Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 13, issue 12
Articles | Volume 13, issue 12
https://doi.org/10.5194/essd-13-5711-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-5711-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 12
Data description paper
 | 
10 Dec 2021
Data description paper |  | 10 Dec 2021

ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model

Sandip S. Dhomse, Carlo Arosio, Wuhu Feng, Alexei Rozanov, Mark Weber, and Martyn P. Chipperfield

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Latest update: 23 Apr 2024
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Short summary
High-quality long-term ozone profile data sets are key to estimating short- and long-term ozone variability. Almost all the satellite (and chemical model) data sets show some kind of bias with respect to each other. This is because of differences in measurement methodologies as well as simplified processes in the models. We use satellite data sets and chemical model output to generate 42 years of ozone profile data sets using a random-forest machine-learning algorithm that is named ML-TOMCAT.
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