Journal cover Journal topic
Advances in Statistical Climatology, Meteorology and Oceanography An international open-access journal on applied statistics
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 49-62, 2016
https://doi.org/10.5194/ascmo-2-49-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
10 Jun 2016
A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors
Laura D. Riihimaki1, Jennifer M. Comstock1, Kevin K. Anderson1, Aimee Holmes1, and Edward Luke2 1Pacific Northwest National Laboratory, Richland, Washington, USA
2Brookhaven National Laboratory, Upton, New York, USA
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.

Citation: Riihimaki, L. D., Comstock, J. M., Anderson, K. K., Holmes, A., and Luke, E.: A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors, Adv. Stat. Clim. Meteorol. Oceanogr., 2, 49-62, https://doi.org/10.5194/ascmo-2-49-2016, 2016.
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Short summary
Between atmospheric temperatures of 0 and −38 °C, clouds contain ice crystals, super-cooled liquid droplets, or a mixture of both, impacting how they influence the atmospheric energy budget and challenging our ability to simulate climate change. Better cloud-phase measurements are needed to improve simulations. We demonstrate how a Bayesian method to identify cloud phase can improve on currently used methods by including information from multiple measurements and probability estimates.
Between atmospheric temperatures of 0 and −38 °C, clouds contain ice crystals, super-cooled...
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