arXiv (All)
open access
From Regional to Global: Transfer Learning for Atmospheric Transport Emulators
Greenhouse gas emissions estimates can be derived using inverse methods by combining atmospheric concentration observations with chemical transport models. The latter traditionally use physics-driven simulators such as Lagrangian Particle Dispersion Models (LPDMs), which are expensive to run and do
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
- Reactive transport codes for subsurface environmental simulation#436244
- SaWaM WRF physics parameterization scheme combination #12 (RUN12)#1082659
- SaWaM WRF physics parameterization scheme combination #5 (RUN5)#1082652
- SaWaM WRF physics parameterization scheme combination #13 (RUN13)#1082660
- Residuals Are Not Enough: Limits of Physics-Informed Pre-Training for Scientific Foundation Models#1144711
Record · ID 1050030
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