June 10, 2024, 4:44 a.m. | Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten

stat.ML updates on arXiv.org arxiv.org

arXiv:2406.04759v1 Announce Type: cross
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 …

abstract arxiv cs.lg current focus forecasting graph graph neural networks hierarchical machine machine learning machine learning models modeling networks neural networks probabilistic modeling resolution stat.ml tool type uncertainty weather weather forecasting while

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