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

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 additionalmore » information from the Doppler spectrum provides substantial improvement to the algorithm. As a result, this is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.« less
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  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Report Number(s):
PNNL-SA-112801; BNL-112562-2016-JA
Journal ID: ISSN 2364-3587
Grant/Contract Number:
AC05-76RL01830; Pacific Northwest National Laboratory LDRD; SC00112704
Published Article
Journal Name:
Advances in Statistical Climatology, Meteorology and Oceanography (Online)
Additional Journal Information:
Journal Name: Advances in Statistical Climatology, Meteorology and Oceanography (Online); Journal Volume: 2; Journal Issue: 1; Journal ID: ISSN 2364-3587
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1324907; OSTI ID: 1336115