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Radar (Radio Detection and Ranging) and Lidar (Light Detection and Ranging) have become an indispensable tool in profiling atmospheric properties and have played a significant role in atmospheric science research for several decades. Radar was first developed in the 1930s for military purposes and then applied for weather monitoring in the late 1940s. Lidar was first developed in the 1960s for atmospheric research purposes. Since then, radars and lidars have been used for atmospheric science research in conjunction with the establishment of the light scattering and radiative transfer theories. In recent years, radar and lidar systems have evolved to include multi-wavelength and sophisticated capabilities such as phased-array radars (PAR) and high-spectral resolution lidars (HSRL).
Radars and lidars have been deployed on airborne platforms for atmospheric science research since the 1950s and 1970s, respectively. The strengths of the airborne radar-lidar instruments are their capabilities to profile the in-cloud and aerosol plume structures with fine spatial scales. Our group is exploring these capabilities to develop the radar- and lidar-based remote sensing algorithms in a physically solid manner and characterize the cloud/aerosol spatial structures at scales not accessible by the measurements from spaceborne platforms.
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
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 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
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.
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