What is recorded here?
Applies AFNO to autoregressive high-resolution global weather fields and develops feature/data-parallel GPU training with optimized spectral operations, I/O and launch overhead.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Two 2022 sources bundled with separate 13/9-person bylines; SC account shares contribution among Subramanian/Harrington/Pathak, not automatically first author. Forecast speed is inference-only on specific hardware/variables; ERA5 reanalysis/training cost and fewer vertical variables matter. Distributed timings include exposed I/O/validation but exclude setup/warm-up/capture and extrapolate some 80-epoch costs from five epochs. No general climate or exact physical-simulation certificate.
Read the original work
- FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
- FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Jaideep Pathak; Shashank Subramanian; Peter Harrington; Sanjeev Raja; Ashesh Chattopadhyay; Morteza Mardani; Thorsten Kurth; David Hall; Zongyi Li; Kamyar Azizzadenesheli; Pedram Hassanzadeh; Karthik Kashinath; Animashree Anandkumar; Andrea Miele
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
FourCastNet high-resolution weather forecasting and distributed training
Applies AFNO to autoregressive high-resolution global weather fields and develops feature/data-parallel GPU training with optimized spectral operations, I/O and launch overhead.
Scientific scope
Two 2022 sources bundled with separate 13/9-person bylines; SC account shares contribution among Subramanian/Harrington/Pathak, not automatically first author. Forecast speed is inference-only on specific hardware/variables; ERA5 reanalysis/training cost and fewer vertical variables matter. Distributed timings include exposed I/O/validation but exclude setup/warm-up/capture and extrapolate some 80-epoch costs from five epochs. No general climate or exact physical-simulation certificate.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Jaideep Pathak; Shashank Subramanian; Peter Harrington; Sanjeev Raja; Ashesh Chattopadhyay; Morteza Mardani; Thorsten Kurth; David Hall; Zongyi Li; Kamyar Azizzadenesheli; Pedram Hassanzadeh; Karthik Kashinath; Animashree Anandkumar; Andrea Miele
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.