Articles | Volume 13, issue 12
https://doi.org/10.5194/essd-13-5711-2021
https://doi.org/10.5194/essd-13-5711-2021
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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Cited articles

Abalos, M. and de la Cámara, A.: Twenty-First Century Trends in Mixing Barriers and Eddy Transport in the Lower Stratosphere, Geophys. Res. Lett., 47, e2020GL089548, https://doi.org/10.1029/2020GL089548, 2020. a
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Ball, W. T., Alsing, J., Mortlock, D. J., Staehelin, J., Haigh, J. D., Peter, T., Tummon, F., Stübi, R., Stenke, A., Anderson, J., Bourassa, A., Davis, S. M., Degenstein, D., Frith, S., Froidevaux, L., Roth, C., Sofieva, V., Wang, R., Wild, J., Yu, P., Ziemke, J. R., and Rozanov, E. V.: Evidence for a continuous decline in lower stratospheric ozone offsetting ozone layer recovery, Atmos. Chem. Phys., 18, 1379–1394, https://doi.org/10.5194/acp-18-1379-2018, 2018. a
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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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