Use of Weather Information by General 

Aviation Pilots, Part II, Qualitative: 

Exploring Factors Involved in WeatherRelated Decision Making

William R. Knecht

Civil Aerospace Medical Institute

Oklahoma City, OK 73125

March 2008

Final Report

DOT/FAA/AM-08/7

Office of Aerospace Medicine

Washington, DC 20591

OK-08-1997
NOTICE

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of the U.S. Department of Transportation in the interest 

of information exchange. The United States Government 

assumes no liability for the contents thereof.

___________

This publication and all Office of Aerospace Medicine 

technical reports are available in full-text from the Civil 

Aerospace Medical Institute’s publications Web site: 

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Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-08/7 

4. Title and Subtitle 5. Report Date 

Use of Weather Information by General Aviation Pilots, Part II, March 2008 

Qualitative: Exploring Factors Involved in Weather-Related Decision 

Making

6. Performing Organization Code 

 

7. Author(s) 8. Performing Organization Report No. 

Knecht WR 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

FAA Civil Aerospace Medical Institute 

P.O. Box 25082 11. Contract or Grant No. 

Oklahoma City, OK 73125 

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

Work was accomplished under approved task AM-A-02-HRR-521 

16. Abstract 

Interview data obtained from 221 general aviation (GA) pilots are qualitatively scored for factors which influence 

weather-related decision making. Factors finding relatively strong support are (a) the specific type of weather to 

be faced (storms, ice, visibility, and cloud ceiling are of greatest concern to GA pilots), (b) type of flight (IFR vs. 

VFR), (c) pilot physiological state (primarily disorientation), and (d) the inherent uncertainty of weather and the 

resultant cognitive difficulty of understanding this uncertainty. Factors finding more modest support are (a) 

social and/or economic pressures, and (b) impulsive behavior. Additionally, relatively strong support is found in 

previously unpublished data for the influence of mission goals. Research directions, remediations, and the value 

of qualitative analysis are discussed. 

 

17. Key Words 18. Distribution Statement 

 

 

General Aviation, Weather, Risk Factors Document is available to the public through the 

Defense Technical Information Center, Ft. Belvior, VA 

22060; and the National Technical Information 

Service, Springfield, VA 22161 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 22 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 

iii

ACKNOWLEDGMENTS

The author gratefully acknowledges the efforts of Tom Chidester and Mike Wayda for editing, 

Dennis Beringer and Jerry Ball for data collection, of the various site points-of-contact who made 

that data collection possible, and of all the pilots, without whose participation this study would 

not have come to fruition.

Contents

INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Background and purpose. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

METHOD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Participants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Methodological issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

RESULTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Deductive (top-down, theory-driven) themes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Inductive (bottom-up, emergent) themes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Sponsor-driven questions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

DISCUSSION AND CONCLUSIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Endnotes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

REFERENCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

APPENDIX A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

APPENDIX B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

Use of Weather Information by General Aviation Pilots, 

Part II, Qualitative: Exploring Factors Involved in

Weather-Related Decision Making

INTRODUCTION

Background and purpose

While the causes of aviation accidents are varied and 

many, adverse weather remains a major, elusive cause of 

general aviation (GA) fatalities. Weather was cited as causal 

in only 4% of GA accidents but it accounted for 12 to 

17% of fatalities, since about 70% of weather-induced 

accidents prove fatal (AOPA, 2005). 

The U.S. Federal Aviation Administration has a stated 

current goal of reducing GA fatalities (FAA, 2006). To 

this end, weather accidents are targeted for reduction. 

This calls for psychological research. Yet, the more 

we learn, the more we appreciate how delicately the 

complexity of weather interacts with the intricacy of 

the human mind and the pilot’s unique task-world. The 

problem is not simple.

This report is the second in a two-part series, Part I 

being Use of weather information by general aviation pilots, 

Part I, quantitative: Reported use and value of providers 

and products (Knecht, 2008). In Part I, we examined 

quantifiable aspects of how an interviewed sample of 

221 GA pilots actually used the weather information 

available to them.

Here in Part II, the remaining interview data are examined for themes—factors seeming to influence pilot 

weather-related decision making. The mission is to look 

beyond quantitative analysis to explore both known and 

novel weather-related ideas worthy of further investigation 

or actual intervention.

METHOD

Design

The original data collection involved onsite interviews 

with GA pilots. The interview instrument itself is shown 

in Appendix A. In addition to its quantitative research 

items, this instrument contained qualitative items designed to elicit free responses. Free-responses are often 

useful in getting respondents to think beyond the rigid 

limits imposed by checkbox items.

Participants

During July and August, 2005, FAA staff conducted 

on-site interviews with 230 GA pilots at locations across 5 

states (CA, OK, ND, IL, FL). Four venues were universitybased flight schools; the fifth was a helicopter training 

course. Of these 230 pilots, 221 ultimately provided usable data. Median pilot age was 23 years; median flight 

experience was 245 hours. Women comprised 14% of 

the sample. All were volunteers paid for their services as 

subject matter experts.

Procedure 

Like the Part I study, all responses were referenced 

to a common benchmark—a “standard flight,” defined 

as a 4-hour flight through “weather serious enough to 

challenge your skill level and the aircraft’s capabilities.” 

This benchmark was crucial to the study. Without it, 

pilots would have substituted their own private definitions of “bad weather,” making it impossible to relate 

their attitudes and behavior to any common real-world 

standard.

Pilots were asked open-ended questions and Likertscale items designed to capture their thought processes 

while dealing with adverse weather. The open-ended 

responses were then scored according to a coding scheme 

(rubric) laid out in Appendix B. 

Rubrics are central to qualitative analysis. A rubric is 

a scoring methodology, a systematic way of defining a 

set of key factors well enough to let a rater recognize and 

tally specific instances within some given text, speech, or 

behavior (Miles & Huberman, 1994). Multiple instances 

of a single factor then constitute a “theme.” A theme can 

be unique to an individual, but we are usually interested 

in themes involving more than one person. Naturally, 

the more respondents who mention a theme, the more 

universal we consider it.

Rubrics typically take time to develop. They evolve 

gradually, within the context of specific data, using a process 

of deduction and induction applied iteratively. Deduction 

is top-down, logical reasoning starting with general principles and then looking for specific instances in specific 

data. Induction is the reverse, the bottom-up formation 

of general conclusions after looking at specific data.
Consequently, three influences shaped the evolution 

of our rubric (a) deductive, (b) inductive, and (c) sponsordriven. Cognitive and behavioral psychological theory 

guided the search for top-down, deductive evidence of 

certain themes in the data.Conversely, reviewing specific 

pilot responses induced other themes from the bottomup. Finally, project sponsors had specific questions about 

mission-characteristic effects. The rubric was amended 

to assess those questions.

Methodological issues

Issues of reliability and validity take on a markedly 

different role in qualitative analysis than they do in quantitative analysis. First, the concept of “themes” lends itself 

less to discreteness and definability and more to broadness 

and generality. Consequently, we typically recalibrate our 

priorities regarding experimental error when we go out 

“fishing for factors” this way. 

Second, in quantitative analysis the focus is almost 

always on controlling Type I error (the misidentification 

of factors as “significant” which are truly not). Whereas, 

in qualitative analysis the focus is frequently on controlling Type II error (the failure to identify as significant 

those factors which truly are). This difference in focus 

exists because quantitative analysis is about hypothesistesting, whereas qualitative analysis is more often about 

hypothesis-generation.

What we have to understand is the relation between 

Type I and Type II errors. “Truth” can be thought of as a 

“signal,” and signal detection theory says that whenever 

we choose to capture more signal (increase the hit rate, 

minimize Type II errors), this inevitably comes at the 

expense of increased false alarms, namely increased Type 

I error (Green & Swets, 1966). A finer-mesh net catches 

more fish—but also more flotsam.

The point is that quantitative and qualitative analysis 

are really two methodologies designed to do different 

things. Where quantitative analysis tests hypotheses, qualitative analysis can generate new hypotheses. Researchers 

must simply make clear when they are engaged in each 

type of analysis and properly limit the interpretation of 

subsequent results.

RESULTS

Since the rubric was iteratively shaped by three influences (deductive, inductive, sponsor-driven), the results 

are presented under similar headings.

Deductive (top-down, theory-driven) themes

Theme 1: Type of weather. Psychologically, weather is 

a set of specific stimuli. So, certainly, different types of 

weather must affect pilot decisions differently. Change 

the weather, change the decision. 

Following a deductive approach requires first listing 

the main weather factors pilots could discuss. Next, using 

their free-response answers (Appendix A), the factors they 

actually did discuss could be scored and tabulated.

An endnote [page 9] clarifies the complete methodology and exactly how to read Table 1.1

 Briefly, each cell 

represents the percentage of pilots who discussed that 

factor at least once in their interview. If a given pilot 

mentioned a given factor more than once, it still counted 

only once. Note that neither rows nor columns need 

Table 1. Percentages of the 221 pilots who referred at least once to each weather factor in their 

interview. Data to the left refer to preflight. Data to the right refer to in-flight. Row "IR" data came 

from instrument rated pilots, row "Non-IR" from non-IR pilots. Row "Total" combines both IR and 

non-IR responses. "Storms" mainly means thunderstorms, but also tornadoes + hurricanes. “Wind” 

includes turbulence + wind shear. Significant results are bold+highlighted (pz < .05 (uncorrected for 

number of comparisons), 1-tailed for all but "(Non-IR-IR)" scores, which reflect pz < .02, 2-tailed, 

corrected for number of comparisons). See Endnote for details. Storm, (incl.T-storm, tornado, hurricane) Rain Snow Ice Hail Cloud ceiling Visibility Wind/Turbulence Storm, (incl.T-storm, tornado, hurricane) 

RainSnow IceHailCloud ceiling VisibilityWind/Turbulence 

Preflight In-flight

Total 83 5 7 48 5 46 57 52 81 5 4 42 5 41 49 38

Non-IR 78 10 6 25 3 51 64 63 66 9 3 22 4 48 66 37

IR 86 3 7 57 6 44 54 47 87 3 4 50 5 38 42 38

(Non-IR)-IR -32 -21 -28 24
total to 100%. Factors with percentages significantly 

higher than average (p < .05) are shown boldfaced and 

highlighted in gray. Statistical issues are elaborated in a 

second endnote.2

The row labeled “Total” represents instrument-rated 

and non-instrument-rated pilots combined. Higherthan-average combined-group preflight factors mentioned 

by pilots were 

storms 83%

ice 48%

cloud ceiling 46%

visibility 57% 

wind 52%

In the same row, farther to the right, above-average 

combined-group in-flight factors mentioned were 

storms 81%

ice 42%

cloud ceiling 41%

visibility 49%

This gave us a rough idea of what factors seemed to 

mentally dominate the average pilot over the entire length 

of a flight, namely storms, ice, ceiling, and visibility.

A brief note about snow: Since the interviews were 

conducted in the summer, it is perfectly natural to wonder 

if the relatively low numbers for snow were merely due to 

the current air temperature. If we had interviewed during 

the winter, would snow have received higher priority? The 

honest answer is that we simply do not know. There is 

a modest validity crosscheck in the fact that pilots did 

emphasize icing. However, icing can occur nearly yearround, especially carburetor icing (and the pilots rarely 

distinguished between icing types in their responses). So 

the question remains debatable, and the only valid (but, 

unfortunately, cost-prohibitive) way to settle it would be 

to reconduct the study during the winter.

Theme 2:IR versus non-IR flying. Because instrumentrated (IR) pilots are trained to fly in a different physical 

environment from non-IR pilots, it made sense to split 

Table 1 in half on this basis. The two additional rows 

labeled “Non-IR” and “IR” also use boldface and gray to 

highlight significantly above-average cells (p < .05).

The patterns that emerged for the two groups looked 

fairly consistent and jibed with practical experience. 

Storms, ice, ceiling, and visibility are logically both 

common and potentially dangerous to all pilots at all 

phases of flight.

So how did IR pilots’ flying differ from non-IR? The 

“(Non-IR) – IR” row shows percentage differences between 

•

•

•

•

•

•

•

•

•

the two groups.3 Significant differences (greater than 20 

points, p < .02) emerged for

Preflight ice -32%

In-flight ice -28%

In-flight storms -21%

In-flight visibility +24%

Negative numbers (less than zero) meant the factor was 

bigger to IR pilots. Numbers greater than zero meant the 

factor was bigger to non-IR pilots.

Again, this jibed with experience. Non-IR pilots must 

avoid in-flight restrictions to visibility, while this is much 

less an issue with IR pilots.

Stepping back to get the big picture, it was easy to see 

that instrument flight rules (IFR) flight has a lot more 

built-in ambiguities than visual flight rules (VFR) flight. 

First, IR pilots should (and did) talk more about preflight 

and in-flight ice because ice would not automatically preclude flying, whereas it usually would for non-IR pilots. 

Second, in-flight storms should (and did) elicit more 

responses from IR pilots because they would certainly 

be more likely to fly near (and, sometimes, into) weather 

capable of storm intensity.

Third, wind and turbulence loomed larger for everyone 

during preflight than in-flight. That might seem counter-

intuitive. But it may simply have been a technicality. The 

term “in-flight” does not technically include the landing 

phase, whereas preflight planning always includes all phases 

of flight, including landing, when wind and turbulence 

are most likely to be hazardous.

To summarize, in some ways the environmental context, 

training, and equipment used in instrument meteorological 

conditions (IMC) is quite different from visual meteorological conditions (VMC). The basic hazards are the same but 

vary mainly in intensity and frequency of encounter.

Theme 3: Social and economic pressure. Pilots often cite 

social and economic pressures as reasons for taking chances 

they would not otherwise take. For example, Rhoda and 

Pawlak (1999) found that commercial airline pilots were 

more likely to penetrate storms in terminal airspace when 

following another aircraft. In one sense, proximal pilots 

form a “society” where one member influences the risktaking of others.

Here, at least 48 pilots reported external social or business-related pressures to fly in marginal weather. So, in an 

informal sense, pressure certainly was a theme. 

Social psychological theory elaborates this idea of “pressures” into better-defined and more measurable processes 

such as social facilitation, diffusion of responsibility, social 

assessment of risk, and obedience to authority. So did these 

pilots report any influence of these processes?

•

•

•

•
Honestly, there was little direct evidence here for 

diffusion of responsibility and social assessment of risk. 

While we cannot rule them out, we cannot find support, 

either. However, the reports of “pressure” were possibly 

consistent with social facilitation and/or obedience to 

authority.

Social facilitation describes the effect of an audience on 

task performance (Zajonc, 1965). Despite its name, social 

facilitation is not facile. For one thing, “facilitation” can 

be positive or negative. For another, outcomes depend on 

situational factors, for instance, task difficulty (Figure 1). 

For example, experienced runners tend to run faster in a 

group than when alone (the “Simple/Yes” cell). However, 

complex math problems are easier to solve when we are 

by ourselves (the “Hard/No” cell).

Does this theory apply to weather-related risk-taking? 

Recall that these pilots were predominately either students 

or flight instructors. If students said they felt pressure 

to expedite training, that pressure might be caused by 

social facilitation.

However, one fact argues against the social facilitation 

hypothesis and that is the theory itself. Weather flying is a 

difficult task. Therefore, as Figure 1 shows, the presence of 

the instructor in the cockpit should have inhibited student 

willingness to fly in bad weather, not facilitated it.

An alternate, simpler economic explanation might be 

more plausible, namely that flight training is expensive 

and students wanted to get through it as fast as possible. 

After all, many flight schools charge by the hour. Plus, 

time spent training is time away from earning money, so 

school carries a double incentive to finish fast.

Now what about instructors? Instructors reported occasional pressure from both their employers and students 

to fly in marginal weather. While this might be social 

facilitation, the more straightforward answer would again 

be economic. Students probably exert pressure because 

they are trying to finish fast. Employers probably exert 

pressure, partly in response to student pressure and partly 

because the faster a school moves students along, the more 

money it can make. 

Finally, there was one more alternate social hypothesis 

to consider here, namely obedience to authority. In his 

classic experiment, Milgram (2004/1974) demonstrated 

that otherwise-normal people would administer supposedly lethal electrical shocks to strangers on command from 

an authority figure. If some people would do anything 

that extreme, might they possibly also dally a bit with 

bad weather, if pressured into it?

Now, an employer is definitely an authority figure. But, 

as any teacher will confirm, so is the modern student. 

This reflects the increasing tendency for schools to follow 

a business model where teachers have to treat students as 

customers (Armstrong, 2003). And, as we all know, the 

customer is always right.

The presence of passengers is another common social 

situation that could arguably influence risk-taking. However, the influence could conceivably go both ways. On 

the one hand, passengers can be a source of pressure to 

start or continue a flight into bad weather. On the other 

hand, since risk depends on what we stand to lose, the 

more lives that stand to be lost (and the closer they are 

to us) the greater the risk should be. 

Table 2 was based on two interview questions, “Does 

having non-family (Q36, or) family (Q37) passengers 

affect your willingness to fly in bad weather?”

Here, most pilots basically said “no.” While the reported 

concern for family passengers was significantly greater 

(Χ2 = 32.4 (3), p < .00001), only 33% ((27+29+18)/221) 

reported more than “a little bit” of concern over having 

non-family passengers, as compared to 47% for family 

passengers. 

Beyond the palpable conclusion that family trumps 

non-family, these results were hard to interpret. Could 

+

Task difficulty

Simple Hard

Yes 

No

Audience 

present 

- +

-

Figure 1. The 2x2 matrix of possibilities for task 

performance as defined by social facilitation 

theory. “+” means task performance is enhanced, 

“-” means it is degraded. 

Table 2. Influence of passengers (response frequencies) 

"not at all" or "a 

little bit" "somewhat" "quite a bit" "an extreme amount" 

Q36 passengers are not family 

147 27 29 18 

118 27 37 39 

Q37 passengers are family (N = 221, both questions) 
the majority of passengers be quite so persuasive? Or 

could the majority of pilots believe in their own skill and 

luck so profoundly that having others on board added 

absolutely nothing to their perception of risk?

Both those conclusions are absurd. What is more 

believable is that multiple motivations are confounded 

here, some facilitative, some inhibitory. And the net 

effect that any given passenger must have on a pilot’s 

willingness to take weather-related risks probably depends 

on the specific pilot plus the specific passenger plus the 

specific circumstances. A boss pressuring you to get to 

a meeting on time is different from taking your childhood friend out to practice touch-and-goes. Individuals 

matter, relationships matter, the flight mission matters, 

and so forth.

To wrap this up, both social and economic circumstances can obviously influence weather-related behavior. 

But there is a lot more to it than meets the eye. First, some 

factors may increase risk-taking, others decrease it. Second, 

situational details matter, and broad questions such as the 

ones asked here will not tease apart all the causal intricacies. Example: Question 17h was worded “Have social 

or business pressures ever influenced your GA go/no-go 

weather decision?” While not a terrible question, future 

interviews should try to devise more specific, theory-based 

questions better able to differentiate facilitation from 

inhibition and able to distinguish social inhibition from 

obedience, economic, and other motivations.

Theme 4: Pilot physiological state. Factors such as 

fatigue, hypoxia, and spatial disorientation affect weatherrelated decision making (Taneja, 2002). Interview time 

constraints prevented full exploration of all these factors, 

but we did sample disorientation. When asked, “How 

many times have you become so disoriented that you had 

to land or call ATC for assistance in determining your 

location?”, 22 pilots responded (Q31, average 1.1 episodes 

per pilot). Additionally, when asked, “How many times 

have you become so disoriented after entering IMC that 

you had difficulty in maintaining aircraft control?”, 35 

pilots responded (Q33, average 1.5 episodes per pilot). 

Given how relatively low-hour most of these pilots were, 

these numbers were surprisingly high. Fortunately, of pilots who admitted to disorientation, only 32% responded 

to both questions. Put another way, up to two-thirds of 

those who did experience serious disorientation may have 

undergone one-trial learning. Powerful experiences tend 

to have that effect.

Of course, the nagging facts remained that (a) about 

20% of mostly low-hour pilots reported getting seriously 

disoriented at least once and (b) there are more physiological effects than just disorientation. It seems that serious 

physiological disruptions may be more common than we 

thought. Moreover, these people, even if they did learn 

effectively, learned the hard and dangerous way. And the 

handful who went through the experience more than 

once may constitute a target group in need of training 

or intervention.

Theme 5: Impulsivity. Nearly every adult acts impulsively at least once in their adult life. Pilots are specifically 

trained to minimize impulsive behavior. Do they?

Fortunately for this analysis, more than a few pilots 

felt comfortable enough to admit to various kinds of 

impulsive or accidental rule-breaking behaviors such as 

taking off with no weather planning at all (Q17g, 40 affirmative responses) or flying into IMC without proper 

rating or aircraft (Q32, 35 affirmatives). 

This is evidence we should conservatively call “un-

intentional impulsivity” because none of the questions 

overtly asked about intentions (realistically, how many 

pilots would have admitted, in writing, to an FAA official, 

that they had intentionally broken any rule?)

So, these answers did show that pilots do act on impulse once in a blue moon. While the vast majority of this 

behavior obviously did not result in serious consequences 

in the sense of having hurt someone, the potential seriousness was impossible to gauge because the sample was 

biased. It contained no one who had ever been killed as 

a result of impulsive behavior. If such pilots could talk, 

they might judge impulsivity far more harshly than either 

we or these pilots did.

Inductive (bottom-up, emergent) themes

Induction is the opposite of deduction. In examining 

the data, patterns and themes emerge, induced by the 

evidence. 

Theme 6: The uncertainty of weather. A strong theme 

gradually materialized after multiple reviews of the data. 

It started with the broad idea of uncertainty. Pilots made 

at least 30 general and 21 specific remarks concerning 

the uncertainty of weather. So how could this effect of 

uncertainty be best understood?

There are at least four main influences that drive the 

uncertainty of weather prediction

Weather is chaotic

Most of us have difficulty understanding probabilities

Weather forecasts are biased towards false alarms

Weather risk increases with flight length

First, weather truly is hard to predict. Compared 

to simple, orderly probabilistic systems like cards or 

dice, the earth’s surface, oceans, and atmosphere form 

a complex, chaotic system in the strictest mathematical 

•

•

•

•
sense (Gleick, 1987). The mathematical models that 

forecasters use to make predictions are, by definition, 

simplifications which contain error. Over time, this error 

gets amplified and, inevitably, the forecast becomes less 

and less accurate. Anything over 24 hours is literally a 

“long time” in forecasting.

Second, estimating and understanding probabilities is 

something most of us are not terribly good at, particularly with rare events. Kahneman, Slovic, and Tversky 

(1982) and many others have shown that people often 

underestimate the occurrence of likely events that happen 

to be commonplace while overestimating the occurrence 

of much less likely events that merely happen to be more 

dramatic. The chance of being killed by lightning is a good 

example. Many of us would think this is fairly common, 

yet lightning claims just about 200 Americans each year 

(National Weather Service, 2006a). This is a small figure 

compared to the 44,000 typically taken by car accidents 

(Bureau of Transportation Statistics, 2006). It is as if we 

have a built-in tendency to confuse high drama with 

high likelihood.

A third factor driving the uncertainty of weather prediction is the fact that weather forecasts are biased towards 

false alarms. From a static observer-centered point of 

view—the point of view of any given observer standing 

still on the ground—weather forecasts tend to overpredict the chance of bad weather. This is a rather obscure 

mathematical artifact which requires some explanation.

Bad weather is something we want to detect. It is a 

“signal” buried in “probability noise.” This lets us draw 

the four outcomes defined by signal detection theory 

(Figure 2).

A “false alarm” happens when bad weather is predicted, 

but the actual weather turns out good (or at least better 

than predicted). A “miss” is when good weather is predicted, but the actual weather turns out bad (or at least 

worse than predicted).

Misses and false alarms are our two types of mistakes. 

Too many of either and we start mistrusting the weather 

forecast. 

Weather forecasters do not want us to mistrust the 

forecast. But they also know that people dislike being 

surprised by unexpected bad weather far more than 

they do being surprised by unexpected good weather. 

So, forecasts are typically conservative and effectively 

overpredict bad weather. In fact, when the forecast says 

“20% chance of rain,” all it really means is that, within 

a rather large, pre-specified geographical area during a 

pre-specified time period, there is a 20% chance of at least 

.01 inches of rain falling somewhere (National Weather 

Service, 2006b). 

This way of defining bad weather automatically leads 

to likelihood overestimation. The easiest way to understand is to think of weather as a shotgun. If you shoot at 

a cereal box 100 yards away, you may have a 20% chance 

of hitting it. But the chance of hitting a flea on top of 

that box is much smaller. Smaller size—less chance of 

getting hit. Now, just imagine you are the “flea” and the 

“cereal box” is the size of Connecticut. The idea is that 

weather probabilities apply to large geographic regions, 

not to one, tiny person. Your chance of personally being 

hit by something is typically much less than the forecast 

suggests.

Unfortunately, this bias has a great effect on aviation. 

Given this built-in tendency for false alarms, pilots start 

expecting the actual weather to be less severe than predicted. 

And then, like the boy who cried “Wolf,” along comes 

a time when the actual weather turns out far worse than 

predicted. Given enough time, this is statistically almost 

certain to happen.

The fourth influence driving the uncertainty of weather 

prediction is yet another statistical quirk, one biased in 

the opposite direction to the one just discussed. Longer 

flights usually involve more bad weather than short flights. 

The reason is based in statistical mechanics (Knecht, 

2000) but is easy to understand: The farther you fly, 

the more airspace you plow through. The more airspace 

you plow through, the more chance you have of running 

into bad weather somewhere. It is like walking through 

a minefield. The farther you travel, the more likely you 

are to step on something bad.

So, to summarize, uncertainty is rampant in weather 

forecasting. First, weather is chaotic, hence, truly hard 

to predict. Second, we innately tend to overestimate the 

likelihoods of some events while underestimating others. 

Third, weather forecasts are biased to overpredict bad 

HIT FALSE 

ALARM 

MISS CORRECT 

REJECTION

Actual weather

Bad Good

Bad 

Good

Predicted 

weather 

Figure 2. The 2x2 matrix of possibilities for 

weather prediction, as defined by signal detection 

theory.
weather for stationary observers. Finally, an opposite bias 

also occurs: that longer flights run greater weather risk.

With so many deep and contradictory forces at work, 

is it any wonder why weather confuses us?

Fortunately, many of the pilots we interviewed openly 

acknowledged this issue of uncertainty. At least 86 explicit 

references were made to specifics such as “expecting the 

unexpected” and of having preconsidered options such as 

diverting to alternate airports or driving instead of flying. 

This showed an appreciation of the underlying problem 

and a healthy evolution of strategies to combat it.

Sponsor-driven questions

This work was partially tasked to address an additional, 

sponsor-driven question, namely:

Theme 7: To what extent do mission goals influence 

weather analysis and decision making? Unfortunately, 

there was little evidence in this study to illuminate that 

theme beyond discussion of Theme 3. Since the current 

interview questions specified neither a range of weather 

nor of missions, it was no surprise that mission goals did 

not come up spontaneously as a theme.

Fortunately, the basic question was addressed by prior 

work. In unpublished data gathered by Knecht, Harris, 

and Shappell (2005), 105 GA pilots were asked about 

their willingness to fly in assigned visibilities ranging from 

1-5 sm and ceilings of 1000-2000’. One debrief question 

was, “If your flight mission had been critical (for example, 

delivering a human heart for surgery), how much would 

that change your willingness to take off/continue?” 

Given that hypothetical situation, virtually everyone

indicated a strong willingness to fly in the visibility/ceil-

ing combination to which they had been assigned. This 

was particularly striking, given that many had actually 

chosen not to fly, even when offered $200 to do exactly 

that. Now, while respondents admittedly may have been 

merely trying to cast themselves in a good light, the chances 

are good that these answers really did reflect a genuine, 

altruistic human concern for fellow citizens. 

If so, then this addresses the influence of flight mission. Given an extremely critical mission, nearly all pilots 

will fly into some fairly bad weather. Conversely, given 

severe weather coupled with an unimportant mission, 

nearly no one will. 

That defines two ends of a continuum. But remember that this is a multidimensional decision landscape 

with many possible weather characteristics and mission characteristics. As is usual in such cases, details 

are critical, and every scenario ends up having to be 

examined separately.

DISCUSSION AND CONCLUSIONS

This research was the second in a two-part series. 

Part I looked at quantitative data from 221 GA pilot 

interviews, categorizing the types of weather information pilots could use versus the ones they say they do 

use (Knecht, 2008).

Here in Part II, the emphasis was qualitative. Using the 

same data set, we looked for further evidence of weatherrelated factors that influence pilot decision making and 

which were either logically deducible facts or which could 

be arrived at inductively by scoring and tallying pilots’ 

free-response items. The underlying goal was hypothesisgeneration for possible future exploration.

The following factors were fairly strongly supported:

1. “Short list” of weather types uppermost in 

pilots’ minds

a. storms

b. ice

c. deteriorating visibility

d. lowering cloud ceilings

2. Specific pilot factors influencing the “short list”

a. instrument rating

b. experience with weather

3. Spatial disorientation

4. Uncertainty

a. forecast accuracy decreases as lookahead time 

increases

b. forecast probabilities are greatly misunderstood 

by most people

c. forecasts routinely overpredict bad weather for 

stationary observers

d. weather risk increases as flight length increases

The following factors found modest support:

5. Social and economic pressures can increase risktaking

6. Strictly impulsive behavior is rare but does 

exist

Finally, there was narrowly focused (but fairly strong) 

support found in previously unpublished data for the 

influence of:

7. mission goals

Factors 1 and 2 receive extensive intellectual training 

in GA flight training. But one important thing new 

pilots typically lack is the “fear factor” which can only 

be learned from the physical and emotional experience of 

storms, ice, and inadequate visibility.
Factor 3 concepts are also covered in flight train-

ing. But, again, unless pilots have spent time in special 

full-motion disorientation training simulators, physiological effects will probably end up being learned the 

“old-fashioned” way.

Most of Factor 4 receives little emphasis. We will return 

to it in a moment.

Factor 5 is one everybody acknowledges but nobody does much about because it is about psychology, not strictly about pilot training. To resist social 

and economic pressures takes knowledge, wisdom, 

and character as pilot-in-command. Flight schools 

cannot teach wisdom. That is a lifelong process. But 

they can offer guidance, encourage pilots to develop 

personal minima, and share personal accounts of 

weather encounters, both their own and those of pilots 

they know. Personalization can help make the abstract 

more concrete.

Factor 6 is the reason there are few “old, bold pilots.” 

Impulsivity catches up with them. Impulsivity is aviation’s 

crabgrass—we can control it but can never completely 

get rid of it. Our culture values daring behavior. Flying is 

synonymous with daring. So how do we honor our culture 

and still teach the disciple that discretion is usually the 

better part of valor? Maybe the answer is to teach that 

there is no honor in unnecessary risk.

Little direct support was found for Factor 7 in these 

data (although it was in other, unpublished data). Given 

the right mission, pilots will take on considerable risk. 

But the relation is complex and the details of the mission 

and the weather both matter.

Most of these factors are well-known and our rediscovery of them far from ground-breaking. But they are real, 

and our focus on them has to remain relentless. 

Factor 4 is perhaps the most intriguing and novel part 

of this report. We all know weather is uncertain, but we 

rarely sit down and think exactly why it is uncertain and 

why it is that the human mind has such difficulty with 

this particular style of uncertainty.

The uncertainty of weather starts with the physical 

world. Weather is complex and chaotic in the true mathematical sense—impossible to fully model, impossible to 

fully predict. That leaves probability as the best we can 

do to express how weather behaves.

Probabilities segue into psychology. By nature, the human mind hates uncertainty. The greater the uncertainty, 

the more uncomfortable we are with it. This discomfort 

is deep-rooted, genetic, and probably related to survival 

of the species. We seem to have biases. We overestimate 

some odds and underestimate others. Why? Because, in 

the still of the night, there may be tigers. And, those of 

our ancestors biased to worry a little too much about tigers 

may have ended up living longer than those who worried 

too little. So, these mental biases may be the remnants 

of tendencies that, under more primitive circumstances, 

conveyed survival advantage. But, here in the modern 

world, a side effect is that we overestimate the average 

severity of weather forecasts. 

Intuition tells us that bad weather is less likely to happen than the forecast says—if you stand in one spot. Now, 

statistical mechanics shows that intuition was right.

Finally, as if all this were not enough, we now know 

that the opposite is true, too, provided we are not standing 

in one spot. The farther we fly, the more adverse weather 

we’re likely to encounter.

We now have a clearer understanding of why weather 

forecasting is inherently problematic and will never be 

trivial. This is no cause for alarm or discouragement, 

but pilots do need to come to the same kind of clear 

understanding. Exactly how to do that will occupy us 

for some time to come.

Suggestions for further study and/or intervention

Uncertainty is the pilot’s greatest enemy. What we 

know and know of, we can either cope with or avoid. Five 

healthy, positive weather-related responses are:

1. Use and understand the modern weather products.

2. Expect the unexpected.

3. Always have multiple, workable options thought out 

ahead of time.

4. Do not wait to learn about weather the hard way.

5. There is no honor in unnecessary risk.

We can teach weather skills. The utility of low-cost, 

PC-based weather training comes to mind, as well as lowcost cockpit weather information devices (and we need 

to stress that the Flight Service Station is the lowest­-cost 

“device” of all).

Ultimately, we need even more reliable, more effective weather forecasts. In the meantime, pilots need to 

seek out the excellent preflight preparation products 

already in place (e.g., www.aviationweather.gov). Pilots 

need to understand clearly what those mean and how to 

use them. Right now, these weather providers are new, 

and we are all still learning how to make the most of 

them. Familiarity and understanding will spontaneously 

increase with use, so that aspect is self-correcting. In 

the meantime, human factors study of the graphic user 

interfaces might be of service to the National Weather 

Service and should be considered.
Endnotes

1

To understand Table 1, first notice the highlighted “Total” 

on the left-hand side. Now follow the percentage of pilots who 

mentioned weather factors in that row. 

Total 83 5 7 48 5 46 57 52

Each number represents the percentage of pilots who 

“voted” for that weather factor by mentioning it in their 

interview. Each cell percentage was calculated as

# of pilots who mentioned this factor at least once

total # pilots (N=221)

If a pilot mentioned a given factor more than once, it 

still only counted as one “vote.” Therefore, the smallest 

percentage a given factor could get was 0% and the largest, 100%. Notice that rows do not add up to 100%. We 

do not expect them to, because each factor was separate 

and based on its own separate tally of votes.

The row at left labeled “Total” considered instrumentrated and non-instrument-rated pilots together as a total 

group. Higher-than-average percentages (highlighted, 

bold) that emerged as preflight factors were storms (83%), 

ice (48%), cloud ceiling (46%), visibility (57%), and 

wind (52%).

In the same row, farther to the right, similar numbers 

emerged for in-flight factors—ones supposedly influencing 

pilots’ go/no-go weather decisions while in-flight. 

11 81 5 4 42 5 41 49 38

Here, higher-than-average percentages emerging as

in-flight factors were storms (81%), ice (42%), cloud 

ceiling (41%), and visibility (49%).

Instrument flying is different from VFR flight, so we 

sorted pilots by instrument rating. These percentages 

form the two rows labeled (at left) “Non-IR” and “IR.” 

In these two rows, highlighting and boldface again mark 

the most frequently mentioned factors. 

The limitations of Table 1 must be explained. Overall, what we were trying to do was get a sense of which 

weather factors were most important to pilots. Since each 

cell represented the percentage of pilots who mentioned 

a particular factor at least once during their interview, 

we assumed that the more pilots who mentioned that 

factor, the more important the factor was likely to be in 

their decision making. Thus, the higher numbers probably reflected the more important weather factors. The 

question then became how big did the numbers have to 

be to be considered reliable?

The first step was to compare each cell in a row with 

its corresponding row mean. This would determine which 

weather factors were above average. To establish reliabil-

ity, in each of the “Total,” “non-IR,” and “IR” rows, a 

standard error of proportion (SEMrow) was calculated to 

estimate the stability of that row mean (Ferguson, 1971, 

ch. 12). Then, each individual cell score was evaluated 

against (row mean + 1.65 SEMr

, ≈ “p < .05,” 1-tailed). 

Note that this is not the same as finding “the upper 5% 

of cell values.” It represents finding which weather factors seem reliably more important than average. Thus we 

expect to see about half the factors identified, which is 

the case in Table 1. Statistical purists may criticize this 

method, but we need to keep in mind the largely qualitative, exploratory nature of this study.

The second thing we wanted to do was compare non-

instrument-rated pilots with instrument-rated pilots 

to see which weather factors seemed more important 

to which group. This method was considerably more 

precise. It involved comparing cells by column, between 

“Non-IR” and “IR” for each weather factor. To do that, 

a separate SEMcolumn was calculated for each “(Non-IR) 

– IR” difference score (because SEM is a function of both 

proportions), and that was used to do z-tests (Ferguson, 

1971, ch. 12). In this case, we elected to use a much 

more stringent criterion for reliability, p < .02, 2-tailed, 

to correct for multiple comparisons.

To summarize, the primary limitation here was that the 

method was extremely lax about the “row conclusions,” 

while being moderately strict about “column conclusions.” 

Therefore, the reader is strongly cautioned to keep this 

in mind, as appropriate to the circumstance.

2

In Table 1 (and throughout this report), most numbers 

presented are not necessarily statistically significant. This 

involves a technical issue having to do with experimentwise error. When many statistical results are reported in 

one study, there is a good chance that at least some of 

those “significant” results will be false. However, if one 

uses standard procedure to statistically correct for this 

(e.g., the Bonferroni correction), then one lands squarely 

in territory where nothing is “significant.” This is one 

reason why, in exploratory research such as this—and, 

particularly in qualitative research—results are typically 

presented “as is,” with no deep statistical analysis, and 

no correction for experimentwise error. 3

At first glance, it might seem appropriate to do analysis of variance (ANOVA) on these numbers. But many 

variables (e.g. ,storms, rain, hail) are correlated and, to an 

unknown degree, violating the assumptions of ANOVA 

and rendering it inappropriate.
10

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of transportation fatalities by mode. Downloaded 

June 28, 2006 from www.bts.gov/publications/

national_transportation_statistics/2002/html/

table_02_04.html.

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Federal Aviation Administration.

Ferguson, G.A. (1971). Statistical analysis in psychology 

and education. New York: McGraw-Hill.

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theory and psychophysics. New York: Wiley.

Kahneman, D., Slovic, P., and Tversky, A. (Eds.). (1982). 

Judgment under uncertainty: Heuristics and biases.

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45th Annual Meeting (pp. 87-91). Santa Monica, 

CA: Human Factors and Ergonomics Society.

Knecht, W., Harris, H., and Shappell, S. (2005). 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. (Technical 

Report DOT/FAA/AM-05/7). Washington, DC: 

Federal Aviation Administration, Office of Aerospace Medicine.

Knecht, W. (2008). Use of weather information by general 

aviation pilots, Part I, quantitative: Reported use and 

value of providers and products. (Technical Report 

DOT/FAA/AM-08/6). Washington, DC: Federal Aviation Administration, Office of Aerospace 

Medicine.

Miles, M.B., and Huberman, A.M. (1994). Qualitative 

data analysis. Thousand Oaks, CA: Sage.

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work published 1974).

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Downloaded June 28, 2006 from noaa.kids.us/

lightnin.htm.

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gov/sgx/research/Guide/Behind_the_Forecasts.

pdf.

Rhoda, D.A. and Pawlak, M.L. (1999). An assessment 

of thunderstorm penetrations and deviations by 

commercial aircraft in the terminal area. (Technical Report NASA/A-2). Lexington, MA: Lincoln 

Laboratory (MIT).

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accidents: Can spatial disorientation training be 

an effective intervention strategy? Indian Journal 

of Aerospace Medicine, 46(2), 59-64.

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269-74.
A-1

APPENDIX A 

Pilot Interview 

1. Age____ 

2. Gender (male __, female __) 

3. Primary occupation ____________________________________________________________________ 

4. Other current occupation(s)____________________ 

5. Past occupations(s) related to aviation______________________________________________________ 

6. Certificates and ratings (check each that applies) 

Sport Airplane Single-Engine 

Recreational Airplane Multiengine 

Private Rotorcraft 

Commercial Balloon 

ATP Airship 

Instrument Glider 

Flight Instructor Powered-Lift 

7. Type of flying you do (to the nearest 10 percent, for example, recreational 20%)

recreational____ business____ corporate____ commercial____(these should add to 100%) 

For questions below, “general aviation” (GA) means “any small aircraft not flying for hire.” 

8. Your total GA flight hours (best guess) ___________ Total hours in last 90 days___________ 

9. Do you own your own GA aircraft, either by yourself or as a member of a partnership? (Y / N) 

10. Type(s) of GA aircraft usually flown:____________________________________________________ 

11. Your normal personal minimum for GA VFR visibility ________ statute miles 

12. Your normal personal minimum for GA VFR cloud ceiling ________ feet AGL 

For questions below, if you’re not a U.S. citizen, use “country” instead of “state” 

13. Current home state (legal residence) _______________________ 

14. Approximate percentage of time you’ve flown GA in your home state _____% versus 

 outside your home state _____% (est.: add up to 100%) 

15. State(s) where you received GA pilot training____________________________

16. States where you’ve flown GA (put a check mark D in each state name below) 

If your flying has been largely 

outside of the USA, please list 

 below the countries in which you 

 regularly fly and the percentages 

of time spent in each (estimates): 

Country % time 

SME#
A-2

This is a study about how GA pilots use weather information. Please bear in mind these things: 

A. We already know the “textbook answers” for how pilots are supposed to use weather information. What we 

need to know is how real pilots are using real weather information in the real world. 

B. Your responses are strictly anonymous and confidential. 

C. In the next section we’ll refer to “cross-country flights.” That may mean different things to different 

people. So define “cross-country” as: 1) Non-local airport, far enough away that the weather could surprise 

you.

D. “Bad” weather can also mean different things. So define it as: Weather serious enough to challenge your 

skill level and the aircraft’s capabilities.

SECTION TWO: CROSS-COUNTRY, BAD WEATHER GA FLIGHT 

17. This question will ask details about how you get a PREFLIGHT weather briefing for CROSS-COUNTRY, 

GA FLIGHT when you ANTICIPATE BAD WEATHER. Use the definitions of “cross country” and “bad 

weather” from above in forming your responses. 

a. When do you start planning such a flight? (for example, the day before, the morning of, etc.) 

b. Where do you start researching the weather? (e.g., at home? At the airfield?) 

(Below, a weather “product” is a single report like a METAR, TAF, ASOS, or AWOS. A 

“provider” is an organization like the FSS that bundles individual products together to 

give a comprehensive wx outlook) 

c. List the main weather information provider(s) you consult. List the main products you use from 

each provider. What relative importance do you give to these products? (write “1” by the most 

important product, “2” by the second-most important product, etc. 

d. About how many minutes does usually it take to finalize your bad-weather GA plan?______ 

e. List the major weather factors that would immediately trigger a no-go decision before takeoff. 

f. What weather factors would lead you to divert a flight in progress? 

g. Is there any time you anticipated bad weather but took off without planning for it? If so, describe it 

briefly. Remember—this is 100% anonymous, so do NOT name names of individuals involved. 
A-3

h. Have social or business pressures ever influenced your GA go/no-go weather decision? (For 

example, have you ever made a risky flight on a dare, or has a boss ever pressured you into flying 

against your better judgment?). If so, describe it, taking care not to name names. 

i. In plain words, describe what goes through your mind in planning for bad-weather, cross-country 

GA flight. 

j. Briefly, how does your good-weather planning differ from your bad-weather planning? 

k. If there were one thing you’d like to see improved about weather information, what would it be? 
A-4

SECTION 3: CROSS-COUNTRY, BAD-WEATHER INFO. SOURCES (IN-DEPTH REPORT)

(As before, a “product” is a single report. A “provider” combines products to give a big picture) 

18. Evaluate the top 5 preflight weather providers you use most to plan a cross-country, bad-weather flight. 

a. Rank: Using the 1-to-5 scale below, rank ONLY your 5 most-used providers (leave others 

blank).

1 2 3 4 5 

 most-used above average average below average least-used 

b. Value: Using the 1-to-5 scale below, rate the information value of each of those top 5 choices. 

1 2 3 4 5 

 excellent above average average below average poor 

c. %: Estimate the percentage of cross-country, bad-wx flights you use each of these top 5 providers 

on. (NOTE: In 18c, 19c, and 20c, the percentages do NOT have to add up to 100%) 

d. Minutes: Estimate the average number of minutes spent on each of the 5 during bad-wx preflight. 

Rank Value Provider Format Details % of flights Minutes

1-5 1-5 used on spent

Commercial vendor Internet Wx via internet, paid (Which site?__________________)

Public NWS or NOAA site Internet Wx via internet, free (Site(s)? _____________________)

DUATS Internet FAA Direct User Access Terminal

DUATS at airport

FSS telephone Flight Service Station, automated briefing (TIBS)

FSS telephone FSS standard briefing

FSS telephone FSS, abbreviated briefing

FSS telephone FSS, outlook briefing

The Weather Channel Internet,TV Cable TV weather

Other sources List_________________________________________
A-5

19. The same way you did in Q18, evaluate the top 8 preflight weather products you use most in planning a 

cross-country, bad-wx flight. “Text” format means sources you read yourself or that are read to you. 

a. Rank ONLY your 8 most-used products. Write “1” next to the source you use most, etc. 

b. Rate the value each of these 8 using the 1-5 scale of Q18b, for its information value.

c. Estimate the percentage of cross-country, bad-wx flights during which you used each of the 8. 

d. Estimate the average number of minutes spent on each of the 8 during bad-wx preflight. 

Rank Value Product Format Details % of flights Minutes

1-8 1-5 used on spent

AC text Severe Wx Outlook Narrative (2-day convective outlook)

AIRMET / SIGMET text Icing, turbulence, IFR, convective advisories, watches

ASOS radio Automated Surface Observing System

ATIS radio Automated Terminal Information Service

AWOS radio Automated Weather Observing System

charts, Air- or Surface-analysis graphic Constant-pressure (isobar) charts

charts, Convective outlook graphic 48-hr forecast charts for T-storm activity

charts, Prog. graphic 12, 24-hr prognostication charts w. isobars, wx symbols

charts, Radar (NEXRAD) graphic Doppler radar maps

charts, Radar summary graphic Maps of precipitation regions

charts, Weather depiction graphic Maps with isobars, precip, IFR regions, ceilings

FA text Aviation area 18-hr forecast

FD text Winds and temps. aloft 12-hr forecast charts

FD graphic Winds and temps. aloft 12-hr forecast charts

GPS T or G Global positioning satellite

LLWAS radio Low-Level Wind Shear Alert System (at airports)

METAR text Meteorological Aviation Routine

PIREP text Pilot reports

Satellite graphic Satellite photos of cloud cover

SD text Radar weather reports (hourly)

TAF text Terminal Aerodrome Forecast

TWEB text Transcribed Weather Broadcast (over telephone)

WW, AWW text Weather Watch bulletins, severe

Other sources List_________________________________________

20. The same way you did in Q18, evaluate the top 3 en route weather sources you use most during a crosscountry, bad-weather flight (here, a “source” can either be a product or a provider). 

a. Rank: Rank ONLY your 3 most-used sources. Write “1” next to the source you use most, etc. 

b. Value: Using the 1-5 scale of Q18b, how do you rate each of these 3 source’s information value? 

c. %: Estimate the percentage of cross-country, bad-weather flights you use these 3 sources on. 

d. Minutes: Estimate the average number of minutes you spend on each during bad-wx flight. 

Rank Value Source Details % of flights Minutes

1-3 1-5 used on spent

avionics (e.g. on-board radar, Stormscope, etc) List_______________________

ASOS Automated Surface Observing System

ATIS Automated Terminal Information Service

AWOS Automated Weather Observing System

EFAS Enroute Flight Advisory System (Flight Watch through FSS)

HIWAS Hazardous Inflight Weather Advisory System (selected VORs)

TWEB Transcribed Weather Broadcast (over VOR, NDB)

Other sources List_________________________________________

21. Are there reasons why the preflight and enroute sources you USE most aren’t the ones you VALUE most? 

If so, why? (For example, some of the graphic Internet products download slowly on a modem. Or some 

products may be unavailable. Or you might consider some too incomplete or unreliable). 

22. What percentage of FSS briefers do you think are National Weather Service-certified? (best guess) _____ 

23. What percentage do you think are pilots? _____ 
A-6

24. Would it matter to you if your briefer were not a pilot, as long as he/she were NWS-certified? (circle 

answer)

 1 2 3 4 5 

 not at all a little bit somewhat quite a bit an extreme amount 

25. If you use FSS weather briefings, how satisfied are you with them? (leave blank if you don’t use FSS) 

 1 2 3 4 5 

 not at all a little bit somewhat quite satisfied extremely 

satisfied 

26. What is the typical number of weather reporting stations (e.g. KOKC, KDWF) you check before an average 

4-hour, bad-weather GA flight?____ The smallest number?_____ The largest number?_____ 

Regarding VFR LOCAL FLIGHT, what percentage of the time do you do the following (0-100%)? 

27. I get a briefing on the weather before I take off ................... ____ 

28. I request weather updates during flight .............................. ____ 

Regarding VFR CROSS-COUNTRY FLIGHT, what percentage of the time do you do the following? 

29. I get a briefing on the weather before I take off .................... ____ 

30. I request weather updates for route & destination during flight ____ 

Answer questions 31 through 34 using a scale of “0” through “6 or more”: How many times have you ... 

31. become so disoriented that you had to land or call ATC for assistance in determining your location? ____ 

32. flown into areas of IMC without an instrument rating or an instrument-qualified aircraft? .......... ____ 

33. become so disoriented after entering IMC that you had difficulty in maintaining aircraft control? ____ 

34. turned back or diverted to another airport because of bad weather while on a VFR flight? ........... ____ 

Use the scale below to answer Qs 35-38 

 1 2 3 4 5 

 not at all a little bit somewhat quite a bit an extreme amount 

35. How much does the distance you have to fly through bad weather affect your willingness to fly? ____ 

36. Does having non-family passengers affect your willingness to fly in bad weather?.................... ____ 

37. Does having family passengers affect your willingness to fly in bad weather? ........................ ____ 

38. Has social or corporate pressure ever affected your willingness to fly in bad weather? ............... ____ 

39. Have you ever had a life-threatening flight experience related to weather? (Y / N) ................... ____ 

 (On Q 39, if answer is 3, 4, or 5, please briefly describe your experiences). 

THIS CONCLUDES THE SUBJECT MATTER EXPERT INTERVIEW. THANKS AGAIN. 
B-1

APPENDIX B 

(Top) Coding rubric for qualitative analysis, with frequencies of incidence. 

(Bottom) Deductive versus inductive elements of the rubric.

At destination 

How well do 

I know the 

weather?

At origin En route 

Increasing uncertainty 

How bad 

does it seem 

to me?

How strong is 

my motivation 

to fly?

How strong is 

my motivation 

to remain safe?

Subjective Objective Subjective

What is my 

physical state?

How good is 

my equipment? 

How skilled / 

experienced 

am I? 

Subjective

Objective 

U

n

c

e

r

t

a

I

n

t

y

What are my 

options?

5. Coded by body-related 

statements such as “fatigue,” 

“hunger,” “disorientation.” 

A. In general text. 

B. Qs 31, 33 

4

50

2. Coded by presence of key 

affective risk-acknowledgment 

phrases such as “risk,” 

“caution,” “could I crash,” 

”worth the trouble.” 

A. In general text. 

B. Due to passengers 

(Qs 36, 37)

49

103

1. Coded by statements such 

as “I really wanted to fly” or “I 

felt pressure from my 

employer to fly” or “I just felt 

like going.” 

A. Internal pressure 

(self-generated) 

B. External pressure 

(Qs 17h, 38) 

C. Impulsivity (Q17g, 

35 32)

40

48

4

4. Coded by references such 

as “my own skill level,” or “I 

had done this before.” 4

3. The goal here was to 

capture statements about 

uncertainty such as 

“unpredictable,” “rapidly 

changing,” or “looking for 

trends.” 30A.

 General 

 21B Specific

7. Statements such as “what 

are my options,” “could I just 

drive,” or any reference to 

alternate airports. 86

6. Coded by references to 

aircraft & nav. capabilities 

such as “whether my ship 

could handle it,” “have enough 

fuel,” or “up-to-date charts.” 17

Theory-driven elements 

(Deductive)

Data-driven elements 

(Inductive)