Abstract
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. 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.
Highlights
Cloud feedbacks are one of the largest uncertainties in global climate model simulations of future climates, limited in part by a lack of observations with sufficient and known accuracy to constrain cloud microphysical parameterizations (Stephens, 2005; IPCC, 2013)
The focus of this study is the development of an algorithm that identifies cloud phase from vertically pointing radars and lidars at the ARM (Atmospheric Radiation Measurement) Climate Research Facility that estimates the uncertainty of that identification
This study describes an algorithm proof of concept, using data from the Mixed-Phase Arctic Cloud Experiment (MPACE) field campaign when aircraft in situ measurements are available along with vertically pointing lidar and radar measurements to help train and evaluate the algorithm
Summary
Cloud feedbacks are one of the largest uncertainties in global climate model simulations of future climates, limited in part by a lack of observations with sufficient and known accuracy to constrain cloud microphysical parameterizations (Stephens, 2005; IPCC, 2013). Riihimaki et al.: Cloud phase from active sensors flag times when instruments indicate detection of small liquid drops, falling hydrometeors, and melting ice along with temperature information to give likely hydrometeor classifications (Hogan and O’Connor, 2006) Both of these decisiontree methods are based on well-established scientific understanding of instrument sensitivities to hydrometeors, but do not quantify the uncertainty of the phase assignment. Yu et al (2014) built on this work and used wavelet transforms to deconvolve liquid peaks in the Doppler spectra from other signals These studies show that a good deal of information is available within the Doppler spectra to identify liquid within a cloud in addition to the high sensitivity to ice. The goal of this study is to test the value of two potential improvements to previous decision-tree approaches to operational phase identification algorithms.
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