PNNL

Abstract

Knowledge of cloud phase (liquid, ice, mixed, etc) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. A variety of methods, based primarily on decision tree approaches, have been used to identify cloud phase from active remote sensors. These algorithms do not include uncertainty estimates, which contributes an unknown amount of uncertainty to the retrieval of cloud microphysical properties and to model parameterization development and evaluation. In this study, we outline a methodology using a Bayesian classifier to estimate the probabilities of cloud phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the algorithm can be trained and run as an operational cloud phase retrieval. Over 95% of data is identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm.