Reconstructing Carbon Monoxide Reanalysis with Machine Learning

TL;DR

This paper explores machine learning techniques to reconstruct and improve Carbon Monoxide reanalysis data by compensating for observational data gaps in satellite-based atmospheric monitoring.

Contribution

It introduces a machine learning approach to predict CO reanalysis data, addressing challenges posed by variable satellite observation availability.

Findings

ML models can effectively predict CO reanalysis data

Improved continuity of atmospheric composition datasets

Potential to enhance reanalysis quality during data gaps

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.