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Radar Icing Algorithm: Algorithm Description and Comparison with Aircraft Observations

David J. SerkeaResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Scott M. EllisaResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Sarah A. TessendorfaResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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David E. AlboaResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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John C. HubbertbEarth Observing Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Julie A. HaggertyaResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado
bEarth Observing Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Abstract

Detection of in-flight icing hazard is a priority of the aviation safety community. The “Radar Icing Algorithm” (RadIA) has been developed to indicate the presence, phase, and relative size of supercooled drops. This paper provides an evaluation of RadIA via comparison to in situ microphysical measurements collected with a research aircraft during the 2017 “Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment” (SNOWIE) field campaign. RadIA uses level-2 dual-polarization radar moments from operational National Weather Service WSR-88D and a numerical weather prediction model temperature profile as inputs. Moment membership functions are defined based on the results of previous studies, and fuzzy logic is used to combine the output of these functions to create a 0 to 1 interest for detecting small-drop, large-drop, and mixed-phase icing. Data from the two-dimensional stereo (2D-S) particle probe on board the University of Wyoming King Air aircraft were categorized as either liquid or solid phase water with a shape classification algorithm and binned by size. RadIA interest values from 17 cases were matched to statistical measures of the solid/liquid particle size distributions (such as maximum particle diameter) and values of LWC from research aircraft flights. Receiver operating characteristic area under the curve (AUC) values for RadIA algorithms were 0.75 for large-drop, 0.73 for small-drop, and 0.83 for mixed-phase cases. RadIA is proven to be a valuable new capability for detecting the presence of in-flight icing hazards from ground-based precipitation radar.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: David Serke, serke@ucar.edu

Abstract

Detection of in-flight icing hazard is a priority of the aviation safety community. The “Radar Icing Algorithm” (RadIA) has been developed to indicate the presence, phase, and relative size of supercooled drops. This paper provides an evaluation of RadIA via comparison to in situ microphysical measurements collected with a research aircraft during the 2017 “Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment” (SNOWIE) field campaign. RadIA uses level-2 dual-polarization radar moments from operational National Weather Service WSR-88D and a numerical weather prediction model temperature profile as inputs. Moment membership functions are defined based on the results of previous studies, and fuzzy logic is used to combine the output of these functions to create a 0 to 1 interest for detecting small-drop, large-drop, and mixed-phase icing. Data from the two-dimensional stereo (2D-S) particle probe on board the University of Wyoming King Air aircraft were categorized as either liquid or solid phase water with a shape classification algorithm and binned by size. RadIA interest values from 17 cases were matched to statistical measures of the solid/liquid particle size distributions (such as maximum particle diameter) and values of LWC from research aircraft flights. Receiver operating characteristic area under the curve (AUC) values for RadIA algorithms were 0.75 for large-drop, 0.73 for small-drop, and 0.83 for mixed-phase cases. RadIA is proven to be a valuable new capability for detecting the presence of in-flight icing hazards from ground-based precipitation radar.

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Corresponding author: David Serke, serke@ucar.edu
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