arXiv (All)
open access
Improving precipitation forecasts in an AI weather model using observational data
Artificial intelligence weather prediction systems now surpass state-of-the-art physical models for medium-range forecasting. However, because these models are trained almost exclusively on historical climate reanalyses, they inherit pervasive structural biases, particularly for precipitation. Here
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
- Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model#984917
- A dataset of one-dimensional idealized probabilistic fields#1061355
- AI in Climate Change Mitigation#72860
- Can Integrated Machine Learning Models Improve Rainfall Forecasting? A Case Study from the Australian Tropics#443670
- Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts#238623
Record · ID 999870
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