Differences in situation assessments and prospective diagnoses of simulated weather radar returns amongst experienced pilots

Abstract Weather radar systems are an important tool in commercial aviation to safeguard the safety and security of aircraft. However, the utility of weather radar systems lies in the accuracy and the reliability of the interpretations of the displays. The primary aim of this study was to determine whether experienced pilots could be clustered based on their assessments of the turbulence associated with simulated weather radar displays and whether these groups corresponded to differences in experience-related metrics. Sixty one participants completed a series of on-line scenarios in which they were asked to rate the level of turbulence associated with 11 simulated weather radar displays. They were also asked to indicate their confidence in being able to continue the flight for 80 nautical miles in the absence of an alteration in track or altitude. A cluster analysis reliably differentiated two groups of participants and these groups corresponded to differences in the capacity to discriminate between weather radar scenarios. The results also reveal both a lack of reliability in experienced pilots' interpretations of weather radar displays and difficulties associated with classifications of expertise on the basis of experienced-related metrics. At an empirical level, the outcomes have implications for assessments of expertise in domains in which ideal performance is difficult to establish. From an industry perspective, the results reveal important differences in the interpretation of weather radar displays amongst experienced, qualified pilots. This suggests a need for both more effective weather radar design, complemented by more reliable and comprehensive training that focuses on the accurate interpretation of different types of weather radar returns. Relevance to industry The research highlights the difficulties that pilots face in interpreting weather radar displays accurately and emphasises the need for new designs and more effective training initiatives.

[1]  Kim S. Sankey,et al.  Relationships between young drivers' personality characteristics, risk perceptions, and driving behaviour. , 2008, Accident; analysis and prevention.

[2]  Marissa L. Shuffler,et al.  How Experts Make Decisions: Beyond the JDM Paradigm , 2010, Industrial and Organizational Psychology.

[3]  David J. Weiss,et al.  Performance-based assessment of expertise: How to decide if someone is an expert or not , 2002, Eur. J. Oper. Res..

[4]  Mark W. Wiggins,et al.  The role of cue utilisation and adaptive interface design in the management of skilled performance in operations control , 2014 .

[5]  David R. Hunter,et al.  Risk Perception Among General Aviation Pilots , 2006 .

[6]  David O'Hare,et al.  Expert and Novice Pilot Perceptions of Static In-Flight Images of Weather , 2003 .

[7]  N. Shadbolt,et al.  Eliciting Knowledge from Experts: A Methodological Analysis , 1995 .

[8]  K. Holyoak.  Symbolic connectionism: Toward third-generation theories of expertise. , 1991 .

[9]  Valerie L. Shalin,et al.  Cognitive task analysis , 2000 .

[10]  John R. Anderson,et al.  Rules of the Mind , 1993 .

[11]  J. Shanteau.  Psychological characteristics and strategies of expert decision makers , 1988 .

[12]  Patric Andersson.  Does experience matter in lending? A process-tracing study on experienced loan officers' and novices' decision behavior , 2004 .

[13]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[14]  Scott A. Shappell,et al.  The Influence of Visibility, Cloud Ceiling, Financial Incentive, and Personality Factors on General Aviation Pilots' Willingness to Take Off into Marginal Weather, Part I: The Data and Preliminary Conclusions , 2005 .

[15]  Mark W. Wiggins.  Cue-based processing and human performance , 2006 .

[16]  R. Boakes,et al.  Perceptual and cognitive aspects of wine expertise , 2001 .

[17]  David O'Hare,et al.  Situational and personal characteristics associated with adverse weather encounters by pilots. , 2011, Accident; analysis and prevention.

[18]  David O'Hare,et al.  Expertise in aeronautical weather-related decision making: A cross-sectional analysis of general aviation pilots , 1995 .

[19]  Stephanie M. Doane,et al.  Pilot Ability to Anticipate the Consequences of Flight Actions as a Function of Expertise , 2004, Hum. Factors.

[20]  David O'Hare,et al.  Weatherwise: Evaluation of a Cue-Based Training Approach for the Recognition of Deteriorating Weather Conditions during Flight , 2003, Hum. Factors.

[21]  Gary Klein,et al.  Naturalistic Decision Making , 2008, Hum. Factors.

[22]  D O'Hare,et al.  Cognitive task analyses for decision centred design and training. , 1998, Ergonomics.

[23]  Dennis B. Beringer,et al.  Effects of NEXRAD Graphical Data Resolution and Direct Weather Viewing on Pilot Judgments of Weather Severity and Willingness to Continue Flight , 2003 .

[24]  M Keel Byron,et al.  Aviation Weather Information Requirements Study , 2000 .

[25]  Ben W. Morrison,et al.  Measuring Relative Cue Strength as a Means of Validating an Inventory of Expert Offender Profiling Cues , 2013 .

[26]  John Uhlarik,et al.  A Review of Situation Awareness Literature Relevant to Pilot Surveillance Functions , 2002 .

[27]  David J. Weiss,et al.  Empirical Assessment of Expertise , 2003, Hum. Factors.

[28]  R. Dawes,et al.  Heuristics and Biases: Clinical versus Actuarial Judgment , 2002 .

[29]  Dennis B. Beringer,et al.  Priority and Organization of Information Accessed by Pilots in Various Phases of Flight , 2001 .

[30]  David O'Hare,et al.  'Pressing On' Into Deteriorating Conditions: An Application of Behavioral Decision Theory to Pilot Decision Making , 1995 .