Development of Optimal Ice Cloud Optical Property Models for Remote Sensing Applications

Abstract

This dissertation develops optimal cloud ice particle optical property models used for remotely sensed data from multi-angular satellite sensors. The optimal degree of surface roughness is inferred from Multi-angle Imaging SpectroRadiometer (MISR) measurements. The results show a latitudinal dependency in the optimal degree of ice particle roughness on a global scale. The optimal model for thick homogeneous clouds corresponds to more roughened ice particles in the tropics than in the extra-tropics. Furthermore, the inferred optimal ice particle roughness model is applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) and MISR data to retrieve the optical thickness and effective radius of the ice cloud. The retrievals indicate a larger median optical thickness by 10.1% and a smaller median effective radius by 6.5% on the pixel-level, compared to the operational MODIS Collection 6 products. In addition to these results, two algorithms are developed to infer the optimal ice particle model. The first algorithm is designed to work with a multi-angular sensor with polarimetric measurements and has been tested using data from a prototype aircraft-mounted sensor. The other algorithm uses multispectral measurements, specifically a combination of shortwave bands and thermal infrared (IR) bands, for performing retrievals of the optimal ice particle shape. This analysis includes a comparison of retrievals between multispectral and multi-angular techniques.