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,Zhibin Sun,John M. Davis,Yan-An Liu,Chelsea A. Corr,and Wei Gao
United States Department of Agriculture UV-B Monitoring and Research
Program, Natural Resource Ecology Laboratory, Colorado State University,
Fort Collins, CO 80523, USA
United States Department of Agriculture UV-B Monitoring and Research
Program, Natural Resource Ecology Laboratory, Colorado State University,
Fort Collins, CO 80523, USA
John M. Davis
United States Department of Agriculture UV-B Monitoring and Research
Program, Natural Resource Ecology Laboratory, Colorado State University,
Fort Collins, CO 80523, USA
Key Laboratory of Geographic Information Science (Ministry of
Education), East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai
200241, China
ECNU-CSU Joint Research Institute for New Energy and the Environment,
Shanghai 200062, China
Chelsea A. Corr
United States Department of Agriculture UV-B Monitoring and Research
Program, Natural Resource Ecology Laboratory, Colorado State University,
Fort Collins, CO 80523, USA
Wei Gao
United States Department of Agriculture UV-B Monitoring and Research
Program, Natural Resource Ecology Laboratory, Colorado State University,
Fort Collins, CO 80523, USA
Department of Ecosystem Science and Sustainability, Colorado State
University, Fort Collins, CO 80523, USA
Viewed
Total article views: 3,384 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
2,298
966
120
3,384
171
148
HTML: 2,298
PDF: 966
XML: 120
Total: 3,384
BibTeX: 171
EndNote: 148
Views and downloads (calculated since 15 Nov 2018)
Cumulative views and downloads
(calculated since 15 Nov 2018)
Total article views: 2,801 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,987
705
109
2,801
155
135
HTML: 1,987
PDF: 705
XML: 109
Total: 2,801
BibTeX: 155
EndNote: 135
Views and downloads (calculated since 12 Feb 2019)
Cumulative views and downloads
(calculated since 12 Feb 2019)
Total article views: 583 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
311
261
11
583
16
13
HTML: 311
PDF: 261
XML: 11
Total: 583
BibTeX: 16
EndNote: 13
Views and downloads (calculated since 15 Nov 2018)
Cumulative views and downloads
(calculated since 15 Nov 2018)
Viewed (geographical distribution)
Total article views: 3,384 (including HTML, PDF, and XML)
Thereof 3,073 with geography defined
and 311 with unknown origin.
Total article views: 2,801 (including HTML, PDF, and XML)
Thereof 2,523 with geography defined
and 278 with unknown origin.
Total article views: 583 (including HTML, PDF, and XML)
Thereof 550 with geography defined
and 33 with unknown origin.
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...
