mlsanityData to 7 Oct 2026

Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks

arXiv 2406.04759 ↗First posted 7 Jun 2024cs.LG stat.MLcited by 29

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Abstract (arXiv)

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.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 11 of its 50 references that are in the map. First posted on arXiv 7 Jun 2024.

Builds on

11 of 50 references.

References · 11

  1. 2022FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operatorscited by 748
  2. 2023Neural General Circulation Models for Weather and Climatecited by 232
  3. 2023WeatherBench 2: A benchmark for the next generation of data-driven global weather modelscited by 227
  4. 2023FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Leadcited by 170
  5. 2023GenCast: Diffusion-based ensemble forecasting for medium-range weathercited by 169
  6. 2022Forecasting Global Weather with Graph Neural Networkscited by 165
  7. 2023Scaling transformer neural networks for skillful and reliable medium-range weather forecastingcited by 106
  8. 2023Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscalingcited by 103
  9. 2021Simulating Continuum Mechanics with Multi-Scale Graph Neural Networkscited by 39
  10. 2023AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learningcited by 35
  11. 2024Uncertainty quantification for data-driven weather modelscited by 24

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 18; 2026: 6.

29 papers.

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  1. 2025OneForecast: A Universal Framework for Global and Regional Weather Forecastingcited by 30
  2. 2024Continuous Ensemble Weather Forecasting with Diffusion modelscited by 27
  3. 2025Elucidated Rolling Diffusion Models for Probabilistic Forecasting of Complex Dynamicscited by 12
  4. 2025Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusioncited by 11
  5. 2025OmniCast: A Masked Latent Diffusion Model for Weather Forecasting Across Time Scalescited by 11
  6. 2026U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecastercited by 10
  7. 2025ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecastingcited by 8
  8. 2025FireCastNet: Earth-as-a-Graph for Seasonal Fire Predictioncited by 7
  9. 2025High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Gridcited by 7
  10. 2024Regional Ocean Forecasting with Hierarchical Graph Neural Networkscited by 4
  11. 2024TCP-Diffusion: A Multi-modal Diffusion Model for Global Tropical Cyclone Precipitation Forecasting with Change Awarenesscited by 4
  12. 2025PEAR: Equal Area Weather Forecasting on the Spherecited by 4
17 more
  1. 2024Valid Error Bars for Neural Weather Models using Conformal Predictioncited by 3
  2. 2024Beyond Grid Data: Exploring Graph Neural Networks for Earth Observationcited by 3
  3. 2025Augmented Invertible Koopman Autoencoder for long-term time series forecastingcited by 3
  4. 2025Learning Stochastic Multiscale Modelscited by 3
  5. 2025Assessing the Geographic Generalization and Physical Consistency of Generative Models for Climate Downscalingcited by 3
  6. 2026Tyche: One Step Flow for Efficient Probabilistic Weather Forecastingcited by 3
  7. 2025IndiaWeatherBench: A Dataset and Benchmark for Data-Driven Regional Weather Forecasting over Indiacited by 1
  8. 2025Graph-based Neural Space Weather Forecastingcited by 1
  9. 2025Hierarchical Graph Networks for Accurate Weather Forecasting via Lightweight Trainingcited by 1
  10. 2025Breaking Scale Anchoring: Frequency Representation Learning for Accurate High-Resolution Inference from Low-Resolution Trainingcited by 1
  11. 2025Reducing Smoothness with Expressive Memory Enhanced Hierarchical Graph Neural Networkscited by 0
  12. 2025On the use of Graphs for Satellite Image Time Seriescited by 0
  13. 2025Fast and Flexible Probabilistic Forecasting of Dynamical Systems using Flow Matching and Physical Perturbationcited by 0
  14. 2026Deterministic and probabilistic neural surrogates of global hybrid-Vlasov simulationscited by 0
  15. 2026Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbationscited by 0
  16. 2026Every Fixed Metric Has a Blind Spot: A Learned Atmospheric Critic for Scoring Forecast Realismcited by 0
  17. 2026Variational Augmented Invertible Koopman Autoencoder for probabilistic time series forecastingcited by 0