Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7511-2021
https://doi.org/10.5194/amt-14-7511-2021
Research article
 | 
03 Dec 2021
Research article |  | 03 Dec 2021

Assessing the feasibility of using a neural network to filter Orbiting Carbon Observatory 2 (OCO-2) retrievals at northern high latitudes

Joseph Mendonca, Ray Nassar, Christopher W. O'Dell, Rigel Kivi, Isamu Morino, Justus Notholt, Christof Petri, Kimberly Strong, and Debra Wunch

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Latest update: 21 Feb 2024
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Short summary
Machine learning has become an important tool for pattern recognition in many applications. In this study, we used a neural network to improve the data quality of OCO-2 measurements made at northern high latitudes. The neural network was trained and used as a binary classifier to filter out bad OCO-2 measurements in order to increase the accuracy and precision of OCO-2 XCO2 measurements in the Boreal and Arctic regions.