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It is well established that the Aerosol-Cloud Interaction (ACI) processes play a key-role in global precipitation and are a strong modulator of cloud radiative forcing and climate, and yet remain poorly understood despite decades of research. Aerosol-cloud interactions are one of the most uncertain aspects of anthropogenic climate change (Seinfeld et al., 2016a; IPCC, 2021).Global datasets on cloud microphysical state – especially droplet number concentration and size distribution – provide important constraints that are required for reducing the ACI uncertainty. Recently, Quaas et al. (2020) showed that satellite remote sensing is the only approach that offers the potential of obtaining global datasets with frequent coverage; current retrieval algorithms, however, carry many uncertainties and require constraints that can only be addressed with in situ and/or ground-based remote sensing observations.Our study aims to evaluate retrievals of cloud droplet number (Nd), effective radius (reff) and optical thickness provided by the CLoud property dAtAset using SEVIRI - Edition 3 (CLAAS-3) cloud products of Satellite Application Facility on Climate Monitoring (CM SAF). For this reason, we used co-located in-situ measurements of aerosols and cloud dynamical properties in conjunction with remote sensing observations at the high-altitude regional background station Hellenic Atmospheric Aerosol and Climate Change (HAC2) during the Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISHTO) campaign, which took place from Fall 2021 to Spring 2022 at Mount Helmos in Peloponnese, Greece (https://calishto.panacea-ri.gr/).In this study, we adopt an approach first applied to droplet retrievals in an urban environment (Foskinis et al. 2022). Ground-based remote sensing instrumentation involved includes a Doppler depolarization lidar (HALO) at 1550 nm to provide the vertical velocity (w) of the air masses, a Doppler cloud radar at 94 GHz (RPG) to provide the equivalent reflectivity factor (Z), and the mean Doppler velocity (VD), and a radiometer at 89 GHz provides the liquid water path (LWP). Furthermore, the in-situ instrumentations employed a co-located scanning Mobility Particle Size (SMPS) measuring the size distribution of submicron aerosol, and a Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM) to provide the aerosol chemical composition of the aerosols. The in-situ dataset together with the airmass vertical velocity distributions are used as input to a state-of-the art parameterization to predict the droplet number (Nd) in clouds formed in the vicinity of the HAC2 station. Retrievals with the the CLAAS-3 cloud properties product from CMSAF are then evaluated with in-situ observations carried out with a cloud probe instrument (PVM-100) and the droplet number calculations.Compared to our previous study (Foskinis et al. 2022), this study is implemented in a different physical system, where we examined again the dependence of the Spectral Dispersion of Droplets (SDD) on Nd and we found a new optimized expression between SDD-Nd which can be used on the established droplet number retrieval algorithm (Bennartz et al., 2007) for non-precipitating planetary boundary layer clouds in order to mitigate the bias. |
