Reconstructing Carbon Monoxide Reanalysis with Machine Learning

2026-02-12

AI Summary

This study addresses the degradation in quality of atmospheric carbon monoxide (CO) reanalysis data caused by gaps in satellite observations, such as those from MOPITT, by introducing a machine learning–based approach to systematically correct biases arising from missing measurements. The proposed method trains a model to learn the systematic differences between control simulations and reanalysis products, enabling high-fidelity reconstruction of monthly mean total column CO concentrations solely from model simulations when satellite data are unavailable. Experimental results demonstrate that this approach significantly enhances the continuity, stability, and reliability of CO reanalysis during observational gaps, offering an innovative and effective strategy for data gap-filling in atmospheric composition reanalysis.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Computer Vision: Diffusion Models for Vision
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Carbon Monoxide
• reanalysis
• satellite observations
• data gaps
• atmospheric composition

Innovation

• machine learning
• carbon monoxide reanalysis
• data gap compensation
• atmospheric composition
• model bias correction