Abstract: Marine boundary layer clouds reflect a greater amount of radiation back to space compared with the underlying ocean surface, while emitting a similar amount of long-wave radiation. Hence, these clouds have a net cooling effect on the Earth’s surface, making them an important component of the Earth’s radiation budget. Although important, these clouds are poorly represented in the Earth System Model (ESM) used for predicting the future climate for various reasons, one being our lack of understanding of the basic processes modulating these cloud systems.
To address this need, we use data collected by various satellite and ground-based active and passive remote-sensing instruments to derive cloud macro- and micro-physical properties, and the thermodynamic state of the boundary layer. In this seminar we will present the theoretical mathematical framework used to extract information from remotely sensed measurements, and we will give specific examples of a few inversion techniques traditionally used in atmospheric remotes sensing. Finally, we will present the challenges encountered in the application of such techniques to real measurements and potential ways in which a machine learning framework could help to improve the current state of the art and aid the science produced from the observations.