Policy Document · 2024-12-01
2024 Climate Risk Landscape Report
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Scientific Paper
3D Cloud Reconstruction Through Geospatially-Aware Masked Autoencoders
Girtsou et al. · 2025-01-19
What it covers
Self-supervised learning enhances 3D cloud reconstruction, improving climate predictions by reducing uncertainties in climate models.
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
Girtsou et al.. 2025-01-19. 3D Cloud Reconstruction Through Geospatially-Aware Masked Autoencoders. https://ml4physicalsciences.github.io/2024/files/NeurIPS_ML4PS_2024_255.pdf (AI & Environment Resource Hub; record paper-026; 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.
lifecycle
Available source metadata
- Author or creator
- Girtsou et al.
- Publisher
- ML4 Physical Sciences
- Publication date
- 2025-01-19
- 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
- Reconstructing Clouds with Geospatially-Aware AI Models
- journal
- ML4 Physical Sciences
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 30 · Original ID: paper-026.
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.
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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 · 2025-05-21
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.
Shared topics: Climate and greenhouse gases; Weather, hazards, and adaptation
Multimedia · 2025-01-01
A Simple Guide to Everyday Climate Action
In her TEDx talk, Tamma shares how her journey from South Africa to the UK shaped her deep understanding of sustainability and the climate crisis, emphasizing the urgent need to reduce consumption to protect Earth's interconnected ecosystems.