Policy Document · 2024-12-01
2024 Climate Risk Landscape Report
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Scientific Paper
Probabilistic Weather Forecasting with Machine Learning
Price et al. · 2024-12-04
What it covers
GenCast, an ML-based weather model, outperforms traditional forecasts in accuracy and speed, enhancing weather prediction and risk assessment.
Who it helps
Intended audience has not been documented.
Limitations
Source-specific limitations have not been reviewed. Inclusion and topic labels do not establish effectiveness or reliability.
Show citation
Price et al.. 2024-12-04. Probabilistic Weather Forecasting with Machine Learning. https://www.nature.com/articles/s41586-024-08252-9 (AI & Environment Resource Hub; record paper-011; collection snapshot 2026-10-04).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
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orientation
Available source metadata
- Author or creator
- Price et al.
- Publisher
- Nature
- Publication date
- 2024-12-04
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source access
- Reachable at last check
- Metadata confidence
- Metadata unconfirmed
- Last verified
- Not yet verified
- Snapshot import
- 2026-09-06
- Legacy domain
- Climate & Weather
- Legacy subdomain
- AI for Probabilistic Climate Modeling
- journal
- Nature
Provenance and review
- Source access
- Reachable at last check
- Metadata confidence
- Metadata unconfirmed
- Editorial relevance
- Relevance has not been separately reviewed.
- Evidence quality
- Evidence quality has not been separately assessed.
Source sheet: Scientific Paper · Row 15 · Original ID: paper-011.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-28. Metadata not yet corroborated. The import date is not the original date added.
Suggest a correction for this record →Newsletter appearances
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Related through shared environmental topics
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Climate and greenhouse gases; Weather, hazards, and adaptation
Shared topics: Climate and greenhouse gases; Weather, hazards, and adaptation
Scientific Paper · 2025-01-19
3D Cloud Reconstruction Through Geospatially-Aware Masked Autoencoders
Self-supervised learning enhances 3D cloud reconstruction, improving climate predictions by reducing uncertainties in climate models.
Shared topics: Climate and greenhouse gases; Weather, hazards, and adaptation
Scientific Paper · 2025-05-21
A Foundation Model for the Earth System
Aurora is a large-scale AI foundation model that significantly outperforms traditional forecasting systems across multiple Earth system domains, like air quality and cyclone tracking, while using far less computational power, marking a major advancement in accessible, efficient environmental prediction.
Shared topics: Climate and greenhouse gases; Weather, hazards, and adaptation
Scientific Paper · 2025-04-23
A Hackathon for Flood Map Prediction From Geospatial Data With Parsimonious Machine Learning Models
This study showcases a machine learning hackathon in France where a CNN-based model accurately predicted flood risk evolution using geospatial and climate data, even without streamflow input, emphasizing trustworthy AI in data-scarce regions.