Liquid, Ice, or Both?

A new algorithm uses ARM remote-sensing measurements and multivariate statistics to determine whether clouds consist of liquid droplets, ice crystals, or both.

The Science

The phase of a cloud (whether it consists of liquid droplets, ice crystals, or both) is an important factor in both the lifetime and radiative impact of a cloud. However, cloud phase is a property that is difficult to simulate correctly in climate models as it depends on interactions among thermodynamic, dynamical, and microphysical processes. A necessary step toward improving climate models is making observations of cloud phase with sufficient accuracy to constrain model representations of the processes that govern cloud phase.

The Impact

A new methodology estimates the probability of a given cloud phase from observations taken by vertically pointing active remote sensors at the Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility. An advantage over previous methods is that the new method includes additional higher-order radar moments and provides uncertainty information on the cloud-phase classification.


This study outlines a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from ARM vertically pointing active remote sensors. The advantage of this method over previous methods is that it provides uncertainty information on the phase classification. The study also tested 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 ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, the study demonstrates 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 study is a first step toward an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.

Principal Investigator(s)

Laura Riihimaki
Pacific Northwest National Laboratory


Research was conducted under the Pacific Northwest National Laboratory’s Laboratory Directed Research and Development Program. Data were obtained from the ARM Climate Research Facility, a DOE Office of Science user facility sponsored by DOE’s Office of Biological and Environmental Research.


Riihimaki, L. D., J. M. Comstock, K. K. Anderson, A. Holmens, and E. Luke. 2016. “A Path Towards Uncertainty Assignment in an Operational Cloud-Phase Algorithm from ARM Vertically Pointing Active Sensors,” Advances in Statistical Climatology, Meteorology and Oceanography 2, 49-62. DOI: 10.5194/ascmo-2-49-2016.