Advanced data quality flagging and data uncertainty assessment using synergies between sun photometers and Pandora spectrometers

Trace gas retrievals from direct sun measurements are not restricted to cloudless conditions and are often still possible with (thin) clouds in front of the sun. We distinguish two cases: firstly, increasing cloud optical depth decreases the signal to noise ratio  of direct sun measurements, and thus increases the uncertainty of the data. Secondly, thin homogeneous cloud layers intersecting with enhanced gas concentration enlarges the direct sun air mass factor (AMF). In this study, the impact of clouds on direct sun measurements by Pandora spectrometers, with respect to their thickness and type, is investigated in detail for the first time. Furthermore, the strength of the correlation between data quality indicators, such as the weighted root-mean-square of the spectral fitting residuals (WRMS), atmospheric variability (ATMVAR) (Cede, 2024), and others, with specific cloud events is investigated and compared with actual cloud flags from PFR stations. The goal is to investigate if the quality indicator limits (Cede et al., 2023) used in the operational processing of the Pandonia Global Network (PGN) do “react” appropriately on cloud conditions.