Professor Roy Grainger

Abstract:

Due to similarities in their radiometric signature, it is not possible to retrieve aerosol and cloud properties simultaneously from satellite imagery. A plethora of filtering techniques have been developed to ensure aerosol and cloud are analysed separately, but this neglects the scientifically interesting regions of interaction between the two. It also limits the spatial coverage of such products, with up to 20% of the planet neglected because it is considered too cloudy to be suitable for an aerosol retrieval but insufficiently so for a cloud retrieval. The Optimal Retrieval of Aerosol and Cloud (ORAC) is a single algorithm that can retrieve the aerosol or cloud properties consistent with a single measurement. By performing radiative transfer calculations via look-up tables, various types of particle can be considered in parallel — such as liquid-phase cloud, different models of ice nuclei, and various clean and polluted aerosols — by simply running the program repeatedly using tables assuming different microphysical properties and vertical distributions. Bayesian statistics can determine the probability that the scene contains a specific species, classifying it as aerosol, cloud, or uncertain. The important but infrequently discussed `uncertain' region can then be used to investigate the impact of contamination and data coverage on existing products by, for example, observing how retrieved aerosol optical thickness varies as a function of the distance from the nearest cloud. It also provides a potential window for the study of aerosol-cloud interactions.