Retrieval of ice nucleating particle concentrations from lidar observations: Comparison with airborne in-situ measurements from UAVs
Authors: Marinou Eleni , Tesche Matthias , Nenes Athanasios , Ansmann Albert , Schrod Jann , Mamali Dimitra , Tsekeri Alexandra , Pikridas Michael , Baars Holger , Engelmann Ronny , Voudouri Kalliopi-Artemis , Solomos Stavros , Sciare Jean , Groβ Silke , Amiridis Vassilis 
Published: December 2018

Abstract. Aerosols that are efficient ice nucleating particles (INPs) are crucial for the formation of cloud ice via heterogeneous nucleation in the atmosphere. The distribution of INPs on a large spatial scale and as a function of height determines their impact on clouds and climate. However, in-situ measurements of INPs provide sparse coverage over space and time. A promising approach to address this gap is to retrieve INP concentration profiles by combining particle concentration profiles derived by lidar measurements with INP efficiency parametrization for different freezing mechanisms (immersion freezing, deposition nucleation). Here, we assess the feasibility of this new method for both ground-based and space-borne lidar measurements, using airborne in-situ observations from an experimental campaign at Cyprus in April 2016. Analyzing five case studies we calculated the particle number concentrations using lidar measurements (with an uncertainty of 20 to 100 %) and we assessed the suitability of the different INP parameterizations with respect to the temperature range and the type of particles considered. Specifically, our analysis suggests that the parametrization of Ullrich et al. (2017) (applicable for the temperature range −50 °C to −33 °C) agree within 1 order of magnitude with the in-situ observations of nINP and can efficiently address the deposition nucleation pathway in dust-dominated environments. Additionally, the combination of the parameterizations of DeMott et al. (2015) and DeMott et al. (2010) (applicable 15 for the temperature range −35 °C to −9 °C) agree within 2 orders of magnitude with the in-situ observations of nINP and can efficiently address the immersion/condensation pathway of dust and continental/anthropogenic particles. The same conclusion is derived from the compilation of the parameterizations of DeMott et al. (2015) for dust and Ullrich et al. (2017) for soot. Furthermore, we applied this methodology to estimate the INP concentration profiles before and after a cloud formation, indicating the seeding role of the particles and their subsequent impact on cloud formation and characteristics. More synergistic data-sets are expected to become available in the future from EARLINET (European Aerosol Research Lidar NETwork) and in the frame of the European ACTRIS-RI (Aerosols, Clouds and Trace gases Research Infrastructure). Our analysis shown that the developed techniques, when applied on CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) space-born lidar observations, are in very good agreement with the in-situ measurements. This study give us confidence for the production of global 3D products of n250,dry, Sdry and nINP using the CALIPSO 13-yrs dataset. This could provide valuable insight into global height-resolved distribution of INP concentrations related to mineral dust, and possibly other aerosol types.