AUTHOR=Foskinis Romanos , Nenes Athanasios , Papayannis Alexandros , Georgakaki Paraskevi , Eleftheriadis Konstantinos , Vratolis Stergios , Gini Maria I. , Komppula Mika , Vakkari Ville , Kokkalis Panos 

TITLE=Towards reliable retrievals of cloud droplet number for non-precipitating planetary boundary layer clouds and their susceptibility to aerosol

JOURNAL=Frontiers in Remote Sensing

VOLUME=Volume 3 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2022.958207

DOI=10.3389/frsen.2022.958207

ISSN=2673-6187

ABSTRACT=Remote sensing has been a key resource for developing extensive and detailed datasets for studying and constraining aerosol-cloud-climate interactions. However, aerosol-cloud collocation challenges, algorithm limitations, as well as difficulties in unraveling dynamic from aerosol-related effects on cloud microphysics, have long challenged precise retrievals of cloud droplet number concentrations. By combining a series of remote sensing techniques and in situ measurements at ground level, we developed a semi-automated approach that helps address several retrieval issues for a robust estimation of cloud droplet number for non-precipitating Planetary Boundary Layer (PBL) clouds. The approach is based on satellite retrievals of the PBL droplet number (Ndsat) using the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) geostationary meteorological satellite data combined with the Optimal Cloud Analysis (OCA) product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The parameters of the retrieval are optimized through closure with droplet number obtained from a combination of ground-based remote sensing data and in situ observations at ground level. More specifically, the remote sensing data are used to retrieve cloud-scale vertical velocity, while the in situ aerosol measurements at ground level were used to determine the aerosol size distribution and the chemical composition. Finally, we used all these data as input to a state-of-the-art droplet activation parameterization to predict the respective Cloud Condensation Nuclei (CCN) spectra, cloud maximum supersaturation and droplet number concentration (Nd). 
Closure studies between collocated Nd and Ndsat are then used to determine the optimal retrieval and filtering algorithm for remote sensing of droplet number. This methodology is used to study aerosol-cloud interactions for non-precipitating clouds formed over the Athens Metropolitan Area (AMA), Greece, during the springtime period (March to May) of 2020 and shows that droplet closure can be achieved to within 30%, comparable to the level of closure obtained in many in situ studies. Given this, the ease of applying the approach, and the high temporal (15 min) and spatial resolution (3 km) of the Rapid Scan High Rate SEVIRI opens the possibility of continuous and reliable Ndsat retrievals of high value datasets for aerosol-cloud-climate interaction studies.