From Regional to Global: Transfer Learning for Atmospheric Transport Emulators

2026-09-20

AI Summary

研究通过转移学习方法解决了从区域到全球的大气传输模拟器扩展问题，以提高温室气体排放估计效率。

Abstract

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 atmospheric transport is regionally heterogeneous. This paper evaluates spatial transferability capabilities of models across four world regions: South America, East Asia, South Asia, North Africa using both region-specific and multi-region models, and leave-one-region-out experiments. Regional differences are characterised in the context of input variable and output footprint distributions. This work builds intuition in cross-region generalisation and transfer learning, aiding regional performance towards efficient global emissions estimates.

Problem

• atmospheric transport
• transfer learning
• global emissions estimates
• regional heterogeneity

Innovation

• Transfer Learning
• Atmospheric Transport Emulator
• Cross-Region Generalisation
• Spatial Transferability