Lidar-based Remote Sensing

Lidar (Light Detection and Ranging) has become an indispensable tool in profiling atmospheric properties and has played a significant role in atmospheric science research for several decades. Lidar was first developed in the 1960s for atmospheric research purposes. Initial experiments involved measuring the altitude of cloud layers using pulsed laser beams and detecting the reflected light by the lidar system mounted on the ground. Since then, the lidar system instruments have been deployed to aircrafts and spaceborne satellites to measure the vertical profiles of cloud and aerosol properties with spacial scales spanning from local, regional and global.  In recent years, lidar systems have evolved to include multi-wavelength and high-spectral resolution capabilities. Multi-wavelength lidar provides information about aerosol composition and allows for the differentiation between different aerosol types. High-Spectral Resolution Lidar (HSRL) can robustly obtain the extinction profiles by separating the molecular and elastic backscattering signals. 

While the substantial technological developments in lidar system instruments have been made in recent decades, the progress on the theoretical understanding of the backscattering signals remains significantly slow. This is partly because lack of reliable backscattering theories for large nonspherical particles. Recent advances in the light scattering computational capabilities enable us to explore the physical interpretations of the lidar-derived backscattering signals from atmospheric nonspherical particles. Our group will be deepening our understanding of the backscattering and improve the lidar-based remote sensing techniques in a physically solid manner.   

This figure, adapted from Saito and Yang (2021) demonstrated the sensitivities of the backscattering properties to the dust particle's effective radius. 

Theoretical demonstrations of the lidar-based dust aerosol microphysical property retrieval

Atmospheric mineral dust particles modulate the direct radiative effect (DRE) through scattering and absorption of atmospheric radiation. Significant complexity of dust particles and their geographical dependence lead to substantial variations of dust aerosol optical properties. As a result, current estimates of the dust DRE suffer from significant uncertainties and potentially regional biases.


We develop the bulk mineral dust particle models based on size-resolved particle ensembles with randomly distorted shapes and spectrally resolved complex refractive indices, which are constrained by using in-situ observations reported in the literature. The lidar ratio is more sensitive to particle shape than particle size, while the depolarization ratio depends strongly on to particle size. The simulated bulk backscattering properties (i.e., the lidar ratio and the depolarization ratio) of typical mineral dust particles with effective radii of 0.5–3 µm are reasonably consistent with lidar observations made during several field campaigns. The present dust bulk optical property models are applicable to lidar-based remote sensing of dust aerosol properties. 

This figure, adapted from Saito and Yang (2023), shows the comparisons of the backscattering properties of ice clouds with those from theoretical simulations.  The colors indicate different degrees of small-scale surface irregularities of ice crystals. 

Backscattering properties of ice crystals with small-scale surface roughness: A potential of lidar remote sensing

Ice clouds are among the leading factors to the uncertainties in climate change.  In particular, the ice/liquid phase partitioning is crucial to the estimation of the climate sensitivity in a warmer climate.  However, it has been a challenge to accurately detect (vertically resolved) cloud phases in clouds due to difficulties in the theoretical understanding of the backscattering signals from nonspherical ice crystals.


We performed the theoretical simulations of the backscattering properties of realistic ice crystals with surface roughness based on solving Maxwell's equations for small ice crystals and based on the geometric optics method with a physical optics correction as well as a newly derived coherent backscattering correction. The comparison between the spaceborne lidar observations and the theoretical simulations suggests the robust applicability of the computed ice crystal backscattering property models for lidar-based remote sensing.  This will open up an unprecedented opportunity for us to tackle a long-standing gap in the microphysics of mixed-phase clouds.