Purpose   Use the questions we developed in Phase I  Include pilots that are more representative of GA (age, flight hours/experience); Collaborate with AOPA  Examine: Knowledge about aviation weather products; Differences between levels of flight certificate and/or rating

Berendschot, Quirijn

Blickensderfer, Beth

Bob, Thomas

DeFilippis, Nick

Guinn, Thomas A

King, Jayde

McSorley, Jacqueline

Ortiz, Yolanda

English

Embry-Riddle Aeronautical University

General Aviation Weather Display Interpretation General Aviation Weather 10-17-2018 Interpreting Aviation Weather Products: Follow-up study with AOPA Members Beth Blickensderfer Embry-Riddle Aeronautical University, blick488@erau.edu Thomas A. Guinn Embry-Riddle Aeronautical University, guinnt@erau.edu Thomas Bob Embry-Riddle Aeronautical University Jayde King Embry-Riddle Aeronautical University Yolanda Ortiz Embry-Riddle Aeronautical University See next page for additional authors Follow this and additional works at: https://commons.erau.edu/ga-wx-display-interpretation  Part of the Aviation Commons, Cognitive Psychology Commons, Human Factors Psychology Commons, and the Meteorology Commons Scholarly Commons Citation Blickensderfer, B., Guinn, T. A., Bob, T., King, J., Ortiz, Y., DeFilippis, N., Berendschot, Q., & McSorley, J. (2018). Interpreting Aviation Weather Products: Follow-up study with AOPA Members. , (). Retrieved from https://commons.erau.edu/ga-wx-display-interpretation/1 This Article is brought to you for free and open access by the General Aviation Weather at Scholarly Commons. It has been accepted for inclusion in General Aviation Weather Display Interpretation by an authorized administrator of Scholarly Commons. For more information, please contact commons@erau.edu. Author / Researcher / PI Beth Blickensderfer, Thomas A. Guinn, Thomas Bob, Jayde King, Yolanda Ortiz, Nick DeFilippis, Quirijn Berendschot, and Jacqueline McSorley This article is available at Scholarly Commons: https://commons.erau.edu/ga-wx-display-interpretation/1 Faculty Researchers:Beth Blickensderfer, Ph.D.Tom Guinn, Ph.D.Bob Thomas, M.S., Ph.D. Interpreting Aviation Weather Products:  Follow-up study with AOPA Members1Graduate Research Assistants: Jayde King, M.S.Yolanda Ortiz, M.S.Nick DeFilippis, M.S.Quirijn Berendschot, M.S.A.Jacqueline McSorley, M.S. October  17, 2018Outline Background Method Results DiscussionAcknowledgements:Funding for this project was provided by the FAA. Thank you to the Aircraft Owners and Pilots Association for their contribution to this study.• Purpose ▫ Use the questions we developed in Phase I  ▫ Include pilots that are more representative of GA (age, flight hours/experience); Collaborate with AOPA ▫ Examine:  Knowledge about aviation weather products;  Differences between levels of flight certificate and/or ratingsAviation Weather Product Interpretation Research  “The older generation” 1. Coordinated with Rune Duke2. 118 questions divided into 5 Tests/Surveys; 3. Study protocol approved by ERAU IRB4. Implemented the 5 separate online surveys/tests (Qualtrics)5. AOPA sent out the survey 3 times (June 2017, August 2017, September 2017) Study Design 118 Questions Divided into 5 TestsTest 1• Data Source (5)• Flight Planning (5)• Storm Definition (5)• Significant Weather (5)Test 2 • Metar (8)• TAF (6)• Winds Aloft (5)• Pirep (6)Test 3• G-Airmet (13)• GTG (5)• CIP (5)Test 4 • Radar (12)• Sigmet (7)• TSTM (5)Test 5• Satellite (7)• Station Plots (6)• Surface Prog (5)• CVA (5)• More than 1000 pilots began the survey • 837 pilots completed the whole survey and were included in analysis▫ Private pilot (Private)▫ Private pilot with instrument rating (Private with Instrument)▫ CPL with instrument  (Commercial with Instrument)▫ CFI or CFII or anyone with additional certificates (CFI)▫ ATP (ATP)Participants  Participant ageM(SD) =  57 (13.8)Sample SizeTest 1 Test 2 Test 3 Test 4 Test 5 Totaln n n n n NPrivate 69 35 40 55 49 248Private w/ Instrument 41 47 55 46 51 240Commercial w/ Instrument 39 22 11 29 33 134ATP 22 24 24 7 23 100CFI 35 21 19 22 18 115Total 206 149 149 159 174 837LocationsParticipant Mean Flight HoursThere was a significant main effect for rating on flight hours, F (4,850) = 196.99, p < 0.01, partial eta squared = 0.48Overall Flight Hours by TestNo significant interaction between test number and pilot rating on flight hours, F (16, 850) = 1.07, p = 0.38, partial eta squared = 0.02ResultsA 5x5 Between Groups ANOVA Independent Variable 1:  Test(Test 1 vs. Test 2 vs. Test 3 vs.  Test 4 vs. Test 5)Independent Variable 2:  Pilot Rating (Private vs. Private w/Inst vs. Commercial w/Inst vs. CFI vs. ATP)Dependent variable:  Percent Correct (Score)Overall AnalysisEffect of Rating on ScoreThere was a significant main effect of pilot certificate/rating on score,F (4, 857) = 12.48, p < 0.01, partial eta squared = 0.55.Effect of Test on ScoreThere was a significant main effect of test on scoreF (4, 857) = 53.39, p < 0.01 partial eta squared = 0.20.Data sources, Significant Weather, Storm Definitions, Flight PlanningMETAR, PIREP, TAF, Winds AloftCIP, GAirmet , GTGRadar, SIGMET, ThunderstormCVA, Satellite, Station Plot,  Surface PrognosticInteraction graphThe interaction was not significant, F (16, 857) = 1.11, p = 0.338, partial eta squared = 0.02.A 4x5 Mixed ANOVA Independent Variable 1:  Topics within Test 1 (Data sources vs. Significant Weather vs. Storm Definitions vs. Flight Planning)Independent Variable 2:  Pilot Rating (Private vs. Private w/Inst  vs. Commercial w/Inst vs.  CFI vs.  ATP)Dependent variable:  Percent Correct (Score)Test 1 Analysis Test 1Significant main effect of  Product type on Test 1 score, Wilks’ Lambda = 0.46, F (3, 202) = 78.29, p > 0.01. Partial eta squared = 0.54.Significant main effect of Pilot Rating on Test 1 score, F (4, 204) = 3.03, p = 0.02, partial eta squared = 0.06.Test 1No significant interaction of Pilot Rating and Topic on ScoreWilks’ Lambda = 0.90, F (12, 534.7) = 1.76, p = 0.053, partial eta squared = 0.03A 4x5 Mixed ANOVA Independent Variable 1:  Topics within Test 2(METAR vs. PIREP vs. TAF vs. Winds Aloft)Independent Variable 2:  Pilot Rating (Private vs. Private w/Inst  vs. Commercial w/Inst vs.  CFI vs.  ATP)Dependent variable:  Percent Correct (Score)Test 2 AnalysisTest 2Significant main effect of product on Test 2 Score, Wilks’ Lambda = .30, F (3, 142) = 110.63, p < 0.01, partial eta squared = 0.70Significant main effect for Pilot Rating on Test 2 score, F (4, 144) = 4.67, p = 0.01, partial eta squared = 0.12Test 2No significant interaction for Product and Pilot Rating/Certificate on Test 2 score, Wilks’ Lambda = .91, F (12, 375.99) = 1.16, p = 0.313, partial eta squared = 0.03.A 3x5 Mixed ANOVA Independent Variable 1:  Topics within Test 3 (CIP vs. GAirmet vs. GTG)Independent Variable 2:  Pilot Rating (Private vs. Private w/Inst  vs. Commercial w/Inst vs.  CFI vs.  ATP)Dependent variable:  Percent Correct (Score)Test 3 AnalysisTest 3Significant main effect found of Product on Test 3 score,Wilks’ Lambda = 0.44, F (2, 144) = 90.8, p < 0.01, partial eta squared .56.No significant main effect of Pilot Rating on Test 3 score, F (4, 145) = 2.25, p = 0.59, partial eta squared = 0.06Test 3No significant interaction of  Product and Pilot Certificate/ Rating on Test 3 score, Wilks’ Lambda = 0.94, F (8, 288) = 1.09,  p = .37, partial eta squared = 0.03A 3x5 Mixed ANOVA Independent Variable 1:  Topics within Test 4 (Radar vs. SIGMET vs. Thunderstorm)Independent Variable 2:  Pilot Rating (Private vs. Private w/Inst  vs. Commercial w/Inst vs.  CFI vs.  ATP)Dependent variable:  Percent Correct (Score)Test 4 AnalysisTest 4A significant main effect also occurred for Pilot Certificate/Rating on score, F (4, 193) = 6.16, p < 0.01, partial eta squared = 0.11.There was a significant effect for product on score, Wilks’ Lambda = 0.54, F (2, 192) = 67.69, p < 0.01, partial eta squared = 0.46.Test 4There was no significant interaction found between Product and Pilot Certificate/Rating, Wilks’ Lambda = 0.95, F (8, 384) = 1.17, p = 0.32, partial eta squared = 0.02A 4x5 Mixed ANOVA Independent Variable 1:  Topics within Test 5(CVA vs. Satellite vs. Station Plot vs. Surface Prognostic)Independent Variable 2:  Pilot Rating (Private vs. Private w/Inst  vs. Commercial w/Inst vs.  CFI vs.  ATP)Dependent variable:  Percent Correct (Score)Test 5 AnalysisTest 5There was a significant main effect for product on score, Wilks’ Lambda = 0.37, F (3, 169) = 96.74, p < 0.01, partial eta squared = 0.63.There was no significant main effect of Pilot Certificate/Rating on score, F (4, 171) = 0.21, p = 0.16, partial eta squared = 0.04.Test 5There was no significant interaction between Pilot Certificate/ Rating and Product on Score, Wilks’ Lambda= 0.93, F (12, 447.4) = .996, p = 0.45, partial eta squared = 0.02.  A major contributing factor in the weather accidents may be GA Pilots’ inability to interpret weather displays. GA Pilots of ALL ratings and certificates are struggling on some products Radar, Satellite, Station Plots   Good news: Better scores on GTG  Further research is needed to understand why these gaps exist and how to fix them. Display design? Training? Key TakeawaysQuestions? 

Interpreting Aviation Weather Products: Follow-up study with AOPA Members

https://commons.erau.edu/cgi/viewcontent.cgi?article=1003&amp;context=ga-wx-display-interpretation

Interpreting Aviation Weather Products: Follow-up study with AOPA Members

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