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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: FourCastNet ×Clear all

10 claims from 5 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

   Pathak, Subramanian, Harrington et al. · arXiv (Cornell University) · 2022

   FourCastNet is a global, data-driven weather model at 0.25° resolution that gives short to medium-range forecasts and runs far faster than the traditional IFS model.

   Unchecked2 claims
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   1. UncheckedFourCastNet, a data-driven model, is reported to match the IFS at short lead times for large-scale variables and to beat it for fine-scale ones such as precipitation.“FourCastNet matches the forecasting accuracy of the ECMWF Integrated Forecasting System (IFS), a state-of-the-art Numerical Weather Prediction (NWP) model, at short lead times for large-scale variables, while outperforming IFS for variables with complex fine-scale structure, including precipitation.”
   2. UncheckedFourCastNet, a data-driven weather model, produces a week-long global forecast in under 2 seconds, orders of magnitude faster than the IFS.“FourCastNet generates a week-long forecast in less than 2 seconds, orders of magnitude faster than IFS.”

2. 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

   Unchecked2 claims
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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…

3. 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.”

4. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators

   Kurth, Subramanian, Harrington et al. · arXiv (Cornell University) · 2022

   Unchecked3 claims
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   1. Unchecked“We report that a data-driven deep learning Earth system emulator, FourCastNet, can predict global weather and generate medium-range forecasts five orders-of-magnitude faster than NWP while approaching state-of-the-art accuracy.”
   2. Unchecked“FourCast-Net is optimized and scales efficiently on three supercomputing systems: Selene, Perlmutter, and JUWELS Booster up to 3,808 NVIDIA A100 GPUs, attaining 140.8 petaFLOPS in mixed precision (11.9%of peak at that scale).”
   3. Unchecked“The time-to-solution for training FourCastNet measured on JUWELS Booster on 3,072GPUs is 67.4minutes, resulting in an 80,000times faster time-to-solution relative to state-of-the-art NWP, in inference.”

5. Earth and Planetary Sciences › Tropical and Extratropical Cyclones Research

   Can AI weather models predict out-of-distribution gray swan tropical cyclones?

   Sun, Hassanzadeh, Zand, Chattopadhyay, Weare and Abbot · arXiv (Cornell University) · 2024

   Unchecked2 claims
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   1. Unchecked“All versions yield similar accuracy for global weather, but the one trained without Category 3-5 TCs cannot accurately forecast Category 5 TCs, indicating that these models cannot extrapolate from weaker storms.”
   2. Unchecked“The versions trained without Category 3-5 TCs in one basin show some skill forecasting Category 5 TCs in that basin, suggesting that FourCastNet can generalize across tropical basins.”