7.4 Detection of High Ice Water Content (HIWC) Conditions: Status of Nowcasting Tool Development for Avoidance of Ice Crystal Icing Events

Tuesday, 24 January 2017: 4:45 PM
Conference Center: Skagit 1 (Washington State Convention Center )
Julie A. Haggerty, NCAR, Boulder, CO; and A. Rugg, G. P. McCabe Jr., C. Kessinger, J. W. Strapp, R. J. Potts, and R. Palikonda

Ingestion of large amounts of ice particles by jet engines, known as ice crystal icing (ICI), appears to be the culprit in over 150 engine power-loss and damage events during the past two decades. Usually occurring in convective weather conditions at high altitudes, heated probes also appear to be susceptible to this threat. Meteorological and engine performance analyses of such events indicate that high concentrations of ice crystals in these areas present a potential hazard to jet engines. Using information from prior ICI events, a real-time nowcasting tool for detecting high ice water content (HIWC) conditions was developed by the NCAR HIWC Product Development Team with FAA sponsorship. The Algorithm for Prediction of HIWC Areas (ALPHA) applies fuzzy logic methodology to define ranges of interest for a set of critical meteorological predictors of HIWC conditions. Input fields from satellite, model, and radar are then blended to yield a 3-dimensional field estimating the likelihood of HIWC conditions.

Data from recent field campaigns have provided an extensive set of research quality measurements in the conditions typically associated with ICI events.  The High Altitude Ice Crystal – High Ice Water Content (HAIC-HIWC) international field campaigns in Darwin, Australia (2014) and Cayenne, French Guiana (2015) were designed to enhance knowledge of ice crystal icing processes in deep convective clouds. Analyses of aircraft in situ data provide improved guidance on the value of individual meteorological predictors and have resulted in modifications to ALPHA that enhance its skill in detecting HIWC conditions. By empirically relating each current and potential ALPHA input variable to measurements of ice water content at flight level, the information relevance and ranges of interest for each variable have been objectively evaluated. In this way, existing fuzzy logic membership functions have been adjusted and new ones have been created for additional satellite, radar, and numerical weather prediction model fields. The resulting algorithms (ALPHA v2.0) are being evaluated against an independent data set collected during a third NASA-sponsored HIWC field campaign in Florida (2015).  This presentation will summarize the performance characteristics of ALPHA v2.0 and provide a case study where ALPHA was applied to a mesoscale convective system.

* This research is in response to requirements and funding by the Federal Aviation Administration (FAA).  The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA.

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