An Albedo-Ice Regression Method for Determining Ice Water Content of Polar Mesospheric Clouds from UV Observations
Thursday, 18 December 2014
Simulations of Polar Mesospheric Cloud (PMC) brightness and ice water content (IWC) are used to develop a simple robust method for IWC retrieval from UV satellite observations. We compare model simulations of IWC with retrievals from the UV Cloud Imaging and Particle Size (CIPS) experiment on board the satellite mission Aeronomy for Ice in the Mesosphere (AIM). This instrument remotely senses scattered brightness related to the vertically-integrated ice content. Simulations from the Whole Atmosphere Community Climate Model (WACCM), a chemistry climate model, is combined with a sectional microphysics model based on the Community Aerosol and Radiation Model for Atmospheres (CARMA). The model calculates high-resolution three-dimensional size distributions of ice particles. The internal variability is due to geographic and temporal variation of temperature and dynamics, water vapor, and meteoric dust. We examine all simulations from a single model day (we chose northern summer solstice) which contains several thousand model clouds. Accurate vertical integrations of the albedo and IWC are obtained. The ice size distributions are thus based on physical principles, rather than artificial analytic distributions that are often used in retrieval algorithms from observations. Treating the model clouds as noise-free data, we apply the CIPS algorithm to retrieve cloud particle size and IWC. The inherent “errors” in the retrievals are thus estimated. The linear dependence of IWC on albedo makes possible a method to derive IWC, called the Albedo-Ice regression method, or AIR. This method potentially unifies the variety of data from various UV experiments, with the advantages of (1) removing scattering-angle bias from cloud brightness measurements,(2) providing a physically-useful parameter (IWC),(3) deriving IWC even for faint clouds of small average particle sizes, and (4) estimating the statistical uncertainty as a random error, which bypasses the need to derive particle size.