Development of a physics-based lidar remote sensing algorithm to characterize dust microphysical properties.

Mineral dust particles significantly impact the atmosphere by directly modulating radiative balance through scattering, absorption, and emission and by acting as cloud condensation and ice nuclei, which can alter cloud formation, modify precipitation rates, and influence the dynamics of cloud systems. Lidar measurements are useful to characterize the vertical profiles of dust plume’s properties. Accurate retrievals of the microphysical properties of dust particles from lidar measurements have been challenging due to the fact that the lidar-based remote sensing techniques rely on empirical assumptions of the lidar signals, which hampers the interpretation of the lidar signals into the microphysical properties of dust. Recent advancements in the light scattering computational capabilities reveal the relationship between the lidar backscattering signals and the dust microphysical properties and has paved the way for us to undertake this research.

The ultimate goal of this study is to develop a physics-based remote sensing algorithm for characterizing the microphysical properties of dust plumes using lidar observations. We aim to develop a physics-based remote sensing algorithm for the characterization of the vertical profiles of the dust microphysical properties. We incorporate particle size distributions, particle shapes, and mineralogical compositions into the bulk dust optical property models. The dust optical property models developed based on the TAMUdust2020 dust scattering property database are incorporated into a lidar simulator. Sensitivity tests of particle size distributions derived from the brittle fragmentation theory highlight the importance of a crack length (λ) to the effective radius. The results revealed that the lidar ratio and depolarization ratio were sensitive to the effective radius of dust particles, with λ emerging as the most critical factor and the sensitivities of lidar ratio being most pronounced at 0.355 µm among three lidar wavelengths. In the presentation, we will show an example retrieval using SALTRACE.