Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

Chang, Kai-Lan; Cooper, Owen R.; Gaudel, Audrey; Petropavlovskikh, Irina; Thouret, Valérie

doi:https://doi.org/10.5194/acp-20-9915-2020

Articles | Volume 20, issue 16

https://doi.org/10.5194/acp-20-9915-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-20-9915-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 16

Research article

|

26 Aug 2020

Research article |

| 26 Aug 2020

Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

Kai-Lan Chang, Owen R. Cooper, Audrey Gaudel, Irina Petropavlovskikh, and Valérie Thouret

Supplement

https://doi.org/10.5194/acp-20-9915-2020-supplement

Data sets

In-service Aircraft for a Global Observing System IAGOS https://doi.org/10.25326/20

R code for "Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles" Kai-Lan Chang https://doi.org/10.5281/zenodo.3992116

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Short summary

We provide a statistical framework for detecting trends of multiple autocorrelated time series from sparsely sampled profile data. The result is a better and more consistent quantification of trend estimates of vertical profile data. The focus was placed on the long-term ozone time series from commercial aircraft and balloon-borne ozonesonde measurements. This framework can be applied to other trace gases in the atmosphere.