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Abstract. Accurate representation of microphysical properties of atmospheric aerosol particles – such as number, mass and cloud condensation nuclei (CCN) concentration – is key to constraining climate forcing estimations and improving weather and air quality forecasts. Lidars capable of vertically resolving aerosol optical properties have been increasingly utilized to study aerosol-cloud interactions, allowing for estimations of cloud-relevant microphysical properties. Recently, lidars have been employed to identify and monitor pollen particles in the atmosphere, an understudied aerosol particle with health and possibly climate implications. Lidar remote sensing of pollen is an emerging research field, and in this study, we present for the first time retrievals of particle number, mass, CCN, giant CCN (GCCN) and ultra–giant CCN (UGCCN) concentration estimations of birch pollen derived from polarization lidar observations and specifically from a Vaisala CL61 ceilometer. A pivotal role in these estimations is played by the conversion factors necessary to convert the optical measurements into microphysical properties. This set of conversion parameters for birch pollen is derived from in situ observations of major birch pollen events in Vehmasmäki station in Eastern Finland in 2021. Then, the conversion factors are applied to ground-based lidar observations and compared against in situ measurements of aerosol and pollen particles. In turn, this demonstrates the potential of ground-based lidars such as a ceilometer network with polarization capacity to document large-scale birch pollen outbursts in detail and thus to provide valuable information for climate, cloud, and air quality modeling efforts, elucidating the role of pollen within the atmospheric system.
Filioglou et al. (Wed,) studied this question.