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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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AMT | Articles | Volume 12, issue 2
Atmos. Meas. Tech., 12, 935–953, 2019
https://doi.org/10.5194/amt-12-935-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 12, 935–953, 2019
https://doi.org/10.5194/amt-12-935-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Feb 2019

Research article | 12 Feb 2019

Improving the mean and uncertainty of ultraviolet multi-filter rotating shadowband radiometer in situ calibration factors: utilizing Gaussian process regression with a new method to estimate dynamic input uncertainty

Maosi Chen et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Maosi Chen on behalf of the Authors (12 Jan 2019)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (16 Jan 2019) by Andrew Sayer
AR by Maosi Chen on behalf of the Authors (27 Jan 2019)  Author's response    Manuscript
ED: Publish as is (28 Jan 2019) by Andrew Sayer
Publications Copernicus
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Short summary
Combining a new dynamic uncertainty estimation method with Gaussian process regression (GP), we provide a generic and robust solution to estimate the underlying mean and uncertainty functions of time series with variable mean, noise, sampling density, and length of gaps. The GP solution was applied and validated on three UV-MFRSR Vo time series at three ground sites with improved accuracy of the smoothed time series in terms of aerosol optical depth compared with two other smoothing methods.
Combining a new dynamic uncertainty estimation method with Gaussian process regression (GP), we...
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