Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> The paper presents an approach to revealing the variability in aerosol type, at high spatiotemporal resolution, by combining fluorescence and MieâRaman lidar observations. The multiwavelength MieâRaman lidar system in operation at the ATOLL (ATmospheric Observation at liLLe) platform, Laboratoire d'Optique Atmosphérique, University of Lille, has included, since 2019, a wideband fluorescence channel allowing the derivation of the fluorescence backscattering coefficient <span class="inline-formula"><i>β</i><sub>F</sub></span>. The fluorescence capacity <span class="inline-formula"><i>G</i><sub>F</sub></span>, which is the ratio of <span class="inline-formula"><i>β</i><sub>F</sub></span> to the aerosol backscattering coefficient, is an intensive particle property, strongly changing with aerosol type, thus providing a relevant basis for aerosol classification. In this first stage of research, only two intensive properties are used for classification, namely the particle depolarization ratio at 532ânm, <span class="inline-formula"><i>δ</i><sub>532</sub></span>, and the fluorescence capacity, <span class="inline-formula"><i>G</i><sub>F</sub></span>. These properties are considered because they can be derived at high spatiotemporal resolution and are quite specific to each aerosol type. In particular, in this study, we use a <span class="inline-formula"><i>δ</i><sub>532</sub></span>â<span class="inline-formula"><i>G</i><sub>F</sub></span> diagram to identify smoke, dust, pollen, and urban aerosol particles. We applied our new classification approach to lidar data obtained during the 2020â2021 period, which includes strong smoke, dust, and pollen episodes. The particle classification was performed with a height resolution of about 60âm and temporal resolution better than 8âmin.
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