@inproceedings{
oskarsson2024probabilistic,
title={Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks},
author={Joel Oskarsson and Tomas Landelius and Marc Peter Deisenroth and Fredrik Lindsten},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=wTIzpqX121}
}
In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting. While most current machine learning models focus on deterministic forecasts, accurately capturing the uncertainty in the chaotic weather system calls for probabilistic modeling. We propose a probabilistic weather forecasting model called Graph-EFM, combining a flexible latent-variable formulation with the successful graph-based forecasting framework. The use of a hierarchical graph construction allows for efficient sampling of spatially coherent forecasts. Requiring only a single forward pass per time step, Graph-EFM allows for fast generation of arbitrarily large ensembles. We experiment with the model on both global and limited area forecasting. Ensemble forecasts from Graph-EFM achieve equivalent or lower errors than comparable deterministic models, with the added benefit of accurately capturing forecast uncertainty.
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
Joel Oskarsson Tomas Landelius Marc Peter Deisenroth Fredrik Lindsten