The Cloud Imaging and Particle Size (CIPS) instrument on the Aeronomy of Ice in the Mesosphere satellite images in the nadir at the UV wavelength of 265 nm. The camera array has an approximately 120° along track (2000 km) by 80° cross track (1000 km) field of view at a horizontal resolution of 1 by 2 km in the nadir. The satellite is in a sun synchronous orbit with an approximately noon local time equator crossing. The observed albedo is due to Rayleigh scattered sun light from an altitude of approximately 50 km and sunlight scattered from Polar Mesospheric Clouds (PMC) which occur in the summer mesosphere at 83 km.

The goal of the CIPS instrument is to retrieve high horizontal resolution maps of PMC albedo and the mode radius of the particle size distribution. The first manuscript analyzes the uncertainties involved in the retrieval. The ability to infer mode radius from the PMC signal is made significantly harder by the presence of the Rayleigh signal. Much of the difference between PMC signals of different mode radii is also consistent with possible changes in the Rayleigh signal. The signal is decomposed into components which isolate the portion of the PMC signal's dependence on radius which is not consistent with changes in the Rayleigh signal. This isolated component is compared with the measurement noise to estimate and understand the uncertainties in the CIPS retrieval.

The presence of the Rayleigh signal is a difficulty in the PMC retrieval, but it is also a valuable data product. The second manuscript highlights the initial findings of a new gravity wave data set developed by the author. The data set provides relative ozone variations at the stratopause with a horizontal resolution of 20 by 20 km. An abundance of gravity wave signatures can be seen in the data which appear to emanate from weather events like thunderstorms and hurricanes as well as orographic sources such as the Andes and the Antarctic Peninsula. The data set fills a gap that presently exists in our observational coverage of gravity waves, so the data set should help significantly in constraining Global Climate Models.