Reconstructing Carbon Monoxide Reanalysis with Machine Learning
This work addresses data gaps in atmospheric monitoring for environmental scientists, but appears incremental as it applies existing ML methods to a new domain-specific challenge.
The study tackled the problem of reconstructing Carbon Monoxide reanalysis data to compensate for potential data losses from satellite discontinuities, using machine learning to predict monthly-mean total column CO from a control model simulation, but no concrete results or numbers were reported.
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.