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

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 our 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 then 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. Furthermore, when lidar data are not available, including additionalmore » 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.« less

Authors:
ORCiD logo; ORCiD logo; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1256528
Alternate Identifier(s):
OSTI ID: 1324907; OSTI ID: 1336115
Report Number(s):
PNNL-SA-112801; BNL-112562-2016-JA
Journal ID: ISSN 2364-3587
Grant/Contract Number:  
AC05-76RL01830; SC00112704
Resource Type:
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
Publisher:
Copernicus Publications
Country of Publication:
Germany
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 97 MATHEMATICS AND COMPUTING

Citation Formats

Riihimaki, Laura D., Comstock, Jennifer M., Anderson, Kevin K., Holmes, Aimee, and Luke, Edward. A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors. Germany: N. p., 2016. Web. https://doi.org/10.5194/ascmo-2-49-2016.
Riihimaki, Laura D., Comstock, Jennifer M., Anderson, Kevin K., Holmes, Aimee, & Luke, Edward. A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors. Germany. https://doi.org/10.5194/ascmo-2-49-2016
Riihimaki, Laura D., Comstock, Jennifer M., Anderson, Kevin K., Holmes, Aimee, and Luke, Edward. Fri . "A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors". Germany. https://doi.org/10.5194/ascmo-2-49-2016.
@article{osti_1256528,
title = {A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors},
author = {Riihimaki, Laura D. and Comstock, Jennifer M. and Anderson, Kevin K. and Holmes, Aimee and Luke, Edward},
abstractNote = {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 our 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 then 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. Furthermore, 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.},
doi = {10.5194/ascmo-2-49-2016},
journal = {Advances in Statistical Climatology, Meteorology and Oceanography (Online)},
number = 1,
volume = 2,
place = {Germany},
year = {2016},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
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https://doi.org/10.5194/ascmo-2-49-2016

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    Works referencing / citing this record:

    Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm
    journal, January 2019

    • Kalesse, Heike; Vogl, Teresa; Paduraru, Cosmin
    • Atmospheric Measurement Techniques, Vol. 12, Issue 8
    • DOI: 10.5194/amt-12-4591-2019