arXiv (All)
open access
Too Rare to Learn: Prescribed Cyclone Tracks Degrade a Bay of Bengal Ocean Emulator
Neural ocean emulators are being proposed for regional forecasting in cyclone-exposed coastal seas, and a natural design choice is to hand the network the cyclone as a prescribed input. We test that choice in the Bay of Bengal and find it harmful. We withhold 15 whole cyclones spanning 65 to 150 kt
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
- 33 U.S.C. § 3611 — Assessing and modeling named storms over coastal States#585026
- Estimation of Sea State Parameters from Measured Ship Motions with a Neural Network Trained on Experimentally Validated Model Simulations#304056
- Neural Synthetic Profiles from Remote Sensing and Observations (NeSPReSO) — Reconstructing temperature and salinity fields in the Gulf of Mexico#344376
- Butterfly Effect Confirmed in Global AI Weather Models: Evidence from Tropical Cyclone Forecasting#1144346
- CasEm: A Cascade Architecture for Long-Horizon Neural Emulation#1119319
Record · ID 927905
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