Apollo
metadata only
End-to-end data-driven weather prediction.
Acknowledgements: We acknowledge the agencies whose efforts in collecting, curating and distributing datasets made this study possible. This study stands on the foundation of decades of contributions from the meteorological community and their commitment to sharing data. Specifically, we thank the E
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Bridging the weather and climate divide with artificial intelligence#676210
- Supporting Code and Data for "MAUSAM: An Observations-focused assessment of Global AI Weather Prediction Models During the South Asian Monsoon"#126987
- Supporting Code and Data for "MAUSAM: An Observations-focused assessment of Global AI Weather Prediction Models During the South Asian Monsoon"#126986
- Climate Informatics: Accelerating Discovering in Climate Science with Machine Learning#499211
- Can Integrated Machine Learning Models Improve Rainfall Forecasting? A Case Study from the Australian Tropics#443670
Record · ID 783645
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive