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
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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...
