Unraveling secondary ice production in winter orographic clouds through a synergy of in-situ observations, remote sensing and modeling

<p>The representation of orographic clouds in numerical weather prediction models remains a great challenge, as a consequence of our incomplete understanding of the microphysical processes acting on them and the complex interactions between the large-scale and orographic flow dynamics. Mixed-phase conditions are frequently occurring in orographic clouds, highlighting the importance of correctly simulating the microphysical evolution of ice- and liquid-phase hydrometeors. In this study we employ the mesoscale Weather Research and Forecasting (WRF) model to investigate the drivers of intense snowfall events observed during the Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISHTO) campaign, that took place from Fall 2021 to Spring 2022 at Mount Helmos in Peloponnese, Greece. Vertical profiles of reflectivity, Doppler velocity, as well as full Doppler spectra measured by a vertically pointing W-band (94 GHz) Doppler cloud radar, in synergy with Doppler and aerosol depolarization lidar data, help gain insight into the snowfall microphysics involved and set the basis for evaluating the performance of the WRF model. A radar simulator coupled with WRF enables the direct comparison between the mesoscale simulations and remote sensing products, and allows us to find the optimal model set-up that minimizes deviations from the observations. Comparing the modeled ice crystal number concentrations (ICNCs) with the Ice Nucleating Particles (INPs) measured in-situ at the Helmos High Altitude Monitoring Station (2314 m, 42&#176;N 05' 30'', 34&#176;E 14' 25'') by the Portable Ice Nucleation Experiment (PINE) instrument, we seek to quantify the ice enhancement factors due to secondary ice production (SIP) or seeding ice particles and their potential role in enhancing orographic precipitation. The synergy between high-resolution modeling and radar observations gives us the opportunity to infer SIP signatures from remote sensing observations, which is an important outcome given the abundance of the latter.</p>