arXiv (All)
open access
Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones
Weather prediction is undergoing an important shift, with data-driven AI models outperforming leading numerical weather prediction systems on average at a fraction of the computational cost. Yet, whether this success extends to extreme events remains contested. Earlier evaluations have exposed impor
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
- Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones#1348872
- Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model#984917
- Probabilistic AI models outperform numerical weather prediction models on record breaking heat extremes#1355438
- Operational tropical cyclone forecasting with AI.#988414
- A dataset of one-dimensional idealized probabilistic fields#1061355
Record · ID 1357732
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