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[2602.15056] Reconstructing Carbon Monoxide Reanalysis with Machine Learning
Summary
This article discusses a study on using machine learning to reconstruct carbon monoxide reanalysis data, addressing challenges posed by varying observational data availability.
Why It Matters
The research is significant as it explores innovative machine learning techniques to enhance atmospheric monitoring, which is crucial for understanding air quality and climate change. By improving data reconstruction methods, the study contributes to more accurate environmental assessments and policy-making.
Key Takeaways
- Machine learning can compensate for gaps in atmospheric data.
- The study focuses on carbon monoxide reanalysis, a key air quality metric.
- Improved data accuracy can enhance climate monitoring efforts.
- The research leverages satellite observations and model simulations.
- Findings may influence future atmospheric monitoring strategies.
Physics > Atmospheric and Oceanic Physics arXiv:2602.15056 (physics) [Submitted on 12 Feb 2026] Title:Reconstructing Carbon Monoxide Reanalysis with Machine Learning Authors:Paula Harder, Johannes Flemming View a PDF of the paper titled Reconstructing Carbon Monoxide Reanalysis with Machine Learning, by Paula Harder and Johannes Flemming View PDF HTML (experimental) Abstract:The Copernicus Atmospheric Monitoring Service provides reanalysis products for atmospheric composition by combining model simulations with satellite observations. The quality of these products depends strongly on the availability of the observational data, which can vary over time as new satellite instruments become available or are discontinued, such as Carbon Monoxide (CO) observations of the Measurements Of Pollution In The Troposphere (MOPITT) satellite in early 2025. Machine learning offers a promising approach to compensate for such data losses by learning systematic discrepancies between model configurations. In this study, we investigate machine learning methods to predict monthly-mean total column of Carbon Monoxide re-analysis from a control model simulation. Subjects: Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2602.15056 [physics.ao-ph] (or arXiv:2602.15056v1 [physics.ao-ph] for this version) https://doi.org/10.48550/arXiv.2602.15056 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Pa...