arXiv (All)
open access
Graph-Learned Spring SST Evolution and Land-Ocean Synergy Improve Yangtze Summer Rainfall Prediction
Seasonal prediction of Yangtze summer rainfall is challenged by nonstationary oceanic precursors. Here we demonstrate that accounting for the evolution of spring SST and state-dependent land surface information improves predictability. Graph total variation (GTV) is applied to March-May SST fields o
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
- Generating high-resolution climate data in the Andes using artificial intelligence: A lightweight alternative to the WRF model#437597
- Long-range statistical forecasting of Korean summer precipitation#497092
- Challenges and Opportunities in Predicting Future Beach Evolution: A Review of Processes, Remote Sensing, and Modeling Approaches#262097
- End-to-end data-driven weather prediction.#783645
- Data-driven global ocean modeling for seasonal to decadal prediction#262177
Record · ID 1387382
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