Wednesday, August 10 - Posters

Advances in the theory of polarimetric remote sensing

• Chamara Rajapakshe - Investigating the 3D radiative transfer effects in the polarimetric cloud retrievals based on large-eddy simulated cloud fields and 3D radiative transfer simulations

• Himadri S. Das - Albedo and linear polarization of fractal aggregates: Effect of porosity

• V Sergey Savenkov - Depolarization metric spaces for natural scattering scenes classification

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Scattering of lights by terrestrial aerosols, clouds, oceans, and land surfaces

• Eduard Chemyakin - Look-up table for lidar and polarimeter liquid water cloud studies

• Masahiro Momoi - Efficient calculation of Stokes radiance vector including solar aureole regions in plane-parallel atmospheres

• Neranga K. Hannadige - Seeking optimal bio-optical models for remote sensing of ocean color

• V Qiaoyun Hu - Variability of biomass burning aerosol properties in North American wildfires in 2020

• V Smita Rani - Panda Conversion of SO2 gas to sulphate aerosols in volcanic plumes from the joint analysis of polar-orbiting POLDER and OMI satellite observations

• Victor A. Shishko - Join the DDA and physical optics solutions to the problem of light backscattering by ice crystals of cirrus

• Wenbo Sun - Partially melting droplets may cause MODIS-retrieved water cloud particle sizes larger than polarimeter-retrieved ones

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Polarimetric applications in atmospheric and planetary science

• Oleksandra Ivanova The scattering properties of dust in active asteroid (248370) 2005 QN173

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Improvement of polarimetric instrumentation quality and information content

• Guangfeng Xiang Geometric Calibration of Directional Polarimetric Camera

• Lisa W. Li Accuracy of a Polarization Metagrating Camera for Stokes Imaging

• V Stephen Broccardo Robotic sunphotometry to enable over-ocean aerosol characterization

• Xiaoguang (Richard) Xu Level-1 Data of the HARP2 onboard NASA's PACE Satellite

• Xuefeng Lei Data comparison and cross calibration/validation between DPC and POSP onboard Chinese GaoFen-5(02) satellite

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Development of advanced retrieval algorithms and data processing

• Chong LI Advanced Aerosol Characterization from Polarized Imaging Nephelometer (PI-Neph) using GRASP algorithm

• Guangliang Fu Advancing retrieval techniques for polarimetric remote sensing of atmospheric aerosols

• James Allen - The PACE-MAPP polarized ocean bio-optical model

• Jason L. Pippitt A Python-based Radar Data Processing System for NASA's GPM Ground Validation Program

• Mikhail D. Alexandrov Investigation of Cu cloud interior structure using a tomographic technique based on Radon Transform. Application to the measurements made by the Research Scanning Polarimeter during CAMP2Ex

• Rachel E. Smith Using HARP to retrieve CDR and CDV variables using a Nakajima-King-like polarized method

• V Shikuan Jin Performance of AOD Retrieval from DPC/Gaofen-5 based on GRASP/Models

• Siyao Zhai Retrieving water reflectance and marine biogeochemical properties with the GRASP algorithm

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Current and forthcoming satellite missions and field campaigns

• Anna Gialitaki Aerosol property retrievals by means of multi-angular, multi-spectral polarimetric measurements

• Daniel Perez Ramirez Developments in GRASP algorithm for the retrievals of aerosol properties vertically-resolved combining multiwavelength backscattered lidar and polarimetry: Application to ACEPOL field campaign data

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Applications of polarimetric observations

• Cheng Chen Constraining global aerosol absorption from multi-angular polarimetric remote sensing

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Databases, software, and artificial intelligence / machine learning applications

• A. Lopatin Using GRASP software for global estimation of ground-level particulate matter on the base of multi-angular polarimetric remote sensing

• Kamal Aryal Instantaneous Photosynthetically Available Radiation (IPAR) Prediction models based on Neural Network for ocean waters

• Patrick Stegmann Polarized Radiative Transfer with the CRTM v3.0 Release

• X. Chen Analytical prediction of scattering properties of spheroidal dust particles with machine learning