Studies of Cloud Characteristics Related to Jet Engine Ice Crystal Icing Utilizing Infrared Satellite Imagery

Grzych, Matthew; Tritz, Terrance; Mason, Jeanne G.; Bravin, Melissa; Sharpsten, Anna

doi:10.4271/2015-01-2086

Cited by 15 publications

(17 citation statements)

References 8 publications

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“…The high TWC is then advected laterally within the anvil. These results are consistent with previous studies such as Grzych et al (2015) and Bravin et al (2015) who identified that engine icing events often occurred during long traverses through MCS clouds with cold tops, and in particular near to tops penetrating significantly above the surrounding anvil. An alternative HIWC diagnostic method from de Laat et al 2017identifies any ice phase cloud top with moderate or greater COD (≥ 20) in the form of a binary HIWC mask.…”

Section: Discussionsupporting

confidence: 93%

RC2 response

Anonymous

2018

Preprint

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Recent studies have found that ingestion of high mass concentrations of ice particles in regions of deep convective storms, with radar reflectivity considered safe for aircraft penetration, can adversely impact aircraft engine performance. Previous aviation industry studies have used the term high ice water content (HIWC) to define such conditions. Three airborne field campaigns were conducted in 2014 and 2015 to better understand how HIWC is distributed in deep convection, both as a function of altitude and proximity to convective updraft regions, and to facilitate development of new methods for detecting HIWC conditions, in addition to many other research and regulatory goals. This paper describes a prototype method for detecting HIWC conditions using geostationary (GEO) satellite imager data coupled with in situ total water content (TWC) observations collected during the flight campaigns. Three satellite-derived parameters were determined to be most useful for determining HIWC probability: (1) the horizontal proximity of the aircraft to the nearest overshooting convective updraft or textured anvil cloud, (2) tropopause-relative infrared brightness temperature, and (3) daytime-only cloud optical depth. Statistical fits between collocated TWC and GEO satellite parameters were used to determine the membership functions for the fuzzy logic derivation of HIWC probability. The products were demonstrated using data from several campaign flights and validated using a subset of the satellite-aircraft collocation database. The daytime HIWC probability was found to agree quite well with TWC time trends and identified extreme TWC events with high probability. Discrimination of HIWC was more challenging at night with IRonly information. The products show the greatest capability for discriminating TWC ≥ 0.5 g m −3. Product validation remains challenging due to vertical TWC uncertainties and the typically coarse spatio-temporal resolution of the GEO data.

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Section: Discussionsupporting

confidence: 93%

RC2 response

Anonymous

2018

Preprint

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“…The high TWC is then advected laterally within the anvil. These results are consistent with previous studies such as Grzych et al (2015) and Bravin et al (2015) who identified that engine icing events often occurred during long traverses through MCS clouds with cold tops, and in particular near to tops penetrating significantly above the surrounding anvil. would have offered a 100% POD and 96% FAR using the daytime satellite-aircraft match database from the three campaigns, indicating that this product is more useful for telling users 15…”

Section: Discussionsupporting

confidence: 83%

A Prototype Method for Diagnosing High Ice Water Content Probability Using Satellite Imager Data

Yost

2017

Preprint

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Abstract. Recent studies have found that flight through deep convective storms and ingestion of high mass concentrations of ice crystals, also known as high ice water content (HIWC), into aircraft engines can adversely impact aircraft engine performance. These aircraft engine icing events caused by HIWC have been documented during flight in weak reflectivity regions near 20 convective updraft regions that do not appear threatening in onboard weather radar data. Three airborne field campaigns were conducted in 2014 and 2015 to better understand how HIWC is distributed in deep convection, both as a function of altitude and proximity to convective updraft regions, and to facilitate development of new methods for detecting HIWC conditions, in addition to many other research and regulatory goals. This paper describes a prototype method 25 for detecting HIWC conditions using geostationary (GEO) satellite imager data coupled with insitu total water content (TWC) observations collected during the flight campaigns. Three satellite-derived parameters were determined to be most useful for determining HIWC probability: 1) the horizontal proximity of the aircraft to the nearest overshooting convective updraft or textured anvil cloud, 2) tropopause-relative infrared brightness temperature, and 3) 30 daytime-only cloud optical depth. Statistical fits between collocated TWC and GEO satellite 2 parameters were used to determine the membership functions for the fuzzy logic derivation of HIWC probability. The products were demonstrated using data from several campaign flights and validated using a subset of the satellite-aircraft collocation database. The daytime HIWC probability was found to agree quite well with TWC time trends and identified extreme TWC events with high probability. Discrimination of HIWC was more challenging at night with IR-5 only information. The products show the greatest capability for discriminating TWC ≥ 0.5 g m -3 .Product validation remains challenging due to vertical TWC uncertainties and the typically coarse spatio-temporal resolution of the GEO data.

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“…Since the mid-1990s, observations of jet-engine power loss in areas with high ice particle concentrations have generated interest from the aviation industry, regulatory agencies, and the meteorological research community. Over 200 ice crystal icing (ICI) events causing power loss or engine rollback (loss of engine control) in commuter and large transport aircraft have been documented as of 2019 (Lawson et al 1998;Bravin et al 2015;Bravin and Strapp 2019). Air data system performance disruptions, such as anomalies in total air temperature measurements, have also been noted in these conditions (Lawson et al 1998;Rogers et al 2002;Mason et al 2006;Beswick et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Analyses of in-service ICI events (Mason et al 2006;Grzych and Mason 2010;Bravin et al 2015;Bravin and Strapp 2019) indicate that potentially hazardous HIWC conditions most often occur near convective systems in warm air masses at high altitudes. The majority of events analyzed occurred outside of vigorous updraft regions.…”

Section: Introductionmentioning

confidence: 99%

Development of a Method to Detect High Ice Water Content Environments Using Machine Learning

Haggerty

Rugg

Potts

et al. 2020

Journal of Atmospheric and Oceanic Technology

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This paper describes development of a method for discriminating high ice water content (HIWC) conditions that can disrupt jet-engine performance in commuter and large transport aircraft. Using input data from satellites, numerical weather prediction models, and ground-based radar, this effort employs machine learning to determine optimal combinations of available information using fuzzy logic. Airborne in situ measurements of ice water content (IWC) from a series of field experiments that sampled HIWC conditions serve as training data in the machine-learning process. The resulting method, known as the Algorithm for Prediction of HIWC Areas (ALPHA), estimates the likelihood of HIWC conditions over a three-dimensional domain. Performance statistics calculated from an independent subset of data reserved for verification indicate that the ALPHA has skill for detecting HIWC conditions, albeit with significant false alarm rates. Probability of detection (POD), probability of false detection (POFD), and false alarm ratio (FAR) are 86%, 29% (60% when IWC below 0.1 g m−3 are omitted), and 51%, respectively, for one set of detection thresholds using in situ measurements. Corresponding receiver operating characteristic (ROC) curves give an area under the curve of 0.85 when considering all data and 0.69 for only points with IWC of at least 0.1 g m−3. Monte Carlo simulations suggest that aircraft sampling biases resulted in a positive POD bias and the actual probability of detection is between 78.5% and 83.1% (95% confidence interval). Analysis of individual case studies shows that the ALPHA output product generally tracks variation in the measured IWC.

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Studies of Cloud Characteristics Related to Jet Engine Ice Crystal Icing Utilizing Infrared Satellite Imagery

Cited by 15 publications

References 8 publications

RC2 response

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A Prototype Method for Diagnosing High Ice Water Content Probability Using Satellite Imager Data

Development of a Method to Detect High Ice Water Content Environments Using Machine Learning

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