Deriving Properties of Marine Low Clouds over the Remote Oceans with A-Train

The Southern Ocean is one of the cloudiest regions on Earth and the majority of this cloudiness is in the form of shallow convective clouds based in the marine boundary layer. Our understanding of the processes that modulate marine boundary layer clouds in the Southern Ocean remain poor and models of the Earth's climate system remain challenged to replicate their occurrence. Some of the best information available to the community regarding these clouds is collected by the A-Train constellation of satellites. Early results derived from A-Train data suggest significant swings in cloud droplet number concentrations between summer when cloud droplet number is higher and winter when cloud droplet number is much lower. In this paper we will explore rigorously the capacity for A-Train data to provide information regarding these low level clouds using advanced statistical techniques like Markov Chain Monte Carlo and optimal estimation retrieval algorithms. With knowledge of the information content in the A-Train data, we will explore the implications to deriving an understanding of these clouds from existing data.