3D Cloud Reconstruction Through Geospatially-Aware Masked Autoencoders
Self-supervised learning enhances 3D cloud reconstruction, improving climate predictions by reducing uncertainties in climate models.
AI & Environment Resource HubUNEP · 2024-12-01
Description not yet supplied.
Intended audience has not been documented.
Source-specific limitations have not been reviewed. Inclusion and topic labels do not establish effectiveness or reliability.
UNEP. 2024-12-01. 2024 Climate Risk Landscape Report. https://www.unepfi.org/wordpress/wp-content/uploads/2024/04/Climate-Risk-Landscape-2024.pdf (AI & Environment Resource Hub; record report-136; collection snapshot 2026-10-04).
Included in the original Policy Document collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Source sheet: Policy Document · Row 140 · Original ID: report-136.
These are policy resources, not a verified register of enacted laws. Legal status and jurisdiction must be checked at source. Source link reachable · checked 2026-09-28. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Publication, placement, and evidence are tracked separately. A legacy coverage flag alone does not prove an appearance.
No issue-specific evidence recorded.
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
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
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
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
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.