Advanced Aerosol Retrievals with RemoTAP and PARASOL: Enhancing Understanding of Aerosol-Cloud Interactions

The precise retrieval of aerosol properties from satellite data is pivotal for advancing our understanding of their impacts on climate and air quality. The RemoTAP (Remote Sensing of Trace Gas and Aerosol Products) algorithm represents a significant leap forward, leveraging data from multi-angle polarimeters (MAPs), such as the past PARASOL-POLDER instrument, the current PACE-SPEXone and the future Metop-SG-3MI and CO2M-MAP instruments. A unique ability of these instruments to measure both the intensity and polarization of sunlight across multiple wavelengths and viewing angles offers an unparalleled dataset for aerosol characterization, including number concentrations, size distributions, and refractive indices. We have substantially enhanced the RemoTAP results by integrating improved cloud fraction values derived from MAPs using a neural network approach, ensuring more accurate aerosol retrievals through better cloud filtering techniques. To further elevate data quality, advanced quality filters utilizing multiple key metrics were developed, effectively enhancing data integrity, resulting in a more refined aerosol dataset essential for precise atmospheric analysis. The validation of these enhancements involved comparisons with ground-based AERONET (Aerosol Robotic Network) observations over 284 sites, demonstrating the reliability of RemoTAP-derived aerosol properties. Furthermore, a pixel-level cross-comparison was carried out with GRASP-derived PARASOL-based aerosol data, as RemoTAP and GRASP are similar kind of algorithms for polarimetric measurements. The scientific implications of these advancements are profound, as the improved retrieval of aerosol size and composition using advanced polarimetric observations directly refines the estimation of cloud condensation nuclei (CCN) (proxy) concentrations and consequently the global CCN-Nd (cloud droplet number concentration) relationship. This refined relationship is crucial for understanding aerosol-cloud interactions, allowing for more accurate quantification of aerosol-induced cloud albedo changes, thereby reducing uncertainties in radiative forcing estimates due to aerosol-cloud interactions (RFaci). Such improvements contribute to a more precise representation of aerosol impacts in climate models, ultimately enhancing predictions of climate sensitivity and future warming scenarios. By advancing the RemoTAP algorithm, our findings underscore the transformative potential of these methodologies in delivering accurate and reliable aerosol climatology, driving forward the frontier of atmospheric science and climate research.