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. To recover the actual responsivity for the Ultraviolet Multi-Filter RotatingShadowband Radiometer (UV-MFRSR), the complex (e.g., unstable, noisy, andwith gaps) time series of its in situ calibration factors ( V0 ) need tobe smoothed. Many smoothing techniques require accurate input uncertainty ofthe time series. A new method is proposed to estimate the dynamic inputuncertainty by examining overall variation and subgroup means within a movingtime window. Using this calculated dynamic input uncertainty within Gaussianprocess (GP) regression provides the mean and uncertainty functions of thetime series. This proposed GP solution was first applied to a syntheticsignal and showed significantly smaller RMSEs than a Gaussian processregression performed with constant values of input uncertainty and the meanfunction. GP was then applied to three UV-MFRSR V0 time series at threeground sites. The method appropriately accounted for variation in slopes,noises, and gaps at all sites. The validation results at the three test sites(i.e., HI02 at Mauna Loa, Hawaii; IL02 at Bondville, Illinois; and OK02 atBillings, Oklahoma) demonstrated that the agreement among aerosol opticaldepths (AODs) at the 368 nm channel calculated using V0 determined bythe GP mean function and the equivalent AERONET AODs were consistently betterthan those calculated using V0 from standard techniques (e.g., movingaverage). For example, the average AOD biases of the GP method (0.0036 and0.0032) are much lower than those of the moving average method (0.0119 and0.0119) at IL02 and OK02, respectively. The GP method's absolute differencesbetween UV-MFRSR and AERONET AOD values are approximately 4.5 %,21.6 %, and 16.0 % lower than those of the moving average method atHI02, IL02, and OK02, respectively. The improved accuracy of in situ UVMRP V0 values suggests the GP solution is a robust technique for accurateanalysis of complex time series and may be applicable to other fields.

atmospheric measurement techniques

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