the data and computational requirements of data-driven regional weather prediction model development and offers a practical route for adapting existing machine-learning forecasting systems to new regions.