从区域到全球：大气输运模拟器的迁移学习

中文摘要 AI 辅助

温室气体排放估算可通过结合大气浓度观测与化学输运模型的反演方法得出。后者传统上使用物理驱动的模拟器，如拉格朗日粒子扩散模型（LPDMs），这些模型运行成本高昂，且难以扩展至现代卫星的高分辨率数据。此前，我们开发了一种高性能大气输运模拟器，其在南美洲上空近似LPDM输出（“足迹”）的速度比英国气象局的LPDM快约1000倍。向全球模拟扩展并非易事，因为大气输运具有区域异质性。本文评估了模型在四个世界区域（南美洲、东亚、南亚、北非）的空间可迁移性，使用了区域特定模型、多区域模型以及留一区域外实验。区域差异在输入变量和输出足迹分布的背景下被刻画。这项工作为跨区域泛化和迁移学习建立了直觉，有助于提升区域性能，从而迈向高效的全球排放估算。

英文摘要

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.

发表机构

• University of Bristol（布里斯托大学）

机构由 AI 辅助整理，请以论文原文为准。