Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks

Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten

Keywords: weather forecasting, graph neural network, probabilistic, ensemble forecasting, latent variable model, earth system modeling

TL;DR: We introduce a probabilistic graph neural network model for weather forecasting, capturing uncertainty by generating ensemble forecasts.

Abstract: 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.

Primary Area: Machine learning for physical sciences (for example: climate, physics)

Submission Number: 9990