High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport
<p>Quantifying greenhouse gas (GHG) emissions is critically important for projecting future climate and assessing the impact of environmental policy. Estimating GHG emissions using atmospheric observations is typically done using source–receptor relationships (i.e., “footprints”). Constructing...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-05-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/25/5159/2025/acp-25-5159-2025.pdf |
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