SOURCE-LINKED INTELLIGENCE
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 not scale well to modern satellites' high resolution data. Previously we developed a performant atmospheric transport emulator that approximates LPDM outputs ("footprints") over South America ~1,000X faster than the UK Met Office's LPDM. Expanding towards global emulation is not straightforward, as
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-20T19:38:18.000Z
First collected: 2026-09-23T09:51:33.063Z. This is not the publication date.