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1,248 claims from 785 papers are on the record. 45 have been checked so far; the other 1,203 have no check with a result yet.

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Status: Unchecked ×Keyword: GraphCast ×Clear all

15 claims from 8 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Learning skillful medium-range global weather forecasting

   Lam, Sánchez‐González, Willson et al. · Science · 2023

   The paper introduces GraphCast, a machine-learning weather model trained on reanalysis data, and reports that it beats the leading operational deterministic forecasting system on most verification targets.

   Unchecked2 claims
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   1. UncheckedGraphCast, a machine-learning model, forecasts hundreds of weather variables 10 days ahead on a 0.25° global grid in under a minute.“It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute.”
   2. UncheckedGraphCast, a machine-learning model, is reported to beat the best operational deterministic forecasts on 90% of 1380 targets and to aid severe-event prediction.“GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclone tracking, atmospheric rivers, and extreme temperatures.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   GraphCast: Learning skillful medium-range global weather forecasting

   Lam, Sánchez‐González, Willson et al. · arXiv (Cornell University) · 2022

   Unchecked2 claims
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   1. Unchecked“It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute.”
   2. Unchecked“We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclones, atmospheric rivers, and extreme t…

3. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán

   Charlton-Perez, Dacre, Driscoll et al. · npj Climate and Atmospheric Science · 2024

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   1. Unchecked“The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector an…
   2. Unchecked“All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradien…

4. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Evaluation of five global AI models for predicting weather in Eastern Asia and Western Pacific

   Liu, Hsu, Peng et al. · npj Climate and Atmospheric Science · 2024

   Unchecked2 claims
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   1. Unchecked“A multi-model ensemble, constructed by averaging predictions from the five models, demonstrates superior performance, rivaling that of FengWu.”
   2. Unchecked“For the 11 typhoons in 2023, FengWu demonstrates the most accurate track prediction; however, it also has the largest intensity errors.”

5. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events

   Pasche, Wider, Zhang, Zscheischler and Engelke · Artificial Intelligence for the Earth Systems · 2024

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   1. Unchecked“We find that ML weather prediction models locally achieve similar accuracy to HRES on the record-shattering Pacific Northwest heatwave but underperform when aggregated over space and time.”

6. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast

   Olivetti and Messori · Geoscientific model development · 2024

   Unchecked2 claims
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   1. Unchecked“However, the performance of data-driven models varies by region, type of extreme event, and forecast lead time.”
   2. Unchecked“Notably, data-driven models appear to perform best for temperature extremes in regions closer to the tropics and at shorter lead times.”

7. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   A Practical Probabilistic Benchmark for AI Weather Models

   Brenowitz, Cohen, Pathak et al. · Geophysical Research Letters · 2025

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   1. Unchecked“The results reveal that two leading AI weather models, i.e. GraphCast and Pangu, are tied on the probabilistic CRPS metric even though the former outperforms the latter in deterministic scoring.”

8. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Physics-based models outperform AI weather forecasts of record-breaking extremes

   Zhang, Fischer, Zscheischler and Engelke · Science Advances · 2026

   Unchecked3 claims
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   1. Unchecked“Here, we show that for record-breaking weather extremes, the physics-based numerical model High RESolution forecast (HRES) from the European Centre for Medium-Range Weather Forecasts still consistently outperforms state-of-the-art AI models GraphCast, GraphC…
   2. Unchecked“We demonstrate that forecast errors in AI models are consistently larger for record-breaking heat, cold, and wind than in HRES across nearly all lead times.”
   3. Unchecked“We further find that the examined AI models tend to underestimate both the frequency and intensity of record-breaking events, and they underpredict hot records and overestimate cold records with growing errors for larger record exceedance.”