Assessing the impact of clouds on ground-based UV–visible total column ozone measurements in the high Arctic

• Published in: Atmospheric Measurement Techniques
• Main Authors: Zhao, Xiaoyi, Bognar, Kristof, Fioletov, Vitali, Pazmino, Andrea, Goutail, Florence, Millán, Luis, Manney, Gloria, Adams, Cristen, Strong, Kimberly
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2019
• Subjects: article; Verlagsveröffentlichung; Arctic; Canada; Eureka
• Online Access: https://doi.org/10.5194/amt-12-2463-2019; https://noa.gwlb.de/receive/cop_mods_00002602; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00002560/amt-12-2463-2019.pdf; https://amt.copernicus.org/articles/12/2463/2019/amt-12-2463-2019.pdf

Zenith-Sky scattered light Differential Optical Absorption Spectroscopy (ZS-DOAS) has been used widely to retrieve total column ozone (TCO). ZS-DOAS measurements have the advantage of being less sensitive to clouds than direct-sun measurements. However, the presence of clouds still affects the quality of ZS-DOAS TCO. Clouds are thought to be the largest contributor to random uncertainty in ZS-DOAS TCO, but their impact on data quality still needs to be quantified. This study has two goals: (1) to investigate whether clouds have a significant impact on ZS-DOAS TCO, and (2) to develop a cloud-screening algorithm to improve ZS-DOAS measurements in the Arctic under cloudy conditions. To quantify the impact of weather, 8 years of measured and modelled TCO have been used, along with information about weather conditions at Eureka, Canada (80.05∘ N, 86.41∘ W). Relative to direct-sun TCO measurements by Brewer spectrophotometers and modelled TCO, a positive bias is found in ZS-DOAS TCO measured in cloudy weather, and a negative bias is found for clear conditions, with differences of up to 5 % between clear and cloudy conditions. A cloud-screening algorithm is developed for high latitudes using the colour index calculated from ZS-DOAS spectra. The quality of ZS-DOAS TCO datasets is assessed using a statistical uncertainty estimation model, which suggests a 3 %–4 % random uncertainty. The new cloud-screening algorithm reduces the random uncertainty by 0.6 %. If all measurements collected during cloudy conditions, as identified using the weather station observations, are removed, the random uncertainty is reduced by 1.3 %. This work demonstrates that clouds are a significant contributor to uncertainty in ZS-DOAS TCO and proposes a method that can be used to screen clouds in high-latitude spectra.