Maximizing the scientific application of Pandora column observations of HCHO and NO₂

<p>As part of the Pandonia Global Network (PGN), Pandora spectrometers are widely deployed around the world. These ground-based remote-sensing instruments are federated such that they employ a common algorithm and data protocol for reporting on trace gas column densities and lower-atmospheric profiles using two modes based on direct-sun and sky-scan observations. To aid users in the analysis of Pandora observations, the PGN standard quality flagging procedure assigns flags to the data indicating high, medium, and low quality. This work assesses the suitability of these data quality flags for filtering data in the scientific analysis of formaldehyde (HCHO) and nitrogen dioxide (<span class="inline-formula">NO₂</span>), two critical precursors controlling tropospheric ozone production. Pandora data flagged as high quality assure scientifically valid data and are often more abundant for direct-sun <span class="inline-formula">NO₂</span> columns. For direct-sun HCHO and for sky-scan observations of both molecules, large amounts of data flagged as low quality also appear to be valid. Upon closer inspection of the data, independent uncertainty is shown to be a better indicator of data quality than the standard quality flags. After applying a filter to independent uncertainty, Pandora data flagged as medium or low quality in both modes can be demonstrated to be scientifically useful. The utility of this filtering method is demonstrated by correlating contemporaneous but independent direct-sun and sky-scan observations. When evaluated across 15 Pandora sites in North America, this new filtering method can recover as much as 90 % of data that would have previously been discarded. This method suggests that standard PGN criteria for atmospheric variability and the normalized root mean squared error are too stringent, as they are responsible for downgrading most of the recovered data. A method is also developed for combining the direct-sun and sky-scan observations into a single dataset by accounting for biases between the two observing modes and differences in measurement integration times. These combined data provide a more continuous record that is useful for interpreting Pandora observations against other independent variables such as hourly observations of surface ozone. When Pandora HCHO columns are correlated with surface ozone measurements, data filtered by independent uncertainty exhibit similarly strong and more robust relationships than high-quality data alone. These results suggest that Pandora data users should carefully assess data across all quality flags and consider their potential for useful application to scientific analysis. The present study provides a method for maximizing use of Pandora data with the expectation of more robust satellite validation and comparisons with ground-based observations in support of air quality studies.</p>