Estimating Planetary Boundary Layer Influence at the Helmos Hellenic Atmospheric Aerosol & Climate Change (HAC)2 Station Using Remote Sensing Techniques and In-Situ Measurements
OLGA ZOGRAFOU, Maria Gini, Prodromos Fetfatzis, Konstantinos Granakis, Romanos Foskinis, Christos Mitsios, Carolina Molina, Aiden Jönsson, Paul Zieger, Mika Komppula, Alexandros Papayannis, Athanasios Nenes, Konstantinos Eleftheriadis, NCSR Demokritos, Athens, Greece
Abstract Number: 330
Working Group: Remote and Regional Atmospheric Aerosol
Abstract
Aerosol-cloud interactions introduce significant uncertainties in climate models, and thus their study under real-time conditions by in-situ measurements is essential in the effort to mitigate climate change. High-altitude stations (HAS) offer great possibilities to investigate these interactions, particularly cloud formation processes with respect to aerosol availability, air mass origin and air mass type. HAS often reside in the Free Troposphere (FT). A challenging issue in HAS measurements analysis is the segregation between FT and Planetary Boundary Layer (PBL) influenced air masses, as the definition and quantification of the PBL are inherently vague.
The Helmos Hellenic Atmospheric Aerosol and Climate Change ((HAC)²) station in Greece (2314 m a.s.l.) is the only HAS in the eastern Mediterranean. The CALISHTO (Cloud-Aerosol InteractionS in the Helmos Background TropOsphere) and CHOPIN (Cleancloud Helmos OrograPhic sIte experiment) campaigns were conducted at Mount Helmos during the autumn-winter periods of 2021–2022 and 2024–2025, respectively, with the scope of enhancing the understanding on the processes driving mixed-phase clouds formation. Both in-situ (GHGs, aerosol size distributions, aerosol optical properties, meteorological data, etc) and remote sensing (Doppler lidar) measurements at a number of sites and the (HAC)2 were used to investigate PBL influence at (HAC)².
A set of metrics was established, including the water vapor mixing ratio, the accumulation mode (particles with a diameter > 95 nm) number concentration, and the ratio of eBC to CO, as proxies for estimating PBL influence at (HAC)2 without remote sensing measurements. A logistic regression model was trained using periods with both in-situ and remote sensing data, enabling PBL influence estimation from in-situ metrics alone. The model was tested on a subset of the common dataset, and the overall agreement between remote sensing and model predictions exceeded 80 %.