A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors

• Published in: Advances in Statistical Climatology, Meteorology and Oceanography
• Main Authors: L. D. Riihimaki, J. M. Comstock, K. K. Anderson, A. Holmes, E. Luke
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2016
• Subjects: Oceanography; GC1-1581; Meteorology. Climatology; QC851-999; Probabilities. Mathematical statistics; QA273-280; Arctic
• Online Access: https://doi.org/10.5194/ascmo-2-49-2016; https://doaj.org/article/801620a1bc774acd8d1f5f0aa0c402bb

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. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty information on the phase classification. 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 method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are 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. This is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.