What is recorded here?
Maps heterogeneous weather observations through learned state estimation, autoregressive forecasting and local prediction modules, permitting end-to-end adaptation without numerical-model initial states at inference.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
V1 ten-author UNet account differs from expanded v3 eleven-author ViT account; first two equal marks are explicit in v3. Independence from NWP at inference does not remove reanalysis dependence during pretraining. V1 abstract says one-degree while body reports1.41-degree grid; no unsupported exact resolution selected. Skill and baseline wins vary by variable/lead; spectral blurring and weak upper-level/short-range skill remain. No agricultural/public-health application claims.
Read the original work
- Aardvark Weather: end-to-end data-driven weather forecasting
- End-to-end data-driven weather prediction
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
Anna Vaughan; Stratis Markou; Will Tebbutt; James Requeima; Wessel P. Bruinsma; Tom R. Andersson; Michael Herzog; Nicholas D. Lane; J. Scott Hosking; Richard E. Turner; Matthew Chantry
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
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English catalogue narrative
Aardvark end-to-end observation-to-weather forecasting
Maps heterogeneous weather observations through learned state estimation, autoregressive forecasting and local prediction modules, permitting end-to-end adaptation without numerical-model initial states at inference.
Scientific scope
V1 ten-author UNet account differs from expanded v3 eleven-author ViT account; first two equal marks are explicit in v3. Independence from NWP at inference does not remove reanalysis dependence during pretraining. V1 abstract says one-degree while body reports1.41-degree grid; no unsupported exact resolution selected. Skill and baseline wins vary by variable/lead; spectral blurring and weak upper-level/short-range skill remain. No agricultural/public-health application claims.
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
Anna Vaughan; Stratis Markou; Will Tebbutt; James Requeima; Wessel P. Bruinsma; Tom R. Andersson; Michael Herzog; Nicholas D. Lane; J. Scott Hosking; Richard E. Turner; Matthew Chantry
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.