20 Super-Cooled Large Drop Detection with Precipitation Radars for the Enhancement of Operational Icing Products

Monday, 28 August 2017
Zurich DEFG (Swissotel Chicago)
David J. Serke, NCAR, Boulder, CO; and D. R. Adriaansen, S. Tessendorf, J. A. Haggerty, D. Albo, and G. Cunning

The National Center for Atmospheric Research developed an in-flight icing detection algorithm, called the 'Current Icing Product' (CIP). This operationally available product ingests numerical weather prediction model, lightning, surface meteorological observations, pilot reports, weather satellite, and two-dimensional 'max-in-column' reflectivity values from weather radars to provide volumetric icing hazard classifications over the Continental US. The outputs from CIP include 'icing probability', 'icing severity' and 'supercooled large-drop potential' (SLD).

An algorithm called the 'Radar Icing Algorithm' (RadIA) has been developed to detect the presence, phase components and relative size of supercooled drops. RadIA interest fields for small-drop, large-drop , mixed-phase and plate-shaped crystals are computed in realtime mode in the native polar coordinate system inherent to the WSR-88d precipitation radars. In order to bring these radar-based interest fields into CIP, these fields are then downsampled and gridded to the same native WRF model 13 km horizontal spacing that is used by CIP.

During the recent 'Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment' (SNOWIE), two instrumented research aircraft each encountered significant supercooled liquid water, many cases which included a large drops of mean diameter greater than 100 um. During this study, the focus will be on one case with a mean supercooled drop diameter of 400 um that was sampled over the terrain north of Boise, Idaho. In this case which consisted of a wide range of supercooled drop diameters with an embedded large drop mode, the high RadIA interest in large and small drop icing was used to modify the 'icing probability', 'icing severity', and 'SLD potential produced by CIP. The presence of any large or small drop icing indicators from RadIA should serve to boost the CIP 'icing probability' to the maximum possible value. This is very similar to how ground observations of freezing rain and drizzle affect CIP, which cause the SLD potential to be maximized since these observations are direct indicators of the presence of SLD conditions. The 'icing severity' from CIP was affected by combining both the large and small drop icing indicators from RadIA to affect the severity based on how much interest was contained within each category (i.e. more large drop or more small drop interest). Finally, the 'SLD potential' from CIP was maximized if the large drop interest from RadIA met a pre-defined threshold.

The inclusion of RadIA's interest fields in the developmental version of CIP will be the first time that a three-dimensional, in-cloud, observation-based dataset will be available. The value added by the inclusion of these radar-based fields could make a significant impact on the certainty we have in the presence or absence of small (FAA 'Appendix C') and large-drop (FAA 'Appendix O') icing in the volumetric airspace sampled by the national network of weather radars. 

Although the FAA has sponsored this project, it neither endorses nor rejects the findings of the research. The presentation of this information is in the interest of invoking technical community comment on the results and conclusions of the research.

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