Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks

• URL: http://arxiv.org/abs/2406.04759v2
• Date: Sat, 26 Oct 2024 13:24:08 GMT
• Title: Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
• Authors: Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten,
• Abstract summary: We propose a probabilistic weather forecasting model called Graph-EFM. The model combines a flexible latent-variable formulation with the successful graph-based forecasting framework. Ensemble forecasts from Graph-EFM achieve equivalent or lower errors than comparable deterministic models.
• Score: 17.64833210797824
• License:
• 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.

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