Assessing General Aviation Pilots\u27 Weather Knowledge and Self-Efficacy

While accident trends in General Aviation (GA) have decreased overall, accidents rates involving weather have remained relatively consistent over the past 10 years. An assessment was developed and validated to assess if GA pilots lack adequate knowledge of aviation weather concepts. The assessment consisted of a 95 question Aviation Weather Knowledge multiple-choice test covering weather phenomena, aviation weather products, and aviation weather product sources. 204 GA pilots completed the knowledge questions along with an aviation weather self-efficacy (confidence) survey. Results indicated that while instrument rated commercial pilots demonstrated the highest levels of knowledge, their scores were only moderate – around 65% correct. Private pilots had scores in the 60% range. These results may indicate that pilots flying in GA operations have a relatively low level of aviation weather knowledge. Weather self-efficacy was correlated positively with aviation weather knowledge

Evaluating GA Pilots\u27 Interpretation of New Automated Weather Products

Introduction: Over the past 30 years, General Aviation (GA) operations have incurred the majority of weather related accidents in civil aviation operations. Aviation weather knowledge and skills are imperative for hazardous weather avoidance and safe flight activity. Previous research suggests applying human-computer interaction (HCI) principles to weather products may promote better decision-making among pilots. Currently, the Aviation Weather Center (AWC) generates two forms of graphical weather products for reporting icing, turbulence, and visibility: traditional human-in-the-loop products (G-AIRMETs Ice, Tango, and Sierra) and the fully-automated products (CIP/FIP, GTG, and CVA). This study compares pilots’ interpretation of fully-automated products against their interpretation of human-in-loop products. Method: Participants (n=131) completed a series of weather product interpretation questions. Mixed ANOVAs were conducted to analyze the effects of pilot certificate and/or rating (Student, Private, Private w/Instrument, Commercial w/ Instrument) and product generation (traditional vs. automated) on product interpretation scores. Results: Regardless of product generation, pilots displayed similar levels of proficiency when interpreting the icing and ceiling/visibility products. However, pilots’ performed significantly better on the new fully automated turbulence product (GTG) than on the traditional human-in-the-loop turbulence product (AIRMET Tango). Discussion: Producing more user-friendly weather products may make weather product interpretation easier for novice pilots