Using GEOS-Chem to design a cloud-slicing retrieval algorithm for application to TROPOMI formaldehyde and carbon monoxide

The cloud-slicing retrieval technique has yielded new datasets of atmospheric composition in the free troposphere from satellite observations. The retrieval involves isolating clusters of satellite pixels above optically thick clouds from single overpasses or scans before regressing total column densities against corresponding cloud top heights to obtain a regression slope that is then converted to a single mixing ratio value representative of the average concentration of a target compound within the range of cloud top heights sampled. Recent datasets obtained with cloud-slicing include vertically-resolved concentrations of NO₂ and O₃ for multiple free tropospheric layers from TROPOMI and single-layer free tropospheric concentrations of NO₂ from TEMPO. Cloud-slicing for both NO₂ and O₃ suffers substantial data loss, as many clusters with non-uniform overlying stratosphere need to be discarded, due to the contribution of stratospheric variability to the regression slope. Cloud-slicing is yet to be tested on compounds sufficiently abundant in the free troposphere and without contamination from the stratosphere, namely formaldehyde (HCHO) and carbon monoxide (CO). Here, GEOS-Chem is used to generate pseudo-observations of HCHO and CO over target domains with distinct characteristics. Specifically, the remote troposphere (Pacific Ocean), and regions influenced by biomass burning (southern Africa) and anthropogenic pollution (South Asia). Cloud-slicing is applied to these pseudo-observations to tailor the retrieval steps to yield cloud-sliced mixing ratios that are consistent with the “true” mixing ratios as simulated by the model. According to preliminary data so far obtained for southern Africa in June-August, the peak of the burning season, lack of stratospheric contribution and greater data retention from cloud-slicing HCHO and CO total columns reduces noise in the cloud-sliced data, resulting in seasonal means that are more consistent with the "truth" than was possible with NO₂ and O₃. Cloud-sliced seasonal mean mixing ratios of HCHO and CO are typically within 5-10% of the “true” simulated mixing ratios and also achieve spatial consistency (R > 0.7). Though, cloud-sliced mixing ratios do underestimate large enhancements in HCHO and CO over the most intense biomass burning gridboxes. Work is underway to determine the cause for the bias over large sources, apply cloud-slicing to the other domains, explore the added value of free tropospheric HCHO and CO for understanding the oxidative capacity of the atmosphere, and quantify error contributions, including the representation error induced by sampling very cloudy scenes. Following this cloud-slicing characterisation, the algorithm developed with synthetic experiments will be applied to TROPOMI HCHO and CO data products to further extend the utility of Earth observations.