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=3

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=<p>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 <italic>in situ</italic> measurements at ground level, we developed a semi-automated approach that can 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 cloud droplet number (<italic>N</italic><sub><italic>d</italic></sub><sup><italic>sat</italic></sup>) using the geostationary meteorological satellite data of the Optimal Cloud Analysis (OCA) product, which is obtained by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) 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 <italic>in situ</italic> observations at ground level. More specifically, the remote sensing data are used to retrieve cloud-scale vertical velocity, and the <italic>in situ</italic> aerosol measurements at ground level were used constrain 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 (<italic>N</italic><sub><italic>d</italic></sub>), accounting for the effects of vertical velocity distribution and lateral entrainment. Closure studies between collocated <italic>N</italic><sub><italic>d</italic></sub> and <italic>N</italic><sub><italic>d</italic></sub><sup><italic>sat</italic></sup> are then used to evaluate exising droplet spectral width parameters used for the retrieval of droplet number, and determine the optimal values for retrieval. This methodology, used to study aerosol-cloud interactions for non-precipitating clouds formed over the Athens Metropolitan Area (AMA), Greece, during the springtime period from March to May 2020, shows that droplet closure can be achieved to within ±33.4%, comparable to the level of closure obtained in many in situ studies. Given this, the ease of applying this approach with satellite data obtained from SEVIRI with high temporal (15 min) and spatial resolution (3.6 km × 4.6 km), opens the possibility of continuous and reliable <italic>N</italic><sub><italic>d</italic></sub><sup><italic>sat</italic></sup>, giving rise to high value datasets for aerosol-cloud-climate interaction studies.</p>