Articles | Volume 20, issue 16
https://doi.org/10.5194/acp-20-9915-2020
https://doi.org/10.5194/acp-20-9915-2020
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

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Cited articles

Augustin, N. H., Musio, M., von Wilpert, K., Kublin, E., Wood, S. N., and Schumacher, M.: Modeling spatiotemporal forest health monitoring data, J. Am. Stat. Assoc., 104, 899–911, https://doi.org/10.1198/jasa.2009.ap07058, 2009. a, b
Chang, K. L.: R code for “Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles”, Zenodo, https://doi.org/10.5281/zenodo.3992116, 2020. a
Chang, K.-L. and Guillas, S.: Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model, J. Roy. Stat. Soc. C-App., 68, 51–78, https://doi.org/10.1111/rssc.12309, 2019. a
Chang, K.-L., Guillas, S., and Fioletov, V. E.: Spatial mapping of ground-based observations of total ozone, Atmos. Meas. Tech., 8, 4487–4505, https://doi.org/10.5194/amt-8-4487-2015, 2015. a
Chang, K.-L., Petropavlovskikh, I., Cooper, O. R., Schultz, M. G., and Wang, T.: Regional trend analysis of surface ozone observations from monitoring networks in eastern North America, Europe and East Asia, Elem. Sci. Anth., 5, p. 50, https://doi.org/10.1525/elementa.243, 2017. a, b, c, d
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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.
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