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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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Cited articles

Alexandrov, M. D., Lacis, A. A., Carlson, B. E., and Cairns, B.: Remote Sensing of Atmospheric Aerosols and Trace Gases by Means of Multifilter Rotating Shadowband Radiometer. Part I: Retrieval Algorithm, J. Atmos. Sci., 59, 524–543, https://doi.org/10.1175/1520-0469(2002)059<0524:Rsoaaa>2.0.Co;2, 2002. 
Alexandrov, M. D., Marshak, A., Cairns, B., Lacis, A. A., and Carlson, B. E.: Automated cloud screening algorithm for MFRSR data, Geophys. Res. Lett., 31, L04118, https://doi.org/10.1029/2003GL019105, 2004. 
Alexandrov, M. D., Kiedron, P., Michalsky, J. J., Hodges, G., Flynn, C. J., and Lacis, A. A.: Optical depth measurements by shadow-band radiometers and their uncertainties, Appl. Opt., 46, 8027–8038, https://doi.org/10.1364/AO.46.008027, 2007. 
Alexandrov, M. D., Lacis, A. A., Carlson, B. E., and Cairns, B.: Characterization of atmospheric aerosols using MFRSR measurements, J. Geophys. Res.-Atmos., 113, D08204, https://doi.org/10.1029/2007JD009388, 2008. 
Augustine, J. A., Cornwall, C. R., Hodges, G. B., Long, C. N., Medina, C. I., and DeLuisi, J. J.: An Automated Method of MFRSR Calibration for Aerosol Optical Depth Analysis with Application to an Asian Dust Outbreak over the United States, J. Appl. Meteorol. Clim., 42, 266–278, https://doi.org/10.1175/1520-0450(2003)042<0266:Aamomc>2.0.Co;2, 2003. 
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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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