Left image: different aerosol emission sources (dust storm, wildfires, industrial emissions, and volcano eruption); Middle image: NASA Multi-angle Imaging SpectroRadiometer (MISR, Diner et al. 1998) for aerosol and cloud remote sensing; Right image: Satellite remote sensing of aerosol properties provides an effective tool to investigate aerosol impact on public health – which is the target of NASA Earth-Venture-Instrument Multi-Angle Imager for Aerosols (MAIA) (Diner et al. 2018).
Atmospheric aerosols, clouds, and trace gases are key factors that influence global climate, radiative budget of the Earth-Atmosphere system, and air pollution. With the purpose of utilizing and developing active and passive radiation measurements for characterizing spatio-temporal distributions and radiative properties of aerosols, clouds and trace gases, my group focuses on the development of remote sensing theory in three fundamental aspects, which include
Remote sensing inversion. We aim at establishing a reliable remote sensing inversion system to improve the retrievals of aerosol and cloud properties by a) using observations that combines subsets of multi-angle, multi-spectral/hyperspectral, and/or polarimetric measurements; b) imposing multiple types of model and physical constraints; c) using a priori aerosol and cloud information from transport model or from assimilation. Our inversion system also allows simulating new types of measurements for designing the next-generation sensors for aerosol and cloud remote sensing.
Left image: Cloud radiance and polarization images acquired by JPL’s AirMSPI (Diner et al. 2013) during ORACLES field campaign over Southern Africa (Redemann et al. 2019, in preparation); Middle image: Retrieved pixel-scale (25-meter) cloud optical depth and cloud-top droplet effective radius; Right image: Retrieved above-cloud aerosol optical depth at a set of wavelengths indicated in sub-panel titles, cloud optical depth (COD), cloud droplet effective radius (reff) and dispersion parameter (veff). The AirMSPI inversion algorithm was developed by Xu et al. (2018). Retrieval validation was made against the reference data provided by NASA Langley HSRL-2 and NASA GISS RSP teams.
Atmospheric radiative transfer. We aim at developing fast yet accurate one-dimensional (plane-parallel) and three-dimensional polarized radiative transfer models for modeling multi-angle, multi-spectral/hyperspectral, and/or polarimetric measurements. These models are for use by remote sensing inversion, climate models and data assimilation which involves a direct use of radiance.
Upper left image: Saturn’s 6th moon – Titan as seen by the Cassini spacecraft (Credit: NASA). Lower left image: Cartoon of Huygens Probe landing on Titan (Credit: NASA); Upper right images: Measured Stokes parameter I (intensity) and Q images of Titan by Cassini’s Imaging Science Subsystem (ISS); Lower right images: Modeled I and Q images using the radiative transfer code for spherical-shell atmosphere (Xu et al., 2013).
Electromagnetic light scattering by small particles. We perform research on modeling absorption and scattering properties of small particles and to understand a) the impact of aerosols and cloud particles on radiative budget of the Earth-Atmosphere system; and b) various phenomena created by small particles in nature, lab and industrial processes.
Left image: An experimental setup for measuring and tracking rainbow scattering pattern of an acoustically levitated droplet (~1mm). By adjusting acoustic pressures, the droplet gradually changing its shape from spherical to oblate (Yu et al. 2013); Right image: Recorded rainbow scattering patterns when the droplet is deformed into oblate shapes – characterized by a set of axis ratios (a/b = 1.0, 1.1, 1.2, and 1.3). The modeled images for deformed rainbow (right bottom) associated with oblate droplets were calculated using the theory of Debye series developed for a non-spherical particle developed Xu et al. (2010).