William R. Knecht

Michael Lenz

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

September 2010

Final Report

Causes of General Aviation WeatherRelated, Non-Fatal Incidents: 

Analysis Using NASA Aviation 

Safety Reporting System Data

DOT/FAA/AM-10/13

Office of Aerospace Medicine

Washington, DC 20591

OK-10-0077-JAH

Federal Aviation

Administration
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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i

Technical Report Documentation Page 

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

DOT/FAA/AM-10/13 

4. Title and Subtitle 5. Report Date 

Causes of General Aviation Weather-Related, Non-Fatal Incidents: Analysis September 2010 

Using NASA Aviation Safety Reporting System (ASRS) Data 6. Performing Organization Code 

 

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

Knecht WR, Lenz M 

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-07-HRR-521 

16. Abstract 

Adverse weather remains a major cause of general aviation accidents. However, weather alone is never the sole culprit. 

Searching for other salient causal factors, we turned to incident analysis. Incidents are less serious than accidents, but far 

more common, and have witnesses to better determine causes. 

The current research examined 100 GA weather-related incident reports made to the Aviation Safety Reporting System 

(ASRS) during 2005-06. With pilot permission, ASRS gathered additional data on nearly 300 variables related to possible 

root causes. The following factors seemed to constitute a problem for 5%, or more, of pilots: 

1. Darkness (4 dusk +17 night = 21% of pilots). 

2. Moisture affecting visibility (clouds, fog, rain, snow > 50%) and/or air movement affecting aircraft handling 

(thunderstorm, icing, turbulence > 25%). 

3. Multiple weather factors experienced simultaneously (85%). 

4. Failure to get a preflight weather briefing, or “briefing” with only a low-grade (non-aviation-oriented) source (5%). 

5. Deterioration of weather forecast accuracy over time (66% correct forecasts at departure, decreasing to 37% correct 

at destination). 

6. Weather that materialized worse than predicted (35%. This implicitly includes lack of en-route forecast updates). 

7. Lack of weather-related training and experience (> 50%, non-instrument-rated and new instrument-rated pilots). 

8. Inadequate equipment (less-experienced pilots tend to have less-capable airframes and avionics). 

9. Ambulance missions (7%, particularly helicopter ambulance). 

10. “Non-weather-related factors”: decision-making (26%), time pressure (21%), “get-home-itis” (9%), aircraft 

equipment problem (8%), fatigue (7%), distraction by passenger or crew (5%). 

In broad terms, this analysis reveals two major at-risk target groups with distinct training needs: 

 Non-instrument-rated pilots 

 Newly minted instrument-rated pilots

 

17. Key Words 18. Distribution Statement 

 

 

Aviation Weather, Incidents, Accidents, Causes, ASRS Document is available to the public through the 

Defense Technical Information Center, Ft. Belvoir, 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 45 

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

iii

Acknowle

We are grateful to Chuck Drew (Battelle Corporation) and Elisa Taube (Aviation Safety Reporting System 

Research Coordinator), who led the ASRS team in a preliminary analysis of the data. Thanks are also due to all 

pilots who take the time to fill out ASRS incident reports, for these provide the statistical data that help save the 

lives of their fellow airmen.

v

Co

Introduction.

Motivation for the Current Study. 

Background—Quantifying the Degree of Risk Posed by Adverse Weather. 

Categorizing and Modeling the Causes of Risk. 

Perceptual Errors  

Skill Errors  

Decision Errors. 

Procedural Violations. 

Modeling Accident Causality and Risk. 

The Devil Is in the Details  

Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

How These Data Were Collected. 

Participants. 

Analytical Procedure. 

Study Limitations. 

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

Preliminary Conclusions. 

Supporting Analysis—Whole-Group. 

ASRS Report. 

Nature of the Weather Encountered. 

Information Quantity and Quality in Weather Information Briefings. 

Pilot Weather Training, Experience, and Expertise. 

Equipment Factors. 

Non-Weather-Related Factors  .

Recovery Strategies. .

Summary of Whole-Group Analysis. .

Preliminary Statistical Summary. .

Parsing the Group Data on the Basis of This Preliminary Analysis. .

Non-Instrument-Rated Pilots. .

Weather Training and Experience. .

Equipment. .

Non-Weather Factors. .

Crosscheck With “Stories”  .

Summary—Non-Instrument-Rated Pilots. .

Instrument-Rated Pilots. .

Air Transport Pilots. .

Summary—ATPs. .

Non-ATP IR Pilots. .

Summary— Non-ATP IR Pilots. .

Group Pattern Differences. .

Equipment. .

Discussion.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  .

The Importance of Training. .

Conclusions and Recommendations. .

Future Exploration. .

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Appendix A. Post-Classical ADM.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  .

Appendix B. General Aviation Weather Encounters Supplemental Coding Structure.. . . . . . . . . . . . . . . . . . .  .

Appendix C. Aircraft Classification Categorie.                                                                                             .

Appendix D. Synopsis of Interview Section F, Free-Response Explanations of Why the Incident Occur

vii

LIST of Abb

ACARS Aircraft Communications Addressing and Reporting System

ADM Aeronautical decision making

ATC Air traffic control

ATP Air Transport Pilot

CFI Certified Flight Instructor

CFII Certified Flight Instructor-Instrument

CFIT Controlled flight into terrain

FAA U.S. Federal Aviation Administration

FBO Fixed-base operator

FSS Flight Service Station (a.k.a. AFSS, Automated Flight Service Station)

GA General aviation

GPS Global Positioning System

HCA Hierarchical cluster analysis

IFR Instrument flight rules

ILS Instrument landing system

IMC Instrument meteorological conditions

IR Instrument-rated

NAS National Airspace System (U.S.)

NEXRAD Next-Generation Radar

non-IR Not instrument-rated

NTSB National Transportation Safety Board (U.S.)

NWS National Weather Service (U.S.)

PTT Push-to-talk (microphone key)

TAA Technically Advanced Aircraft

VFR Visual flight rules

VMC Visual meteorological conditions

VOR VHF omnidirectional range

1

Causes of General Aviation Weather-Related, Non-Fatal Incid

Analysis Usig NASA Aviation Safety Reportin

Intr

A pilot, his wife, and daughter fortunately survived 

the accident below.

In August of 2005, the pilot had received his private 

pilot certificate on a Tuesday, and this accident occurred 

the following Saturday. It was his first cross-country flight, 

travelling with his family, from Fort Mill, NC, to Myrtle 

Beach, SC, to enjoy the beach.

While en route, he had seen an area of weather along 

the route and diverted to Lumberton, NC. In Lumberton, he was checking the automated weather terminal 

at the fixed base operator (FBO) when an instrumentrated (IR) pilot and a flight instructor offered assistance. 

They suggested that he take his family to lunch while 

the thunderstorms passed, and the trip could likely be 

completed as planned after lunch. Instead, he elected to 

depart immediately and return home, attempting to do 

so before the weather reached the Lumberton airport.

As he taxied out for departure, the approaching weather 

was now visible; however the winds were calm. As he 

began his takeoff, the storm winds began to impact the 

runway area. The plane became airborne, and the pilot 

was in the process of retracting the landing gear when the 

gusts and downdrafts struck, driving the aircraft into the 

ground and literally breaking it into pieces. Remarkably, 

no one was injured. As bystanders arrived on the accident 

scene to offer assistance, they heard the pilot say, “Why 

didn’t somebody tell me it could be this bad?”

Motivation for the Current Stu

The above story starkly reminds us how easily bad 

weather can kill general aviation (GA) pilots. Why? In 

truth, the typical “weather accident” involves not just 

adverse weather but additional factors as well (Wiegmann 

& Shappell, 2003). So, what are these factors that conflate 

with and potentiate weather to bring on accidents?

This study will involve a data-mining approach, rather 

than the hypothesis-testing approach commonly used 

in experimental studies. Its purpose will be to look for 

relations between potential causal factors in existing data, 

with the intent of identifying factors that relate to each 

other. For instance, do low-hour pilots also tend to fly 

less weather-capable aircraft? If so, we could hypothesize 

that aircraft weather capability and pilot flight experience 

interact to increase weather-flight risk. Such a hypothesis 

could subsequently be tested in a flight simulator study, 

and the results be made known to FAA policymakers 

and rulemakers, as well as to pilots and equipment 

manufacturers.

Background—Quantifying the Degree of Ri

Posed by Adverse Weathe

Natural forces can be capricious, powerful, hard to 

understand, and physically hard to control or avoid. 

Consequently, weather-related accidents are disproportionately disastrous compared to most other types of 

accidents. According to a U.S. National Transportation 

Safety Board (NTSB) report, “about two-thirds of all...

GA...accidents that occur in instrument meteorological 

conditions (IMC) are fatal” (NTSB, 2005). Based on a 

study of Australian GA accidents and incidents,1

 Batt & 

O’Hare (2005) stated that 76% of GA visual flight rules 

flight-into-instrument meteorological conditions (VFRinto-IMC) accidents involved a fatality. 

According to NTSB data, VFR-into-IMC was the 

top single cause of GA fatalities during the years 1983-

2002 (Bazargan, 2005). In an effort to quantify this risk, 

Bazargan defined a metric2

 called “risk ratio” R as

1 Mishaps fall into three formal categories. “Fatalities” involve just that. 

“Accidents” involve substantial, non-fatal damage to persons and/or property. 

Finally, “incidents” involve no fatalities and no substantial damage but represent 

situations where a lapse or potential lapse of safety occurred, or came close to 

occurring, in the judgment of some expert, either a pilot, air traffic controller, 

or professional safety official.

2 The symbol “N” stands for “number of.”

16.8

Non-fatal VFR-into-IMCaccidents Non-fatal accidents

Fatal VFR-into-IMCaccidents Fatal accidents

VFR−into−IMC = = N N

N N R (1) 
2 

By way of context, other top GA accident causal factors have far lower risk ratios 

(e.g., Rairspeed = 1.7, Raircraft control = 2.8).

Categorizing and Modeling the Causes of Ri

To better understand what causes weather-related accidents, we seek to organize and categorize risk factors. 

Our approach is to examine perceptual errors, skill errors, 

decision errors, and procedural violations.

Perceptual Errors 

Perceptual errors are particularly associated with 

adverse weather. The brain cannot accurately process 

what the senses do not accurately perceive. For example, 

judging cloud clearance is notoriously difficult (Coyne, 

Latorella, & Baldwin, 2005, Wiggins & O’Hare, 2003), 

as is judging slant range through fog or mist. Likewise, a 

violent updraft can appear without prior cues or warning 

almost instantaneously near thunderheads and literally 

rip the wings off a small aircraft. How do we avoid what 

we cannot even see? The answer lies in learning to assess 

the likelihood of risk and in practicing strategic avoidance3

when that likelihood reaches a certain threshold.

Skill Errors 

Skill errors are easy to understand but harder to correct 

because we tend not to be aware of a given skill deficiency 

until after some close call points it out to us. This is a 

particular problem with skills normally not required, 

which consequently are rarely practiced. A good example 

would be a pilot from a normally warm, dry climate 

suddenly encountering wing icing. How do we maintain 

proficiency at something we rarely get to practice?

Skill errors can be difficult to identify as accident causes 

after the fact because, in the case of fatalities, there is no 

pilot left alive to interview. Conversely, in non-fatal cases, 

people may not perceive (and rarely openly admit to) their 

own shortcomings, especially when doing so might result 

in swift, substantial punishment by authorities. This can 

make objective study of skill errors difficult.

Decision Errors

Pilot decision errors are another, often-quoted, psychological factor involved in GA accidents. This falls 

under the rubric of “aeronautical decision making” 

(ADM). Appendix A contains an extensive list of ADM 

error factors (FAA Joint Safety Analysis Team, 2002). 

These have been listed as causal in as many as 50% of 

GA accidents, depending on the reviewer. (Wiegmann 

& Shappell, 1997; Jensen, 1995).

3 For instance, maintaining proper distance from an entire region of thunderheads, rather than just the few closest to us.

Decision errors may be influenced by unconscious 

conditions, including subtle affective (emotional) motivations. For example, Batt & O’Hare (2005) concluded that 

the midpoint of an adverse-weather flight—no matter how 

long the flight—may represent a psychological “tipping 

point,” after which there is an increased tendency for 

an adverse-weather flight to end up VFR-into-IMC (as 

opposed to weather avoidance or diversionary landing). 

This finding supports a variety of hypothesized psychological motivations for VFR-into-IMC, ranging from the 

folksy “get-home-itis” to the more esoteric idea of “sunk 

cost”4

 (Kahneman & Tversky, 1979; Goh, Wiegmann, 

& O’Hare, 2002).

Procedural Violations

Seasoned accident investigators will quickly add “pilot 

not following proper procedures” as a cause of many accidents. However, ADM and procedural violations must 

be related since, if we assume that most pilots know most 

procedures, then a decision not to follow a proper procedure can usually be defined as poor decision making. 

However, there are inherent problems with simply 

declaring “poor decision making” as a cause of accidents. 

First, blaming “poor decision making” alone is a tautology.5

 Since we usually define a “poor decision” as “one that 

winds up having bad consequences,” it then logically 

follows that 100% of all accidents must be caused by 

someone exhibiting “poor decision making” somewhere. 

This leads to a classic situation in logic called the 

nominal fallacy.6

 Setting up “poor decision making” as a 

one-size-fits-all causal factor may sound satisfying, but 

it does not really explain anything. Take for example 

“controlled flight into terrain” (CFIT). No normal person 

purposely crashes a perfectly good airplane. Yet, there is 

often a tendency to think that, when such an accident 

is blamed on “poor decision making,” we have actually 

explained what caused that accident. 

Nothing could be further from the truth. When 

it comes to weather, two pilots can make very similar 

decisions, yet one pilot ends up arriving safely while the 

4 Sunk cost is the notion that the more time or treasure we have “sunk into” 

(i.e., “invested in”) some plan, the more many of us stubbornly stick to that 

plan, even when confronted with apparent loss and failure of the plan. Perversely, the human mind is often more attracted to uncertain gains than it is 

repelled by uncertain losses. 5 A tautology is an argument that is true merely by how its terms were defined 

in the first place. The statement “All men are mortals, therefore all men die” 

is a tautology because the word “mortal” is already predefined as “subject to 

death.” Rephrased, the original statement then becomes “All men are subject 

to death, therefore all men die,” which sounds far less erudite than the original 

statement. Colloquially, a tautology might be thought of as a trivial statement 

dressed up to look like a profound one.

6 The nominal fallacy is the mistake of thinking that, by assigning a name 

to something, we have actually explained it. For instance, claiming that 

“gravity” causes objects to fall actually explains nothing about what causes

gravity—only how gravity behaves. How something behaves is different from 

what causes it to behave.
3

other ends up crashing. So did the pilot who crashed 

exhibit “poor decision making” while the other did not? 

Obviously, there is much more to the “quality” of a decision than whether or not it results in success on a single, 

given occasion. 

Modeling Accident Causality and Risk

H.W. Heinrich (1959) proposed a “pyramid model” 

of accident causation and risk (Figure 2), in suggesting 

that for every major fatal accident there are multiple 

non-fatal accidents, for every non-fatal accident there 

are multiple incidents, and for every incident there are 

multiple unreported unsafe acts.

Me

How These Data Were Collec

To look for categories of hazardous situations and their 

root causes, we normally need large amounts of data, 

otherwise we run the risk of incurring sampling error.7

This report analyzes data collected from 100 detailed aviation incident reports.8

 Incident analysis is a highly useful 

technique for gathering data and generating hypotheses 

about “how pilots nearly get into trouble.” Like the 

Heinrich Pyramid, incident analysis relies on the modest 

assumption that studying a fairly large group of people 

nearly getting into trouble can tell us much about how a 

small group of people may actually get into trouble in the 

future. This approach is particularly useful in situations 

where accidents tend to (a) be inherently rare, (b) have 

missing or incomplete telemetry, and (c) involve fatalities, 

making it impossible to interview key witnesses. 

Put rather starkly, we can interview a survivor.

The data for this report were collected specifically for 

the FAA by the Aviation Safety Reporting System (ASRS). 

By its own self-description (ASRS, 2006),

The ASRS was established in 1975 under a Memorandum of Agreement between the Federal Aviation 

Administration (FAA) and the National Aeronautics and 

Space Administration (NASA). FAA provides most of the 

program funding; NASA administers the program and sets 

its policies in consultation with the FAA and the aviation 

community. NASA has chosen to operate the program 

through a contractor selected via competitive bidding.

The ASRS collects, analyzes, and responds to voluntarily 

submitted aviation safety incident reports in order to lessen 

the likelihood of aviation accidents. ASRS data are used to: 

•Identify deficiencies and discrepancies in the National 

Aviation System (NAS) so that these can be remedied by 

appropriate authorities.

•Support policy formulation and planning for, and 

improvements to, the NAS.

•Strengthen the foundation of aviation human factors 

safety research. This is particularly important since it is 

generally conceded that over two-thirds of all aviation 

accidents and incidents have their roots in human performance errors.

These voluntary incident reports form one of the 

major sources of aviation safety research data in the U.S. 

By reputation, ASRS is both non-punitive and strongly 

committed to maintaining the anonymity of its reporters. 

7 Sampling error is error created by measuring a sample rather than the entire 

population. The smaller the sample size, the greater the risk of that sample 

failing to resemble the broader population.

8 N=100 is arguably a medium-sized sample. Therefore, readers may want to 

consider this study exploratory.

Figure 2. A modified "Heinrich Pyramid."

The Heinrich model assumes that some percentage of 

unsafe behaviors become incidents, a smaller percentage 

become accidents, and an even smaller percentage become fatalities. Informationally, this implies that over a 

reasonably long period of time, a known set of unsafe acts 

contains information about the smaller set of incidents, 

which itself contains information about the even-smaller 

set of accidents, and so on.

This implies that we can learn something about one 

category in the pyramid by studying the category beneath it. For instance, studying incidents should tell us 

something about accidents (Nazeri, Donohue, & Sherry, 

2008). In formal terms, incidents are considered precursors to accidents.

The Devil Is in the Details 

All of this means we have to probe deeply into specific 

details of a given accident to try to see subtle and oftenhidden relations between those details to understand 

that accident. This takes detective work. It requires data 

and methodology. 

In this report, we are going to pursue the logic of the 

Heinrich model and look for features that may discriminate one hazard category or group from another. Thus, 

if we can find such discriminating features, we can start 

understanding these hazardous situations, to whom they 

tend to happen, and how to keep them from happening 

in the future. 
4 

This includes granting of provisional legal immunity to 

pilots who file an incident report, as long as the incident 

involved no criminal activity or accident (ASRS, 2010).9

Over the years, pilots, controllers, and aircraft maintenance personnel, both private and commercial, have 

learned that the ASRS is a non-threatening way to report 

aviation safety issues to personnel in the FAA whose job it 

is to study such issues. This makes ASRS data particularly 

valuable in trying to do proactive safety research. 

The ASRS maintains a small-but-expert analytical staff, 

including data analysts with pilot, air traffic control, and/

or aircraft maintenance experience, plus retrieval specialists who can mine their extensive database for specific 

information. We turned to this staff to engage participants 

who had previously submitted incident reports naming 

weather as a factor.

Participants

During 2005 and early 2006, ASRS staff kept a 

running list of weather-related GA incidents reported 

on their standard intake forms. They telephoned each 

pilot who had experienced a weather-related incident, 

explaining this research study, and requesting the pilot’s 

participation as an aviation professional. Those agreeing to participate received a weather incident follow-up 

instrument developed specifically by one experimenter 

(Lenz) for this study (see Appendix B). The interview 

asked detailed questions about pilot demographics and 

factors that might have contributed to the incident. Each 

packet was sent via overnight delivery with a prepaid return 

envelope to convey a sense of gravity and professional respect. Participants were not financially compensated. One 

hundred twenty-one interviews were sent out before the 

requisite 100 responses were finally obtained, equivalent 

to a response rate of 83%.

Analytical Procedure

The intent behind this analysis was to identify what 

kinds of things go wrong with flights when weather enters 

the picture, to whom those things seem to be happening, how those factors relate to one another, and what, if 

anything, might be done to reduce future risk. 

The analytical procedure started by coding “Yes” responses to interview questions as “1” and “No” responses 

as “0.” This allowed statistical analysis, primarily with 

nonparametric procedures (Hollander & Wolfe, 1999).

Both quantitative and qualitative analysis were applied 

to the data.10 The present report primarily concerns the 

9 The intent here is not to exonerate wrong-doers, but to reward preporting of 

honest mistakes, which provides valuable data that can be analyzed, precisely 

as we are doing here.

10 Quantitative analysis centers on numerical data amenable to statistical 

analysis, whereas qualitative analysis usually centers on non-numerical aspects 

of data such as behaviors or verbal responses.

quantitative data and seeks to identify salient issues involving significant numbers of pilots, either by comparing 

different groups of pilots within this sample to each other, 

or by comparing this sample to other samples or groups 

external to the study (e.g., to nationwide data collected 

by FAA or industry groups).

In selected instances where alternate data sources were 

available, the present findings were contrasted with findings based on those alternate sources. Such cross-checking 

can both strengthen pre-existing knowledge, as well as 

uncover the occasional contradiction, which can lead to 

further investigation and clarification.

Study Limitatio

The current study has at least two limitations. The 

first is a limitation of the data sample itself. While this 

sample represents a nearly complete polling of all ASRS 

GA weather-related incidents reported during a good 

range of summer, fall, and winter months, it contains 

only reported incidents. It is virtually certain that many 

incidents go unreported. For one thing, not all pilots 

are aware of the ASRS. For another, people are almost 

universally averse to admitting mistakes, even when we 

know the proper venue for doing it. As a result, some 

incidents go unreported. The methodological issue is 

whether or not incidents that go unreported are basically 

the same as our sample, which are all reported incidents. 

If not, then our sample will be biased in unknown ways. 

Any systematic sampling bias limits how much we can 

generalize from this sample to the population of pilots 

and events at large. 

Voluntary reporting systems are often suspected of 

bias. Rockwell, Roach, and Giffin used the ASRS database 

in their 1981 report, A study of ASRS reports involving 

general aviation and weather encounters. At that time, they 

perhaps overstated the issue, claiming “Clearly, there is no 

evidence that the data are representative” (p. 40). While 

such candor is laudable, there is little evidence to suggest 

that the data are unrepresentative of GA pilots either. So, 

barring a compelling reason to mistrust these data, we 

can only state the concern and move on.

The second limitation of this study is a universal 

one, nonetheless rarely acknowledged, namely the issue 

of multiple comparisons. We intend to examine a large 

number of statistical results here and, by the very definitions of probability theory, a certain small number of 

those are likely to be “significant” merely by chance. This 

is usually about 5%, but does require acknowledgment.
5

Resu

Preliminary Conclusions

An endemic problem in data-mining studies is that 

results can seem scattered and disjointed because we are 

looking for many results of many sorts. One solution 

to this problem is to lay out brief conclusions first, followed by detailed analysis, finishing with a recap of the 

same conclusions. In technical terms, this cognitively 

primes11 the reader and takes advantage of primacy and 

recency effects.12

 Following that strategy, this report intends to show 

that the following specific factors constituted a problem 

for 5% or more of those pilots. To investigation professionals, this will clearly be a list of “usual suspects,” for 

which we now have increased statistical evidence.

1) Low lighting (dusk or darkness)

2) Type of weather encountered (Table 1)

a) deteriorating visibility (e.g., lowering ceiling, 

clouds, fog, rain, rising cloud tops, merging 

cloud layers)

b) icing 

c) thunderstorms

d) turbulence

11 In cognitive priming, preliminary exposure to a particular stimulus enhances 

(“primes”) the participant’s subsequent sensitivity to later presentation of 

similar stimuli (James, 1890).

12 When presented with lists of material, we tend to remember best what was 

first presented and last presented (Deese & Kaufman, 1957).

3) Multiple weather factors experienced simultaneously 

(Figure 3)

4) Failure to get a preflight weather briefing, or “briefing” with only a low-grade (non-aviation-oriented) 

source (Figure 4, Table 2)

5) General deterioration of weather forecast accuracy

over time (Table 3)

6) Weather that materialized worse than predicted

(Tables 4, 10)

7) Lack of weather-related training and experience for 

both non-instrument-rated and new instrumentrated pilots (Tables 5, 8, 9, Figure 6)

8) Air ambulance missions (particularly helicopter 

ambulance)

9) Aircraft lacking substantial weather information/

handling/avoidance equipment (Figures 5, 7)

10) Non-weather-related, compounding factors (Table 6, 

Figure 8, e.g., decision-making factors, time pressure, “get-home-itis,” aircraft equipment problems, 

fatigue, distraction by passenger or crew)

A detailed summary of the analysis that lead to these 

assertions now follows. Readers wishing to postpone this 

detailed analysis may want to go directly to the Discussion section.

Table 1. Types of weather encountered. 

Lowering ceiling 53 Rising cloud tops 17 Snow showers 8

Flew into clouds or fog 51 Icing 16 Ground fog 5

Deteriorating weather ahead 38 Turbulence 12 Strong cross winds 3

Reduced visibility 38 Merging cloud layers 11 Unknown, but IMC conditions 2

Broken or solid undercast 25 Other 10 Hail 0

Rain 19 Thunderstorms 9

Figure 3. Number of different weather types encountered per incident. 
6 

Table 2. Frequency counts of current report’s weather-use data compared to baseline usage. 

Weather information source Acronym Current 

usage

Baseline

usage*

Pilots who used only 1 

source (n=32)** 

(Automated) Flight Service Station FSS 55 55 16

Direct User Access Terminal System DUATS 33 37 2

Nat’l Oceanic & Atmospheric Admin/Nat’l Weather Service NOAA/NWS 29 41 2 

Commercial vendors 22 23 3

The Weather Channel TWC 21 22 2

En-route Flight Advisory Service Flight Watch 17 24 0

Pilot Reports PIREPs 11 18 0

Pilots’ Automatic Telephone Wx Answering Service PATWAS 6 0 0

Transcribed Wx Broadcast (automated FSS telephone brief) TWEB 4 0 2 

Hazardous In-Flight Wx Advisory Service HIWAS 2 11 0

Mean sources used per-capita 2.0 2.3 1.0

Correlation of this report with baseline = .91 (p<.001, 2-tailed, Spearman's rho), with single-source pilots, rho = .56 (p=.094) 

* From Knecht (2005), adjusted for the different number of pilots in that study, to allow comparison with current study. 

**Five single-source pilots reported using “Other” sources (not listed here). 

Figure 4. Number of weather sources consulted across the entire 

(N=100) group. Error bars reflect 1.0 standard error of the mean 

(group standard deviation/sqrt[N]). So, to estimate if one bar is 

significantly different from another, we compare the size of the 

difference between 2 data bars to the height of their error bars. A 

rule of thumb is that a data difference of twice the height of the 

larger error bar represents significance at p < .05. 

Table 3. Forecast accuracy deterioration over time. 

Actual “same-as-forecast” wx values v. expected values 

Actual Expecte d

Departure 66 49.33

En-route 45 49.33

Destination 37 49.33

Column totals 148 148

Χ2 p < .011 

Table 4. Overall forecast accuracy. 

Actual weather, as compared to predicted (expected values in parentheses) 

Better than Same as Worse than Row totals 

Departure 8 (13.5) 66 (66) 19 (13.5) 93

En-route 5 (20.0) 45 (45) 35 (20.0) 85

Destination 10 (24.0) 37 (37) 38 (24.0) 85

Column totals 23 148 92 N=263

Χ2 p < .00001 
7

Supporting Analysis—Whole-Gr

ASRS Report

A detailed report of the data with summary analysis 

is available on-line from ASRS. This is entitled General 

aviation weather encounters (ASRS, 2007). Readers interested in getting the broadest possible view of these data 

are advised to consult that report. It is not the purpose of 

the present report to simply duplicate the ASRS findings 

but, rather, to expand on them.

Nature of the Weather Encountered

Lighting. The current data support the notion that 

low light is a hazard to GA flight, as reflected by the 

rate of incidents. By our tally, 79% percent of these incidents occurred during daylight, 4% at dusk, and 17% 

at night.13 If we plausibly combine our dusk and night 

categories to create a “low-light” category, this 4+17 = 

21% is significantly greater than the 13% normal, uneventful night flights reported by the FAA (2006, Table 

4.1) General aviation and air taxi activity survey (p = .024, 

odds-ratio, 2-tailed).

Supporting Heinrich’s notion (1959) that incidents

inform us about accidents, night flight also dramatically 

increases the risk of accidents. According to an NTSB 

(2005, p 21) survey of 72 weather-related GA accidents, 

about 41% occurred at night and 59% during daylight 

(p = 1.53E-10, odds-ratio, 2-tailed).

Oddly, our low-light data are also significantly greater 

than the Rockwell et al. (1981) ASRS data, which reported 

just 12 of 177 (7%) GA incidents being at night (p < 

.001, odds-ratio, 2-tailed). Whether this reflects a reliable 

increase in the rate of night incidents from 1981 to 2007 

is highly questionable, though. Two time samples alone 

cannot tell us enough about normal year-to-year variation.

Types of weatherencountered. Next, Table 1 summarizes 

the types of weather these 100 pilots encountered.

Examining Table 1, what strikes us is the large number 

of pilots who encountered multiple weather types during 

their single incident, as Figure 3 shows.

Only 15 pilots reported encountering a single weather 

type during their incident. The mode and median were 

both 3, meaning most pilots encountered multiple weather 

types in these incidents.14 This seems logical; the more kinds 

of danger we face, the more likely we are to have a negative 

outcome.

13 Our tallies correlate very highly with the NASA ASRS report. Any small 

discrepancies are merely due to slightly different methods of categorizing 

certain kinds of data.

14 The mode is the most-common response. The median is the number at 

which 50% of respondents fall above, 50% below. The mean is the numerical average. When responses are smoothly and evenly distributed around the 

average, the mean is preferred. Otherwise, the mode and median are better 

at conveying what the “average response” looks like.

Information Quantity and Quality in Weather 

Information Briefings

Adequacy of preflight briefing. Probably the simplest 

explanation we could suggest for any incident would be 

if the pilot had just failed to get adequate preflight and/

or in-flight weather briefings. Therefore, we examined 

these pilots’ weather-source usage for any such evidence.

Only 1 of 100 pilots reported not requesting a preflight 

briefing. Another said he had tried but was unsuccessful. Superficially, this seems like most pilots requested a 

weather briefing. However, a crosscheck with other data 

categories eventually revealed 3 more pilots reporting no 

weather source, making a total of 5 pilots strongly suspected 

of not getting an adequate preflight briefing.

Looking deeper, Figure 4 shows the number of weather 

sources reportedly used by pilots on the day of their 

incident. 

Nearly one-third (32/100) of the pilots reported using 

only a single preflight information source. This could have 

been a problem, particularly for a cross-country flight—if 

that one source were informationally sparse or outdated. 

Keep in mind that a “source” such as the Flight Service 

Station potentially provides far more information than 

one such as AWOS. Not all sole-source briefings are of 

equal quality.

To examine this more deeply, Table 2 compares the 

current preflight and in-flight weather information source 

usage patterns to a previous study of 221 GA pilots 

who had not had a recent incident or accident (Knecht, 

2008a). By comparing incident pilots to non-incident 

pilots, differences might be seen.

In Table 2, the column labeled “Current usage” lists 

total numbers of pilots in the current study who reported 

having used the weather information source listed in 

each row. Note that the numbers reported can exceed 

the number of pilots, since each pilot may use more 

than one source. The “Baseline usage” column shows 

corresponding usage from Knecht (2008a). These first 

two columns can therefore be compared. Likewise, the 

final column shows usage only for the 32 current-study 

pilots who reported using just one information source. 

As stated, these could represent a problem, if that one 

information source were relatively low-quality.

In Table 2, a strong correlation between “Current usage” and “Baseline usage” would suggest that, as a group, 

the current pilots used numbers and kinds of weather 

information sources significantly similar to ones typically 

used by other pilots. Indeed, this is the case (Spearman’s 

rho (r) = .91, pr = .0002, 2-tailed). Consequently, at the 

group level, there is nothing to suggest abnormality in 

the current pilots’ choices of weather information sources.

Single-source briefees. Now, let us examine the “1-source” 

column, which represents the potentially more prob-
8 

lematic sub-group of 32 pilots who used only a single 

briefing source. First, 16 pilots did use an FSS briefing 

and 2+2+3=7 more used DUATS, NWS, or commercial 

vendors—all high-quality sources. For those 23 there was 

no apparent problem. 

Nonetheless, as a sub-group, these single-source pilots 

had a usage pattern suspiciously dissimilar to our baseline 

(r = .56, pr = .094, 2-tailed, NS). Of the 32-23=9 remaining pilots, two reported obtaining no briefing, plus one 

said he briefed, but failed to mention the source, plus two 

more “briefed” from The Weather Channel, which is a 

citizen-oriented source, not an aviation source. This left 

a total of five pilots who arguably received an inadequate 

preflight briefing.

Forecast accuracy. Next, we checked forecast accuracy

because weather that materializes worse than forecast 

can certainly create problems. 

Table 3 addresses both the issue of average forecast 

quality and the issue of how weather information quality 

deteriorates over time. First, the low average predictive 

accuracy expected by chance (49.33%) implies that the 

actual number of correct forecasts for the entire group 

was low to start with.15

Second, the statistical comparison between actual and 

expected (chance) values (pC2 < .011) shows that average

forecast accuracy did, indeed, deteriorate over time.16 The 

“Expected” column shows values we would conservatively 

expect—if the column average of 148/3 = 49.33 were a 

correct estimate of overall prediction accuracy, and one 

which never deteriorated over time. Comparing the actual 

values to those expected, we can see how the values 66, 

45, 37 represent information deteriorating significantly 

over time.

Table 4 addresses forecast accuracy in a slightly different way.17 If we assume that, by pure chance, an equal 

number of forecasts should turn out better-than-predicted 

versus worse-than-predicted, then we see that significantly more of these forecasts turned out worse than expected

(pC2 < .00001).18

15 It is important to state that absolutely no aspersions are being cast on these 

specific weather reports or any provider who supplied them. Remember that 

our data sample is biased. We purposely selected a sample of weather incidents, 

not typical flights. Therefore, these reports imply nothing negative about 

normal U.S. forecast accuracy.

16 We must keep in mind that, on average, all weather forecast accuracy decreases with time because weather is technically chaotic, therefore exquisitely 

dependent on initial conditions, modeling assumptions, and even rounding 

error during execution of computer modeling (Gleick, 1987).

17 Tables 3 and 4 reflect a small (but not problematic) number of missing 

values (some pilots did not answer all the questions).

18 To illustrate Table 4’s expected values for, say, departure weather, (8+19)/2 

= 13.5. This is an unconventional way of calculating the expected frequencies 

compared to the regular chi-square (C2) method. However, conventional C2

still produces the same conclusion, pC2 = .002 (C2 is always 2-tailed).

Pilot Weather Training, Experience, and Expertise

Pilots’ training and experience are important factors 

to explore, particularly instrument rating, general flight 

experience, and specific weather experience. In Table 5, 

the rows labeled “Median” show that most of these pilots 

had little experience flying in adverse weather, certainly in 

comparison to full-time professional pilots.19

Unfortunately, Table 5 reflects considerable missing 

data. Pilots seemed to have relatively little trouble remembering their total instrument hours. Yet, simulator hours 

and instrument approaches were a different story. It is 

unclear whether some pilots omitted answers for lack of 

a clear memory (or records), or whether the intent was 

to purposely withhold potentially self-incriminating 

information. 

Therefore, Table 5 shows the data in two ways, both 

as means and medians based on actual data20 (columns 

labeled “Actual”) and also where zeros were substituted 

for missing values (columns labeled “Zeros”). Because of 

the inherent uncertainty of these data and because a small 

number of very high-time pilots inflated some means, 

medians should be considered the more informative and 

reliable measures of central tendency.

With that caveat, Table 5 bears out the previous assertion that most of these pilots had little experience 

with weather flying. Median instrument hours for even 

the instrument-rated pilots lay between just 42-48, with 

only two median instrument approaches within the past 

90 days.

Oddly, these findings partially contradict the earlier 

Rockwell et al. (1981, p. 49) analysis of ASRS data, 

which concluded, “In general, the analysis of weatherrelated incidents involving GA did not indicate glaring 

deficiencies in skill or training of the GA pilot.” We 

would not call the current pilots “glaringly deficient,” 

of course—simply “relatively inexperienced.” For the 

moment, we have no firm idea why our results differed 

with Rockwell’s. Their conclusion seemed to be based 

mainly on their median non-instrument-rated pilot 

having about 500 overall flight hours and 75% of their 

instrument-rated pilots having more than 1500 overall 

flight hours. Instrument flight-related hours were not 

presented. Perhaps, in 1981, knowledge about the ASRS 

and its purpose was less widespread, resulting in a bias 

for mainly seasoned pilots to report incidents.

19 If medians of 0 seem odd, recall that the median is the “balance point” of 

a distribution, the point at which half the cases fall below and half above. In 

the case of continuously varying quantities such as time, a median can take a 

continuous (non-integer) value. But in the case of discrete quantities such as 

instrument approaches, the median often takes an integer value. In that case, 

given a distribution comprised mainly of 0s, the median will be 0.

20 In which case, means were based only upon the numbers of pilots acually 

reporting data.
9

In Table 5, note that 73 of the current 100 ASRS pilots 

were instrument rated (“n=73”). This is higher than the 

U.S. GA national average of 51% (GAMA, 2005). In 

absolute numbers, IR pilots are represented here nearly 

3 times as often as non-IR pilots (73/26 = 2.81). This 

might tempt us to conclude that IR pilots are more atrisk than non-IR pilots (pC2 < .001). However, a simpler 

alternative is, again, that instrument rated pilots may be 

merely more likely to be familiar with the ASRS, hence 

more likely to fill out a report.

One aspect of pilot experience that these data, unfortunately, do not address is the possibility that some 

of these pilots may have successfully taken considerable 

risks with weather in the past. Behaviorally, to arrive at 

one’s destination constitutes a direct reward (positive reinforcement), while avoidance of damage to oneself or one’s 

aircraft constitutes an indirect negative reinforcement.21

Such a combination of reinforcers represents a theoretical 

encouragement to take risks—if a pilot had been successful at it recently. Unfortunately, it is difficult to get any 

person to admit, in writing, to past risky behavior, let 

alone a pilot who may fear the loss of his/her certification.

Equipment Factors

Who flies what? Certainly, the kinds of equipment 

available to a pilot influences that pilot’s ability to deal 

with adverse weather. Here, 92 of 100 pilots shared the 

disadvantage of flying relatively lightweight, highly weathersusceptible aircraft. Seventy-nine were flying light, singleengine aircraft, six were in light twins, six in helicopters, 

and one flew a glider (of the remaining eight, three were 

corporate jets, five were turboprops).

21 Negative reinforcement is not punishment, as commonly thought. It is “relief 

from ongoing punishment.” So, technically, cessation of the risk-taking pilot’s 

anxiety would be the negative reinforcer in this paradigm (Macintosh, 1974).

Helicopters are overrepresented in these data. The 

General Aviation Manufacturers Association (GAMA, 

2005) reports that 1.6% of U.S. pilots are helicopterrated, versus 6% of this sample (p < .001, odds-ratio). 

The explanation may be simple, however: Helicopters 

are the vehicle of choice in air-ambulance work, which 

is largely in a class by itself regarding pilot and mission 

motivation. Mission difficulty can be high in remote or 

unimproved areas, and response time is almost always a 

factor. Ambulance pilots are highly motivated to fly, even 

under adverse conditions. Consequently, they can be 

expected to incur more than their share of bad-weather 

encounters. They may also be more motivated to report 

incidents, due to their status as commercial carriers.22

We next examined the relation between level of pilot 

training and quality of equipment. Intuition suggests that 

the more experienced pilots probably fly the more capable 

aircraft. Conversely, we expect the less experienced pilots 

to fly the less capable aircraft, which may be a problem. 

Figure 5 graphically depicts the significant relations 

between human and equipment categories found in these 

data. Because an airplane is a “package of components,” 

these human-equipment interrelations form a web of 

Spearman r (rho,

23 nonparametric) correlations.24 Only 

correlations significant at p < .001 (2-tailed) are shown, 

22 Recall that limited legal immunity is granted to pilots who file an incident 

report with ASRS, as long as no criminal activity or accident occurs.

23 Do not confuse the Greek symbol r (rho) with the letter p, which is used 

for “probability” (e.g., p-value). 24 These correlations were derived by simply summing, for each pilot, the 

“Equipped” scores within each major block in Section D of the interview. One 

point was granted for each box checked, then the number of points summed 

to give that pilot’s score for that block. Pilot ratings were coded as 2 points 

for ATP, 1 point for non-ATP IR, 0 points for non-IR. Airframes were rated 

by the categories shown in Appendix C. Because this resulted in different 

maxima and minima for different factors, raw numbers were normalized to 

“min=0, max=10” scores before computing the correlations.

Table 5. Pilot flight hours. 

Instrument h Simulated underthe-hood h Simulator h Instrument

approaches—12 mo 

Instrument

approaches—90 d

“Actual” is observed # of pilots 

answering. “Zeros ” are values 

of 0 substituted for missing data. Actual Zeros Actual Zeros Actual Zeros Actual Zeros Actual Zeros

nbasis* 23 27 24 27 13 27 6 27 4 27 

Mean 9.4 8.0 16.3 14.5 13.1 6.3 2.2 0.5 3.7 0.4 

Median 0 0 10 10 6 0 1.5 0 0 0 

Minimum 0 0 0 0 0 0 0 0 1 0 

Non-instrumentrated (nmax=27)

Maximum 120 120 55 55 70 70 5 5 5 5 

nbasis 71 73 72 73 61 73 57 73 47 73 

Mean 647.4 629.7 102.2 100.8 85.8 71.7 18.0 14.1 7.1 4.4 

Median 48 42 71 70 25 25 8 7 2 2 

Minimum 0 0 0 0 0 0 0 0 1 0 

Instrument-rated 

(nmax =73) 

Maximum 10000 10000 700 700 1000 1000 150 150 45 45 

nbasis 94 100 96 100 74 100 63 100 51 100 

Mean 491.3 461.9 80.7 77.5 73.0 54.1 16.5 10.4 6.5 3.3 

Median 27 23 55 53 25 11 7 3 3 0 

Minimum 0 0 0 0 0 0 0 0 0 0 

Combined (Nmax

=100)

Maximum 10000 10000 700 700 1000 1000 150 150 45 45 

 *nmax is the maximum possible number of IR or non-IR pilots. However, not all pilots answered every question. Therefore, nbasis

is the actual number of pilots who did—the basis of each metric—which may reflect missing data and be less than nmax.
10 

except for those marked with an asterisk * (p <.01, 

2-tailed).25

In Figure 5, “Pilot rating” means either Air Transport 

Pilot (ATP), non-ATP-instrument-rated (non-ATP IR), 

or non-instrument rated (non-IR). Note that the higher 

the pilot rating, the better the equipment generally is. 

Communication Equipment shows no significant correlations with other factors because all aircraft started 

out with functioning communication gear, and the 

questionnaire did not make particularly fine distinctions 

between different types.

Effect sizes. Here, statistical significance is half of the 

issue. Significance embodies reliability, meaning repeatability. The other half is effect size (r2

), which estimates 

how much of the underlying variation in scores is explained 

by the relation itself (e.g., .5732

 = .328 ≈ 33% of the total 

score variation explained). The r2

 (not shown) here all 

imply that, while all these relations are reliable, they are 

modest in strength.

In short, Figure 5 shows a modest-but-reliable tendency 

for the higher the pilot’s rating, the more likely he or she 

is to fly a more expensive, better-equipped aircraft. Finally, 

note that the presence of ATPs here demonstrates that 

even the best-trained pilots, flying superior equipment, 

can still wind up having occasional trouble with weather. 

25 Note that the sheer amount of communication equipment pilots have typically has little or no relation to anything else examined here, which explains 

why there are no links to that factor.

Non-Weather-Related Factors 

Sixty-three percent of pilots reported additional “factors other than weather that contributed to the incident.” 

These came in all shapes and sizes. Table 6 briefly summarizes.

These are all recurring themes familiar to accident 

investigators. The concern about “Decision making,” as 

stated earlier, is that this is a very broad category and one 

that does not suggest specific remedies. 

Time pressure is certainly a perennial issue. The ASRS 

(2007) report offers more detail than we shall cover here. 

However, we can add that a prior study of pilots who had 

not been involved in any sort of incident (Knecht, 2008b) 

revealed that at least 48 of 221 (22%) experienced external social or business-related pressures to fly in marginal 

weather at some point in their careers. 

The rest of the factors in Table 6, although less prevalent 

here, are also well-known. Many of these “Other”-category 

responses are captured in the textual responses to Section 

F of the interview (Appendix B).

Recovery Strategies

As a group, these pilots seemed to prefer some recovery 

strategies over others. Table 7 shows the most popular. 

These are rank-ordered by frequency of occurrence (note 

that some pilots used more than one recovery strategy, 

so that the sum of the “Totals” row will exceed 100).

Figure 5. Correlations between major equipment types (data derived from 

Appendix B, section D). 

Table 6. Non-weather factors contributing to the incident. 

Factor N

Decision making 26

Other 23

Time pressure 21

"Get-home-itis" 9

Aircraft equipment problem 8

Fatigue 7

Distraction by passenger or flight crew 5

Illness 3

Company or FBO policy 0
11

Overall, there are significant differences between categories here (pC2 = .004). Yet, it is difficult to say exactly 

which categories differ significantly from which other 

categories because there are so many possible comparisons. 

The “Other” category is a potpourri, not a unitary entity, 

so landing at destination was actually the second-highest 

ranked category, and so forth. 

We will return to the topic recovery strategies later, after 

we have a better grasp of how best to organize the data.

Summary of Whole-Group Analy

Preliminary Statistical Summary

At this point, let us pause briefly to summarize the 

whole-group results for these N=100 pilots reporting 

these ASRS weather-related incidents. Reported “themes” 

could be characterized as:

1) Darkness

2) Moisture affecting visibility (clouds, fog, rain, snow) 

and/or air movement affecting aircraft handling 

(thunderstorm, icing, turbulence)

3) Multiple weather factors experienced simultaneously 

4) Inadequate preflight weather briefing

5) Deterioration of weather forecast accuracy over time

6) Weather that materialized worse than predicted (this 

implicitly includes lack of en-route forecast updates)

7) Lack of weather-related training and experience (noninstrument-rated and new instrument-rated pilots)

8) Air ambulance missions (particularly helicopter 

ambulance)

9) Aircraft lacking substantial weather information/

handling/avoidance equipment

10) Non-weather-related, compounding factors (e.g., time 

pressure, “get-home-itis,” fatigue)

Parsing the Group Data on the Basis of This 

Preliminary Analysis

The above look at the whole-group data was certainly 

useful. Next, it was logical to wonder if there were some 

way to sub-divide the data into some simple, logical categories, after which additional themes and issues might 

become easier to see. 

This approach follows a certain logic. For instance, 

Burian (2001) reported that ATPs and certified flight instructors (CFIs) differ significantly from non-instrumentrated (private) pilots in weather knowledge. That kind of 

distinction is useful as a heuristic in accident investigation 

because it can help guide an investigator in looking for 

specific details during a given accident. Knowing whether 

the pilot were, say, an ATP might prompt us to ask certain 

questions we might otherwise avoid, since ATPs are likely 

to be highly trained and experienced.

Parsing the data by pilot rating. The idea of sub-dividing 

our data by pilot rating was first supported by a hierarchical cluster analysis (HCA).26 Normally, HCA is used with 

continuously varying (as opposed to discrete, e.g., integer) 

data. However, Finch (2005) suggested that HCA can be 

acceptably per-formed on binary data (the yes/no kind 

we have here). HCA calculates a “mathematical distance” 

between patterns of data, thereby sorting individual cases 

into dissimilar groups composed of similar cases. 

Detailed discussion of the HCA will be omitted here 

for reasons of brevity. However, HCA strongly suggested 

dividing the data into the following three main groups, 

based on training and experience.27

1. air transport pilot—ATP

2. non-ATP instrument-rated pilotsnon—ATP IR

3. non-instrument-rated pilotsnon—IR

That stated, we can detail major characteristics of 

each group, as supported by the data, beginning with 

the non-IR group.

Non-Instrument-Rated Pilot

Weather Training and Experience

When it comes to non-IR pilots and weather, two obvious candidate problems are lack of training and experience. 

As previously stated, Rockwell et al. (1981) implied that 

these were not a factor in their data. However, Table 5 

has already challenged that conclusion. Figure 6 gives a 

26Cluster analysis is an approach to modeling. It starts with a set of measurements (“variables”) taken on individuals (“cases”—here, individual pilots). It 

then explores the relations between variables by combining individual cases 

into groups (“clusters”). The end goal is to group cases so that those within 

the same cluster are more similar to each other than they are to cases from different clusters. This similarity is operationalized by calculating “mathematical 

distances” between cases and clusters. Once complete, it becomes our job to 

interpret what each cluster means in logical and practical terms.

27 Individuals requiring details of the HCA may directly contact the first 

author of this report.

Table 7. Recovery strategies. Descended to stay below wx Other Landed at destination 

ATC offered assistanceDeviated around wx 180-degree turn Instrument approach Requested IFR clearance from ATC Climbed above wx Broke out on top Landed enroute (other than alternate) Landed at alternate 

Totals 34 25 23 21 20 19 16 14 13 12 12 11

Rank of totals 1 2 3 4 5 6 7 8 9 10 10 11
12 

more detailed look at non-IR pilots’ actual instrument 

flight hours.28

Unsurprisingly, the median non-IR pilot had zero 

instrument hours and zero instrument approaches. Of 

the 27 non-IR pilots, 12 said they were currently working to obtain an instrument rating. However, at the time 

of their incident, 13 said they had no actual instrument 

hours. Four failed to answer the question, so there were 

perhaps as many as 17 with no actual instrument hours. 

Only 7 pilots had even 8 h or more. Granted, “no instrument hours” does not necessarily mean “no weather 

experience.” But, we can only analyze the data at hand.

These non-IR pilots arguably constitute a major target 

group—over 20% of our total 100 incidents. By definition, non-IR pilots are supposed to see and avoid weather. 

With a limited “license to learn,” other than from written 

and video training materials or when accompanied by an 

instructor or IR copilot, how can weather training and 

experience logically not be a relevant issue when these 

pilots find themselves caught in adverse weather?

One aspect of risk-taking has to do with whether a 

relatively inexperienced person will engage in a given 

behavior, knowing full well that there is risk involved. 

In our case, that meant a non-IR pilot taking off into 

forecast marginal visual meteorological conditions 

(MVMC) or worse.

Moreover, here, only nine pilots reported having 

visual meteorological conditions (VMC) forecast for all 

three phases of their flight. That means that two-thirds 

of these non-IR pilots must have known that they were taking some risk.

28 A non-IR pilot can accrue flight hours as a student or when accompanied 

by an IR pilot.

Equipment

Non-IR pilots tended to fly the least weather-capable 

aircraft. Three categories of equipment that make an 

enormous difference in weather-handling ability are (a) 

weather-avoidance electronics (e.g., on-board weather 

radar, NEXRAD), (b) physical de-icing and anti-icing 

systems for lift, control, and thrust maintenance, and (c) 

attitude-stabilization equipment (autopilot, wing leveler). 

Of the 27 non-IR pilots,

• 89% had no major weather-avoidance electronics 

(weather radar, lightning detector/Stormscope, and 

so forth).

• 89% had no de-ice, anti-ice, or other related physical 

systems. 

• 56% had no autopilot or wingleveler.

This pattern differs significantly from the other two 

pilot groups we will be discussing, mainly in weatheravoidance electronics. Eighty-nine percent of non-IR 

pilots lacked weather-avoidance electronics, versus 50% 

of ATPs, and 62% of non-ATP IRs (pC2 = .012). There 

was a similar trend29 in anti-icing systems (89% non-IR 

lacking them, vs. 61% ATP, vs. 71% non-ATP IR, pC2

= .085).

Figure 7 shows how many pilots had 0, 1, 2, or all 3 

equipment categories missing. Of course, the final subgroup is the least equipped to handle serious weather. 

Unfortunately, it is also the largest (n=14). 

No non-IR pilot reported being completely without 

communication equipment. One pilot did report having 

no navigation equipment. 

29 A statistical “trend” is generally defined as one where .05 < p < .10.

0

2

4

6

8

10

12

14

16

18

0 2 4 8 16 32 64 128

Non-IR pilots, instrument flight hours

Number of pilots

4 pilots had no data

Figure 6. Numbers of non-instrument-rated pilots (vertical axis, linear 

scale), grouped by instrument flight hours (horizontal axis, logarithmic 

base-2 scale). 
13

Non-Weather Factors

Non-weather factors exacerbated weather factors. As we 

saw in Table 6 earlier, we needed a miscellaneous “Other” 

category to hold explanatory factors not fitting neatly 

under other categories. We expected there to be factors 

unrelated to weather that, nonetheless, helped to cause 

each incident.

Figure 8 shows frequency distributions for the 27 

non-IR pilots concerning non-weather-related factors 

listed as influencing their incident.

Nine pilots said that such factors were not a consideration, while 18 (67%) said they were. Of the 18, 13 

blamed a single factor, 4 blamed 2 factors, and a single 

individual blamed 3 factors. 

Looking deeper, the two non-weather-related factors 

most commonly blamed by non-IR pilots were “decision 

making” (44%) and “time pressure” (22%). Additionally, 

of the sub-group of 5 pilots reporting 2 or more factors, 

4 reported both decision-making and time pressure. This 

naturally leads us to speculate that time pressure and 

decision errors are related. There is certainly a large body 

of literature in psychology to support that idea (Orasanu 

& Martin, 1998).

Crosscheck With “Stories” 

Section F of the ASRS interview allowed pilots to tell, 

in their own words, why the incident occurred. These 

“stories” should at least be consistent with the statistical 

data.

Straight to the point, little in these stories directly 

contradicted our main points. The major trouble with 

the stories themselves was the large number of them (Appendix D contains a synopsis). We needed a systematic 

way to organize them. One sensible way seemed to be 

by mission category, hence, that is how Appendix D is 

organized.

Summary—Non-Instrument-Rated Pilots

• Least training and experience

• Least-capable equipment

• Tended to fly despite bad weather forecast

Figure 7. Numbers of non-IR pilots (vertical axis) lacking one or more 

categories (horizontal axis) of either weather-avoidance electronics, de-icing 

capability, and/or autopilot/winglever. 

Figure 8. Numbers of non-IR pilots (vertical axis) reporting the 

number of non-weather-related factors (horizontal axis) that 

contributed to their incident. 
14 

Instrument-Rated Pilot

In contrast with the 27 non-instrument-rated pilots, 

the 73 instrument-rated pilots seemed almost confusingly 

diverse, especially when one looked at their stories (Appendix D). A way was needed to organize this diverse group 

into more meaningful sub-groups. As stated previously, 

analysis and logic suggested dividing the instrument-rated 

pilots into two sub-categories: the 18 who held the ATP 

certificate versus the 55 who did not.

Air Transport Pilots

As a group, air transport pilots have the highest level of 

training and experience of any major certificate available 

to civilian airmen and are the group most likely to be 

working as professional pilots. Of these 18 ATPs, 8 were 

flying as private individuals at the time of the incident. 

The other 10 were on the job, 4 flying under corporate 

auspices, 3 more as air taxi, and 3 as charter.

Table 8 provides more statistical support for why we 

should formally distinguish between ATP and non-ATP 

IR pilots.30

These ATPs reported 12 times higher median flight 

hours than the remaining instrument-rated pilots (11000 

v. 900 h, pU =.0000001). Instrument hours are also significantly different (pU =.004). At first glance, the picture 

may seem different for 12-month (12m) and 90-day (90d) 

recency of experience in instrument approaches; no reliable differences are evident. But, unlike flight hours and 

instrument hours, the recency data are contaminated by 

large numbers of missing values. If all pilots had reported, 

the results may have been more consistent.31

As we saw in Figure 5, advanced-certificate pilots tended to 

fly more capable aircraft. We shall return to this point later.

One point of interest here is whether ATPs may 

be overrepresented or underrepresented in these data, 

compared to the national pilot population. The answer 

is that 18% of our pilots were ATPs versus about 23% 

of U.S. pilots (GAMA, 2005). This is not a significant 

difference (pC

2

 = .21, NS). 

30 Our group means were skewed by small numbers of high-hour pilots, so 

we compared groups with nonparametric Mann-Whitney U-tests. The terms 

nATP or nnon-ATP refer to the number of pilots providing data in each respective 

category. (Missing) refers to the number who failed to provide data in that 

category (therefore, all nATP cells sum to 18 in the second-to-last column and 

all nnon-ATP cells sum to 55 in the last column). 31 If we redo Table 8’s “past-12m” analysis substituting zeros for all missing 

values, we arrive at a trend of .076 in favor of ATPs. 

However, we need to keep two more things in 

mind. First, ATPs are arguably the group most likely 

to be familiar with the ASRS, thus to file incident 

reports. If correct, that would suggest that their true 

population rate of incidents may actually be less than 

other groups. Second, given that ATPs typically fly 

many more miles than other pilots, their effective

“incidents-per-mile-flown” rate may actually be quite 

small compared to non-ATPs. While this logic is 

speculative, it would be worthwhile to pursue at a 

future date because it speaks to the effectiveness of 

training in reducing risks.

Now, what more in our data distinguishes ATPs 

from other instrument-rated pilots?

Table 9 shows significant differences between ATPs 

and non-ATP IR pilots found in these data. These 

are presented as sample means (x̄), mean differences 

(deltas = Dx̄ = x̄

atp – x̄non-ATP IR), and selected p-values 

(Fisher’s exact test, 2-tailed). Table 9 shows every 

question on the interview where |Dx̄|>.25. A few of 

these differences seem too complex to interpret, but 

the following points emerged as logical candidate 

differences between ATPs and non-ATP IR pilots:

1. More ATPs tried to deviate around adverse 

weather to avoid it (pFisher’s 1-tailed = .045).32

2. More ATPs used GPS and Weather Radar, and 

at least had Terrain Warning System.

3. More non-ATP IRs may have felt that the average 

en route and destination weather they had encountered in the past during most instrument flights was 

better than forecast.

Point 1 addresses a successful weather strategy used 

by top pilots. They are able to deviate around weather. 

Point 2 addresses equipage. The more weathercapable the aircraft, the better—as long as this does not 

encourage the pilot to take on additional risk.

Point 3 addresses a possible attitude toward 

risk acceptance. While speculative because of 

marginal significances (.053, .084), it may be that 

pilots who routinely encounter better-than-forecast 

weather recalibrate their expectancy of success upward, and wind up eventually getting caught for 

it, as evidenced by this incident. Such a Bayesian 

32 Use of the 1-tailed test is justified on the a priori assumption that ATPs 

would try more often to deviate around weather.

Table 8. ATP versus non-ATP instrument-rated pilots—flight experience. 

Category (median values) ATP non-ATP IR pU, 2-tailed nATP (Missing) nnon-ATP IR (Missing) 

Flight hours 11000 900 .0000001 17 (1) 54 (1) 

Instrument hours 255 35 .004 18 (0) 53 (2) 

Instr. approaches, past 12m 9 8 .992 (NS) 13 (5) 42 (13) 

Instr. approaches, past 90d 3 3 .828 (NS) 14 (4) 32 (23) 
15

“prior-history model”33 may help explain “optimism 

bias,” wherein pilots overestimate their own skill 

(O’Hare, 1990, Goh & Wiegmann, 2001, Wilson 

& Fallshore, 2001).

Most of this looks as if ATPs are the safer IR pilots. 

The one exception has to do with thunderstorm encounters. Four of 18 ATPs reported close encounters 

with thunderstorms, versus just 1 of 55 non-ATP IRs. 

This difference (pFisher’s exact< .012, 2-tailed) is significant, 

statistically and implicationally. This may suggest that, 

when it comes to extremely deadly weather, some ATPs 

may be “selectively overconfident,” although we need to 

interpret this cautiously.

Summary—ATPs

• Highest level of training and experience.

• Most-capable equipment.

• Most likely to deviate around weather and most successful at doing so.

• However, most likely to have a negative encounter 

with thunderstorms.

Non-ATP IR Pilots

This third major expertise group is the most difficult 

to characterize. Whereas the non-IR pilots typically 

have the least training, the least experience, and the least 

sophisticated equipment, and the ATPs tend to have the 

most of those, the 55 non-ATP IR pilots are “neither fish 

nor fowl.” They have great diversity within their ranks 

and perhaps require the deepest scrutiny to understand 

and characterize.

33 Bayesian models form “If-Then” tree structures, each with associated 

probabilities-of-occurrence. They assume that prior success or failure probabilities influence current behavior.

One technique we can use with our data is to threshold

them, meaning to filter the group averages to pull out the 

very smallest and very largest numbers.34 This highlights 

extremes, which are theoretically categories of maximum 

information.

To complement thresholding, we will also consider each 

interview question separately, pulling out ones we know 

from either logic or prior analysis to be interesting, no 

matter what their value. While this is time-consuming, 

it is necessary in order to be thorough.

The first part of the callback interview (Appendix B, 

Section A) concerned flight planning and weather briefing. When we threshold the Section A group averages, 

what survives the filter is that all 55 non-ATP IR pilots 

reportedly tried to obtain a pre-flight weather briefing, 

and 32 (58%) briefed with FSS. Additionally, all pilots 

who used The Weather Channel also used at least one 

other source to brief. None of this indicates any particular 

problem with preflight briefing. 

Looking deeper, the preflight forecasts showed 5%, 

15%, and 13% of departure, en route, and destination 

forecast as IMC, respectively. This constitutes forecasted 

risk. However, these are IR pilots, so IMC is no prohibitor by itself. 

Looking deeper still, we see that two pilots had IMC 

forecast for all three phases of flight (departure, en route, 

and destination). Additionally, five had thunderstorms 

predicted for one phase, and one had them predicted for 

two phases. Yet, again, thunderstorm predictions, alone, do 

not constitute automatic refusal for non-ATP IR pilots. 

Again, what they constitute is forecasted risk.

34 The lower bound for thresholding used here was “all questions with a group 

average of 10% or less.” The upper bound was “all questions with 50% or 

greater.” Be apprised that these are arbitrary values based mainly on logic, 

not expected p-values.

Table 9. ATP versus non-ATP instrument-rated pilots—other distinguishing features. Deviating from planned route during incident? Encountered deteriorating wx ahead Encounter consequences = "Other" Recovered by deviating around wx Had & used Dual VHF transceiver Used GPS Had Terrain Warning System Used Weather Radar Actual enroute wx on most instrument flights better than forecast Actual destination wx on most instr flts better than forecast 

N Section B C D E

x̄atp 18 0.67 0.61 0.50 0.44 0.89 0.94 0.39 0.33 0.06 0.17 

x̄non-ATP IR 55 0.40 0.33 0.24 0.16 0.64 0.65 0.05 0.05 0.33 0.45 

Δ x̄ 0.27 0.28 0.26 0.28 0.25 0.29 0.33 0.28 -0.27 -0.29 

p, 2-tailed 0.0611 0.0241 0.0162 0.0012 0.0062 0.0533 0.0843

Subscripts 1, 2 and 3 in the row above refer to list factors 1-3 in the text below. 
16 

Next, we can try to assess “cumulative forecast error.” 

Table 10 shows that 36 non-ATP IR pilots (65%) wound 

up facing worse-than-predicted weather during one or 

more of the three phases of flight (departure, en route, 

or destination). 

Worse-than-predicted weather is a problem. Note how 

nine pilots received a double-dose of misfortune and 

two a triple dose. This reiterates the theme of “bad-luck 

weather” we saw earlier in the whole-group analysis.

Of course, the obverse of bad luck is bad judgment. If 

65% of non-ATP IR pilots could attribute their incident 

at least partly to bad luck, that means the remaining 35% 

logically could not. Assuming that the predicted weather 

was bad enough to warrant at least thinking about cancelling the flight—and this is not unreasonable, given that 

all these flights ended in weather-related incidents—as 

many as one-third of non-ATP IR pilots appeared to know 

they were taking a risk, yet they took it anyway. 

Section B of the callback interview concerned information about the actual weather at the time of the incident, 

plus the use of ATC services. Here, we saw that 67% of 

ATPs tried to deviate around the weather, versus 40% of 

non-ATP IRs (we will return to this point later). Consequently, 53% of non-ATP IRs ended up flying straight 

into clouds or fog, and 51% encountered lowering ceilings (27% encountered both). Fortunately, only a single 

pilot ran into a thunderstorm, which, oddly enough, was 

a better record than the 4 ATPs who did run into one 

(p = .008, odds-ratio). 

All this suggests that non-ATP IR pilots may be overly 

bold about intermediate-severity weather. But, they may 

actually do better than ATPs when it comes to taking 

severe weather seriously.

Sixty-two percent of non-ATP IRs said they attempted 

to obtain en route ATC assistance. This may not reflect 

a problem since the figure is about the same as ATPs 

(67%), and both are higher than non-IR pilots (52%). If 

it does reflect a problem, then all GA pilots are probably 

about equally involved.

Summary— Non-ATP IR Pilots

• May be susceptible to “prior history effect,” where 

pilots who routinely encounter better-than-forecast 

weather recalibrate their expectancy of success upward 

and wind up eventually getting caught.

• Tend to be overconfident in the face of intermediateseverity weather.

• More likely to fly straight into adverse weather, rather 

than deviate around it.

Group Pattern Differenc

Now that we have learned to divide our pilots into 

three meaningful groups—(IR-ATP v. IR-non-ATP v. 

non-IR)—another useful statistical technique is to look 

for between-group differences on each separate interview 

question. With the data split in three, each interview 

question can now generate three separate means, one 

per pilot group, and we can examine high or low values, 

question by question. These can be organized into patterns 

representing themes, as Table 11 illustrates.

Using a chi-square (C2

) technique, we can compare 

expected counts (not shown) to actual counts (shown as 

proportions for easier interpretation) and then generate 

a probability estimate pC2 for that pattern of occurrences. 

This lets us filter only those interview items revealing 

statistically significant high or low probabilities-ofoccurrence (p<.05). These cells are highlighted in gray 

to “pop-out” visually.

The pattern differences that emerge in Table 11 are 

organized into themes. Starting with the top half of Table 

11 and moving left-to-right, the first theme is labeled 

“Deception?” The question mark indicates that this is 

speculation. But, its three components suggest that some 

non-IR pilots may be “lying by omission” after mysteriously leaving several other key interview questions blank. 

Assuming that these omissions mean something, the notion 

that the least-experienced group might be prone to selfmisrepresentation is certainly worth some consideration.

Next (unlabeled column), we note a problem discussed 

earlier, namely, ATPs flying into predicted thunderstorms. 

We will rejoin that theme later in the Discussion section.

Next, the“Flightplan” column suggests that non-IR 

pilots tended not to file flight plans. However, this was 

not illegal and may not mean much; the non-filing pilots 

were predominantly on pleasure flights.

Next, the “Weather” column suggests that ATPs 

encountered significantly more ground fog, while nonATP IR pilots encountered more icing (simultaneously 

while lacking ice-capable aircraft). There is no obvious 

explanation in the data for either of these results; it may 

be serendipity.

Turning to unpredicted thunderstorms, we see a standout 

low-incidence group in the non-ATP IR pilots. By default, 

the numbers leave us with a relatively high incidence rate 

for ATPs (22%), which we previously mentioned, plus a 

moderate incidence (15%) for non-IR pilots. And, since 

all four non-IR thunderstorm encounters happened during pleasure flights, this raises a question about non-IR 

decision-making during pleasure flights.

Table 10. Non-ATP IR pilots facing worse-than-predicted weather. 

Phases of flight, actual wx worse than predicted 0 1 2 3

n (total=55) 19 25 9 2
17

Regarding ATC issues (“ATC” column), the 0% requests 

for IFR clearance from non-IR pilots is trivial because 

non-IR pilots are not allowed to request IFR clearance. 

The higher non-ATP IR instrument approach request 

rate probably just reflects their not having filed IFR flight 

plans in the first place.

Regarding the “Consequences” column, it is something 

of a mystery why 15% of non-ATP IR pilots reported 

equipment problems versus 0% for the other two groups. 

Perhaps non-ATP IRs fly older aircraft or have more 

equipment than the non-IRs because they need it, yet 

lack a corporate employer to keep it all in tip-top shape.

Next, regarding the issue of disorientation, it comes as 

no surprise that 33% of the non-IR pilots became unsure 

of where they were during the incident, and 15% experienced loss of control. After all, this is our least-trained, 

least-experienced, poorest-equipped group.

Next, 50% of ATP incidents resulted in “Other” consequences. This category involves idiosyncratic behaviors 

and unique events, making statistical analysis unreliable.

The final column in Table 11 concerns “Recovery.” This 

addresses problem-solving styles. We know that different 

pilots have different styles when it comes to weather. For 

instance, Ball (2008) found that pilots tended to be either 

“tactical thinkers” or “strategic thinkers,” depending on 

whether they tried to pick their way through weather cells 

or tended to avoid lines of weather entirely. 

Now let us take a brief, expanded look at preferred 

recovery strategies in before returning to finish discussing 

Table 11.

Table 12 implies that, when it comes to recovery strategies, “instrument-rated pilots seem to think alike.” To 

understand this, note how the proportions in the darkhighlighted “ATP” row often appear similar to those in 

the “non-ATP IR” row immediately below it. These two 

rows correlate significantly (rATP-nonATPIR =.632, pr

=.028, 

2-tailed). However, the non-IR pilots’ decision pattern 

(row) resembles neither of the other 2 rows (r

ATP-nonIR = 

-.273, pr

=.390, NS and rnonATPIR-nonIR = -.068, pr=.834, 

NS).35 The correlation between ATP and non-ATP IR 

supports the assertion that instrument-rated pilots think 

alike. If reliable, these patterns would further support 

the idea that instrument-rated and non-instrument-rated 

pilots need to be treated differently because they recover 

from weather differently.

35 In Table 12, proportions are calculated by dividing cell frequencies by the 

number of pilots in that category (e.g., 7 of 18 ATPs descended to stay below 

weather, so 7/18 = .39). Between-group tactical pattern similarity is based on 

Spearman rank-order correlation between each of the three pilot group’s cell 

frequencies across 10 categories of recovery strategy (the category “Other” is 

excluded because it is not unitary).

Table 11. Significant group pattern differences between ATPs, non-ATP IR, and non-IR pilots 

(numbers represent proportions). 

Deception? Flightplan Weather ATC Consequences Recovery 

Air transport pilot? Instrument rated? Preflight weather forecast "Don't recall" Enroute weather forecast "Don't recall" Destination forecast "Don't recall" Destination forecast thunderstorm Did NOT file a flight plan Filed an IFR flight plan Encountered ground fog Encountered icing Encountered thunderstorms Requested IFR clearance from ATC Requested instrument approach from ATC Aircraft equipment problem contributed to incident Lost/unsure of position during incident Experienced loss of control during incident Incident resulted in "Other" consequencesDeviation around weather enabled recovery Landing at alternate enabled recovery 

μATP Y Y 0.06 0.11 0.06 0.28 0.44 0.44 0.17 0.06 0.22 0.22 0.06 0.00 0.00 0.00 0.50 0.44 0.11

μnATP N Y 0.00 0.05 0.04 0.04 0.38 0.40 0.02 0.25 0.02 0.24 0.25 0.15 0.11 0.02 0.24 0.16 0.04

μnIR N N 0.15 0.37 0.22 0.04 0.81 0.00 0.04 0.04 0.15 0.00 0.04 0.00 0.33 0.15 0.07 0.11 0.26

pΧ2 0.02 0.00 0.03 0.00 0.03 0.00 0.05 0.03 0.02 0.04 0.03 0.04 0.01 0.03 0.02 0.03 0.02

μATP Y Y 0.00 0.89 0.17 0.11 0.17 0.00 0.17 0.11 0.17 0.39 0.39 0.33 0.17 0.33 0.56

μnATP N Y 0.18 0.64 0.02 0.00 0.02 0.24 0.00 0.00 0.00 0.05 0.09 0.05 0.00 0.15 0.33

μnIR N N 0.33 0.26 0.04 0.00 0.04 0.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.04

pΧ2 0.04 0.02 0.05 0.01 0.05 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.04 0.00 Single VHF transceiver- equipped Dual VHF transceiver- equipped HF transceiver-equipped ACARS-equipped Skyphone-equipped Single VOR receiver- equipped INS/IRS-equipped INS/IRS used Integrated area navigation-equipped Terrain Warning System- equipped Weather Radar-equipped Weather Radar used Wing/tail bleed-air anti- ice-equipped Engine (air) inlet anti-ice- equipped Approach-capable autopilot-equipped 

Com Nav Avoidance De-ice A/P 
18 

For the sake of completeness, the last four rows of Table 

12 show the data as ranks, both for the whole N=100 group, 

and also broken out by pilot rating. This shows, for instance, 

that although the #1 group recovery strategy was to descend 

below the weather, ATPs preferred to deviate around it. Given 

the equipment, training, and experience of ATPs, these tactics 

make sense. Nonetheless, given the small number of pilots 

per cell, we need to keep sampling error in mind and refrain 

from assigning too much meaning to individual cell results. 

Statistically, patterns tend to be more reliable (e.g., overall 

correlations between one row and another).

Two broader notions fall out of this micro-analysis. First, 

pilots seem to have “mental priorities” of preferred solutions 

to a given weather problem. Second, because these were 

incidents, they obviously attempted recovery maneuvers 

that were not always successful. We will return to these ideas 

in later discussion.

Equipment

The above detailed analysis diverted us away from Table 

11. Therefore, we now return to the bottom half of Table 

11, noting that it all concerns equipment. So far, the message we have seen seems to be that ATPs tend to have the 

best equipment.

Following that theme, we can now see how our three 

pilot ratings differed in the kinds of aircraft they flew, as 

shown in Figure 9:

Figure 9 implies that ATPs operated the majority of 

high-performance jets and turboprops, while non-ATPs flew 

mainly single-engine, fixed-gear airframes. However, finding 

statistical support for that statement is not straightforward. 

There are too many equipment categories relative to the 

sample size of N=100, and many have values near 0, so there 

is no simple way to statistically analyze these patterns.36

36 Chi-square (C2

) cannot not used here because 16 of 21 expected frequencies 

(seaplanes omitted) are < 5, grossly violating the assumptions of that analysis.

Nonetheless, we can straightforwardly assert that the 

frequencies in Figure 9 are not distributed as we would 

expect by chance (pFisher’s Exact Test = .0005). So, equipment 

differences may exist across certificate type; the question 

is where?

Table 13 shows 2-tailed Spearman r intercorrelations 

between the three groups’ equipment patterns (minus 

seaplanes because no one had a seaplane).

To find where the deviation lies, we first note that the 

non-ATP IR pilots’ equipment pattern is significantly 

similar to the non-IR pilots’ (r = .821, pr

 = .023, 2-tailed), 

while both those look different from ATPs (p=NS). So, 

we suspect that ATPs have different equipment from 

non-ATPs. The question is: “How?”

Next we calculate deltas—actual minus expected cell 

frequencies—for each Figure 9 equipment category.37

Table 14 illustrates (positive values indicate that actual 

value > expected value; negative values indicate that actual 

value < expected value).

Table 14 informally implies that ATPs seemed to 

dominated in jets (Category 1, +2.5) and trail in singleengine, fixed-gear (Category 5, -4.7), while non-ATP IRs 

dominated in “retractable-gear, six-passenger, or known 

TAA (technically advanced aircraft)” (Category 4, +5.3), 

and non-IRs dominated in single-engine, fixed-gear 

(Category 5, +6.4). These equipment patterns are consistent with intuition, once again supporting the notion 

of treating ATPs, non-ATP instrument-rated, and noninstrument-rated pilots as separate entities. However, we 

must caution that Table 14 lacks the statistical rigor of 

our other analyses and should be treated as speculative.

37 Expected cell frequency comes from the standard C2

 method, namely row 

total*col total/grand total.

Table 12. Recovery strategies, grouped by pilot rating. 

N

Descended to stay below wx OtherLanded at destinationATC offered assistanceDeviated around wx 180-degree turn Instrument approach Requested IFR clearance from ATC Climbed above wx Broke out on top Landed enroute (other than alternate)Landed at alternate 

Whole-group totals 100 34 25 23 21 20 19 16 14 13 12 12 11

ATP 18 7 5 5 3 8 3 4 4 1 2 4 2 

non-ATP IR 55 20 14 14 12 9 9 10 10 7 4 3 2 

By frequency

non-IR 27 7 6 4 6 3 7 2 0 5 6 5 7 

ATP 0.39 0.28 0.28 0.17 0.44 0.17 0.22 0.22 0.06 0.11 0.22 0.11

non-ATP IR 0.36 0.25 0.25 0.22 0.16 0.16 0.18 0.18 0.13 0.07 0.05 0.04

By proportion

non-IR 0.26 0.22 0.15 0.22 0.11 0.26 0.07 0.00 0.19 0.22 0.19 0.26

Whole-group rank 1 2 3 4 5 6 7 8 9 10 10 11

ATP 18 2 3 3 5 1 5 4 4 7 6 4 6

non-ATP IR 55 1 2 2 3 5 5 4 4 6 7 8 9

non-IR 27 1 2 4 2 5 1 6 7 3 2 3 1
19

Table 13. Equipment intercorrelations (p-values are in parenthesis). 

ATP non-ATP IR 

ATP 1.0

non-ATP IR 0.604 (.151) 1.0

non-IR 0.480 (.275) 0.821 (.023)

Figure 9. Equipment differences between ATPs, non-ATP 

IR, and non-IR pilots. Since there are exactly 100 pilots, 

the raw values for each category (vertical axis) are also the 

percentages relative to the entire group of 100.

Table 14. Equipment category deltas (seaplanes omitted).

Delta (Actual – Expected frequencies) 

Equipment category 1 2 3 4 5 7 8

ATP 2.5 1.5 0.6 -1.5 - 4.7 1.9 - 0.2

non-ATP IR - 0.7 - 0.7 0.6 5.3 -1.7 -1.3 - 0.6

non-IR - 0.8 -0.8 -1.2 - 3.8 6.4 - 0.6 0.7
20 

Disc

Historically, adverse weather has been a major cause 

of general aviation accidents and fatalities. However, 

weather alone is never the sole culprit. There are always 

other factors involved. Some of these causal factors are 

more common than others. Some are easier and/or less 

expensive to fix. Some exert a greater impact on the accident and fatality rates

As investigators, we sometimes try to find these salient 

factors through data mining. In data mining, we search 

through recorded data for underlying statistical commonalities between seemingly disparate events, seeking 

to find risk factors that logically relate to one another. 

Naturally, this works best with large amounts of 

high-quality data. Yet, GA accidents are relatively rare, 

often lacking a living witness to explain what happened. 

Consequently, we sometimes turn to incident analysis. 

Incidents are less serious than accidents, but many times 

more common, and there are witnesses to help better 

determine causes.

To data-mine weather-related GA incidents, we turned 

to the Aviation Safety Reporting System, a clearinghouse 

for public, anonymous reporting of U.S. aviation safety 

incidents. The current project followed up GA weatherrelated incident reports made to the ASRS during 2005-

06. With the permission of pilots who had previously 

reported a weather-related incident, ASRS made 100 

“callback” interviews to look deeper into this specific kind 

of incident. Each interview involved data collection on 

nearly 300 variables related to possible root causes. The 

resulting 30,000-odd data were mostly yes/no responses, 

the rest being free-response text.

In reporting these results, we need to keep in mind a 

few limitations as to how much we can generalize from 

this sample to GA pilots in general. First, while ASRS data 

are submitted voluntarily, it may contain unknown biases. 

Second, all samples contain sampling error, meaning they 

risk overrepresenting some factors while underrepresenting others, purely by chance. By the very definition of 

chance, we expect a small number (1-5%) of “statistically 

significant” relations to ultimately prove false.

With such caveats in mind, the ASRS staff prepared 

a preliminary report. This is available on-line, entitled 

General aviation weather encounters (ASRS, 2007). 

To summarize the ASRS report, major problems 

seemed rare with

• Preflight weather briefing (99% reported briefing, 

71% reported using 2 or more sources).

• Use of ATC in-flight services when requested (59% 

requested them).

• Awareness of geographic position (80% of pilots had 

GPS, presumably mostly hand-held units).

While substantial problems seemed evident with:

• Lack of weather-related experience for both noninstrument-rated and newly minted instrument-rated 

pilots.

• Weather conditions of deteriorating visibility and icing.

• Pilots failing to request in-flight ATC services (41%).

• Aircraft lacking substantial weather avoidance systems 

(66%).

• Non-weather-related, compounding factors, especially 

“decision making,” “time pressure,” “get-home-itis,” 

and aircraft equipment problems.

Using that analysis as a springboard, the current report 

examines the data in greater detail. The following factors 

seemed to pose no discernable problem for most pilots:

1) All pilots at least had access to high-quality preflight 

weather information.

2) 95% were at least partially successful in getting a 

preflight weather briefing.

3) Group weather information-use patterns appeared 

normal as far as could be seen.

In contrast, the following factors seemed to constitute 

a problem for 5%, or more, of pilots:

1) Darkness (4 dusk +17 night = 21%).

2) Moisture affecting visibility (clouds, fog, rain, snow > 

50%) and/or air movement affecting aircraft handling 

(thunderstorm, icing, turbulence > 25%).

3) Multiple weather factors experienced simultaneously 

(85%).

4) Failure to get a preflight weather briefing, or “briefing” with only a low-grade (non-aviation-oriented) 

source (5%).

5) Deterioration of weather forecast accuracy over time 

(66% correct forecasts at departure, decreasing to 

37% correct at destination).

6) Weather that materialized worse than predicted (35%. 

This implicitly includes lack of en-route forecast 

updates).

7) Lack of weather-related training and experience 

(> 50%, non-instrument-rated and new instrumentrated pilots).

8) Inadequate equipment (less-experienced pilots tend 

to have less-capable airframes and avionics).

9) Ambulance missions (7%, particularly helicopter 

ambulance).

10) “Non-weather related factors”

a) Decision-making (26%).

b) Time pressure (21%).

c) “Get-home-itis” (9%).

d) Aircraft equipment problem (8%).

e) Fatigue (7%).

f) Distraction by passenger or crew (5%).
21

The Importance of Training

One major result of the current study revolves around 

the usefulness of grouping pilots into 3 categories based 

on certification/training. Statistically and logically, three 

discrete groups emerged:

1) Air transport pilots (ATPs)

2) Non-ATP, instrument-rated pilots(non-ATP IR)

3) Non-instrument-rated pilots(non-IR, VFR, private)

These three groups displayed not only differences in 

flight hours, weather experience, and equipment, they 

also differed in the kinds of weather they attempted to 

fly in, as well as recovery strategies. Based on quantitative 

analysis of this ASRS data, we can summarize the main 

findings of this study in terms of these three groups.38

In Table 15, a “+” rating means “these pilots’ group 

tended to score high on this factor.” A blank cell means 

“average on this factor,” and a “-” cell means “below average.” “LOR” refers to “likelihood of recovery.”

Table 15 implies that there are training and experience 

differences, equipment differences, and stylistic differences 

in the way pilots tend to handle weather. In fact, each 

group seems to have problems with the exact worst category 

of weather with which it is legally and “culturally” expected 

to be able to cope. 

And, nowhere does acculturation come home to roost 

more than with non-instrument-rated pilots. These pilots 

are only trained in avoiding bad weather. So, when bad 

weather finally enters unannounced, it easily induces 

disorientation, strong emotions, and subsequent errors of 

judgment. For instance, non-IRs had the second-highest 

rate of thunderstorm encounters during their incidents. 

They also had the highest rate of becoming lost (33%), 

and the highest rate of aircraft loss-of-control (15%). Only 

9 of 27 non-IR pilots reported having VMC forecast for 

all three phases of their flight—meaning that two-thirds 

apparently consciously knew that they were taking a risk 

by flying into marginal VMC or worse. Moreover, non38 These reflect factors with arguable statistical differences. Note that all 

“miscellaneous” categories are absent because, by definition, these are catch-all 

categories, not discrete factors.

IRs’ aircraft were typically the least capable at handling 

truly severe weather. Eighty-nine percent had no major 

weather-avoidance electronics, 89% had no physical antiicing systems on the aircraft, and 56% had no autopilot 

or wingleveler. Fourteen (52%) were missing all three. 

Finally, nearly all their flight missions were either for 

pleasure or training, yet five pleasure flights were lowlight (two dusk, three night), none had a flight plan, and 

all ended with severe visibility problems due to weather 

compounded by darkness.

Additionally, ultra-low-hour, low-experience non-IR 

pilots constitute a major target group, representing roughly 

18-20% of these 100 incidents. Being at the bottom of the 

status hierarchy also constitutes a “multiple curse,” having 

most, if not all, of the factors below stacked against them:

• Less training

• Less total flight experience

• Less weather experience

• Lower-performance aircraft

• Little ice-handling capability

• Lower-grade avionics

Conclusi

Reco

In the short term, this analysis reveals two major atrisk target groups:

• Non-instrument-rated pilots

• Newly minted instrument-rated pilots

These are two distinct groups, with distinct training 

needs. Non-IR pilots need to be able to strategically recognize dangerous weather and have well-practiced tactics 

to avoid it. New IR pilots need the strategic ability to 

distinguish between handleable IMC and unhandleable 

IMC. Their training needs are bimodal. They need one 

set of well-practiced tactics for handleable IMC and a 

quite distinct set for unhandleable IMC.

Table 15. Weather-related group differences by pilot certification. 

Group 

TrainingExperience (h flown) Aircraft performance Likelihood of A/C equipment problem Advanced avionics Ice-handling capability Likelihood of a problem w. icing Likelihood a problem w. thunderstorms Likelihood of becoming lost or disoriented Likelihood of A/C lossof-controlLikelihood of recovery (LOR) by descent below wx LOR by deviation around wx LOR by 180-degree turnLOR by landing at alternateType of weather most likely to be problematic

ATP + + + - + + - + - - + + - - Severe 

Non-ATP IR + + - + - - - Intermediate 

Non-IR - - - - - - - + + + + - + + All 
22 

On the tactical level, while deviation around weather 

is an effective strategy for experienced pilots flying 

capable aircraft, it appears we need to emphasize 180°

turns and landing-at-alternates as effective options for 

less-experienced pilots, particularly those flying morebasic aircraft.

Both groups need to be proactive about developing 

alternatives in the event of adverse weather. This means 

thinking about alternative actions before they are needed, 

not waiting until the last minute.

 No matter what their training, experience, or equipment, all pilots need to develop and maintain a “risk 

conscience”—an inner sense of right and wrong when it 

comes to risk-taking. Whenever they meet with a weather 

situation that piques that conscience, they need to listen 

to it and activate those pre-planned alternatives.

Finally, both IR and non-IR pilots need a way to 

develop and maintain weather expertise in a safe setting. 

The most obvious and cost-effective ways to do this are 

through PC- and Web-based weather-skill testing and 

training programs, both in traditional knowledge-based 

format and flight simulator format. In particular, Bayesian 

adaptive testing39 and adaptive training are new, exciting, 

cost-effective strategies that need to be seriously considered. Such technologies would use computer algorithms 

to quickly home in on a testee/trainee’s level of ability and 

could deliver far more “bang for the buck” than current 

methods of weather-related testing and training.

Future Exploratio

The results we have discussed raise a number of concerns. All are potential areas of research.

First, we are becoming increasingly aware that no single 

factor ever explains any incident or accident. Trouble is 

virtually always caused by some combination of events.40

Consequently, effective categorization of accident causal factors helps organize and streamline the investigative process 

and guides the development of programs to fix problems. 

This process needs continuation, specifically investigation 

of what combinations of factors seem most hazardous.

Another concern has to do with how certain individuals may develop risky flying habits. This may involve 

sampling error. Sampling error happens when “too much 

luck”—good or bad—happens to us purely by chance. 

39 In computerized adaptive testing, after a correct answer, the test-taker is 

presented a more difficult question. Conversely, after an incorrect answer, a 

slightly less-difficult item is presented. In this way, the test “statistically homes 

in on the test-taker’s reliable level of competence,” and automatically stops 

the test once an arbitrary (e.g. 95%) statistical confidence level is achieved.

40 It is a combinatorics problem. The number of ways p causes can form 

combinations of size k is p!/k!(p-k)!. As p grows, the number of combinations 

explodes. This is one of the things that makes accident investigation difficult.

What may happen, psychologically, is that people who are 

consistently lucky may start developing ideas that they are 

immune to misfortune, while people who are consistently 

unlucky may start believing exactly the opposite. These 

beliefs may very well influence pilots’ willingness to take 

risks with bad weather. 

Additionally, the data examined here teach us the 

usefulness of considering the flight mission, because 

flight mission often comes with certain motivations and 

mindsets attached. The categories we looked at were:

1) Ambulance

2) Business

3) Passenger

4) Pleasure (Daytime v. Night + Dusk)

5) Training

6) Ferry

7) Freight

8) Miscellaneous

Causal/explanatory “stories” given by the pilots in 

each Training x Mission category are presented in Appendix D. The full analysis of these may be presented 

in a later report. For now, to summarize briefly, each 

category of flight mission looks like a world unto itself. 

The ATP flying the life-or-death ambulance mission is 

simply not the same as the non-IR pilot going out on 

a day off for a pleasure flight. The mission is different, 

the motivation is different, pilot training tends to be 

different, as does the airframe, avionics, and so on. 

This way of grouping data needs to be explored, but 

the methodology is largely qualitative, which explains 

why we defer it for the time being.

Finally, looking forward, the current study can be 

seen as preliminary to one planned by FAA’s Aviation 

Weather Group (AJP-6810), which would evaluate 

weather-related accidents involving weather-datalinked 

aircraft. Datalink is designed to enhance safety by 

providing pilots with increasingly accurate, timely information on critical factors such as weather. Yet, logic 

suggests that a few problems may remain and/or new 

ones potentially created despite the new technology. 

What these are remains to be seen. However, certain 

methodologies employed in the current study may be 

portable and useful to such future studies.
23

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24 

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A1

APPENDIX A

(Source: FAA Joint Safety Analysis Team. (2002). General aviation decision-making, final report. Retrieved Oct. 30, 

2006 from www.faa.gov/about/initiatives/safer_skies/gajsc_documents/media/aeronautical.pdf)

Post-Classical ADM

Beginning in 1991 the FAA initiated a long-term research effort to develop a better understanding of pilot 

decision-making and to develop new interventions to improve decision-making. These studies have focused on how 

pilots acquire and use information to make decisions and, more recently, on how the personality attributes of pilots 

affect their decision-making.

Some of the research findings related to ADM are:

(a) When evaluating weather, pilots tend to let one good aspect of the weather compensate for bad aspects. For 

example, pilots let a high ceiling compensate for low visibility, when rating weather risk. Particularly for 

novice pilots, this is a potentially dangerous practice.

(b) Low-time pilots, in particular, seem not to take into account either dangers or resources outside a very 

narrow corridor along their planned route of flight. For example, they seem to exhibit “tunnel vision” and 

don’t consider the potential for weather to move across their route, nor do they consider using alternative 

airports located a short distance from their route of flight. 

(c) Pilots allow their proximity to their destination to overrule caution when faced with deteriorating weather 

conditions. Pilots will press-on through conditions near their destination, while the same conditions when 

encountered early in the flight will result in a diversion.

(d) Some pilots, particularly those with low time, will make a decision when faced with a problem, and will 

then make no further efforts to diagnose the problem, seek help, consider alternatives, or evaluate the effectiveness of their decision. 

(e) Many pilots are unable to judge when conditions have deteriorated to near or below VFR minima, and are 

unaware of the environmental cues that would alert them to this deterioration. 

(f) Pilots differ in their perceptions of the degree of risk of flying activities, and those who perceive the lowest 

risk (usually of weather) tend to be more at risk for accident involvement. However, differences in risk 

tolerance are not related to accident risk.

(g) Pilots who believe themselves to be more in control tend to be at lower accident risk than those who believe 

that what happens to them is the result of outside factors (i.e., fate, luck, other people and organizations).

(h) Overconfidence in personal abilities and an inability to accurately assess visibility are associated with continued VFR flight into IMC in simulation studies.

(i) In tests of situational judgment, half of the time pilots will choose a course of action not recommended as 

best (i.e., safest) by a panel of instructors.

(j) Approximately 35% of private and commercial pilots have not attended a Safety Seminar in the last two 

years; 20% have attended 1 seminar. The predominant reason for not attending is “too busy.”

B1

APPENDIX B 

General Aviation Weather Encounters Supplemental Coding Structure

NOTE 1.......This form provides for ALL fields and field values for the General Aviation Weather Encounters supplemental 

coding form database, including Full-Form Coding Form fields that will not be included in the reporter mail-out 

form.

NOTE 2.......This form is NOT formatted for distribution (in planning format only), and will not be formatted for distribution 

until the supplemental coding form questions are finalized). 

NOTE 3.......When ASRS receives a weather-related report, it will contact the reporter and request their participation in the 

General Aviation Weather Encounters project. Those who agree to participate will be mailed the supplemental 

coding form (Part 1); this form will NOT contain the fields that provide data found in ASRS Full-Form Coding. 

When the completed form has been returned to ASRS, the supplemental data will be integrated with relevant 

ASRS Full-Form data in a General Aviation Weather Encounters database to create a complete, comprehensive 

record.

NOTE 4.......The original questionnaire asked event-specific information as well as some general information — we feel that 

this can be confusing to respondents. Thus, this supplemental coding form generally contains questions pertaining to the reported event. A general survey of GA Weather issues may best be conducted separately. 

Part 1 — Supplemental Coding Form 

Supplemental coding form to be mailed to project participants. Every effort has been made to convert narrative (anecdotal) responses to list selections, because narrative responses will most commonly need to be converted to lists in order to be meaningful.

Section A — Flight Planning and Weather Briefings 

Section B — Incident Information 

Section C — Contributors and Consequences 

Section D — Aircraft Equipment 

Section E — Instrument-Rated Pilot Information 

Section F — Summary 

Part 2 — ASRS Full-Form Coding Forms Fields 

Data coded during standard ASRS Full-Form analysis.

Section G — Administrative / General / Environment 

Section H — Aircraft Data 

Section I — Reporter Data 
B2 

PART 1 — Supplemental Coding Form

(Mail-out to Study Participants) 

Section A — Flight Planning and Weather Briefings 

A.1 Did you attempt to obtain pre-flight weather information? (Check one only)

 Yes (Go to Question A.2) 

 No (Skip to Question A.8) 

A.2 What sources of weather information did you utilize? (Check all that apply)

 National Oceanic & Atmospheric Administration (NWS) 

 (Automated) Flight Service Station (FSS) 

 Direct User Access Terminal (DUATS) 

 Commercial Vendors 

 Hazardous Inflight Weather Advisory Service (via VORs) 

 Transcribed Weather Broadcast (TWB) 

 Pilots Automatic Telephone Weather Answering Service 

 En route Flight Advisory Service 

 The Weather Channel 

 Other pilots 

 Other (please specify): ___________________________ 

A.3 Were any of your attempts to obtain pre-flight weather information unsuccessful? (Check one only)

 Yes 

 No 

A.4 If you answered “Yes” to Question A.3 above, what were the reasons your attempts were unsuccessful? (Check all 

that apply)

 Did not know or were unable to find telephone or access numbers 

TELEPHONE 

 No telephone available 

 No answer on telephone 

 Telephone briefer did not have all requested information available 

 Telephone briefer denied service 

COMPUTER 

 No online access available 

 Could not connect online 

 Could not maintain online connection 

 Required information not available on computer 

 Experienced difficulty with computer interface 

OTHER

 Other (please specify): ___________________________ 

A.5 What was the preflight weather forecast for the following? (Check one only in each category)

a) Departure airport................................ VMC /  Marginal VMC /  IMC 

b) En route ............................................  VMC /  Marginal VMC /  IMC 

c) Destination airport .............................. VMC /  Marginal VMC /  IMC 

A.6 What were the forecast conditions for the following? (Check all that apply)

a) Departure ................... Fog /  Ice /  Rain /  Snow /  Tstorm /  Turbulence 

b) En route ..................... Fog /  Ice /  Rain /  Snow /  Tstorm /  Turbulence 

c) Destination.................. Fog /  Ice /  Rain /  Snow /  Tstorm /  Turbulence 

A.7 Was the actual weather better than, the same as, or worse than forecast? (Check one only in each category)

a) Departure ................... Better than... /  Same as... /  Worse than... 

b) En route ..................... Better than... /  Same as... /  Worse than... 

c) Destination.................. Better than... /  Same as... /  Worse than... 

A.8 If you answered “No” to Question A.1 above, why did you NOT attempt to obtain pre-flight weather information 

prior to departure? (Check all that apply)

 Did not believe pre-departure weather was necessary for the intended flight 

 Was intimidated by process of obtaining weather 

 Did not know or was unable to find telephone or access numbers 

 No telephone available 

 No online access available 

 Other (please specify): ___________________________ 

Section B — Incident Information 

B.1 Were you (the pilot) deviating from your planned route because of weather when the incident occurred? (Check 

one only)

 Yes 

 No 
B3

B.2 What type of weather did you encounter? (Check all that apply)

 Broken or solid undercast  Reduced visibility 

 Deteriorating weather ahead  Rising cloud tops 

 Flew into clouds or fog  Strong cross winds 

 Ground fog  Thunderstorms 

 Icing  Turbulence 

 Lowering ceiling  Unknown? but IMC conditions 

 Merging cloud layers  Rain 

 Snow showers  Hail 

Other (please specify): ___________________________ 

B.3 Did you attempt to obtain en route assistance from ATC? (Check one only)

 Yes (go to Question B.4) 

 No (go to Question B.5) 

B.4 If you answered “Yes” to the Question B.3, what services did you request? (Check all that apply)

 Emergency climb/descent 

 IFR clearance 

 Vectors to an airport 

 Vectors to VMC 

 PIREPS 

 Weather update 

 Instrument approach procedure 

 Other (please specify): ___________________________ 

B.5 If you answered “No” to Question B.3, what was the primary the reason you did not attempt to obtain assistance 

from ATC? (Check one only)

 Did not feel ATC services were required 

 Did not know that ATC services were available 

 Did not know the frequencies to contact ATC 

 Did not know what services to ask ATC for 

 Knew that communications would not be possible at my altitude or location 

 Was afraid of a reprimand or certificate action 

 Other (please specify): ___________________________ 

B.6 If you were unable to obtain ATC services, or could not obtain them in a timely manner, what were the reason(s)? 

(Check all that apply)

 ATC would not provide clearance 

 There was a delay in ATC providing clearance 

 Request for an IFR or SVFR clearance was denied 

 Too low for radar coverage 

 Unable to contact ATC 

 Other (please specify): ___________________________ 

Section C — Contributing Factors and Consequences 

C.1 Were there factors other than weather that contributed to the incident? (Check one only)

 Yes (Go to Question C.2) 

 No (Skip to Question C.4) 

C.2 If you answered “Yes” to Question C.1, what non-weather related factors contributed to the incident? (Check all 

that apply)

 Company policy  FBO policy 

 Distraction by pax or flight crew  Get-home-itis 

 Aircraft equipment problem  Time pressure 

 Fatigue  Illness 

 Other (please specify): ___________________________ 

C.3 If you answered “Yes” to “Time Pressure” in Question C.2, what were the reasons for the time pressure? (Check all 

that apply)

 A “void if not off by...” ATC clearance 

 Approaching darkness 

 Deteriorating weather 

 Schedule pressure 

 Personal pressure to reach a destination on-time 

 Family or other personal emotional pressures or distractions 

 Other (please specify): ___________________________ 

C.4 What were the consequences of your weather encounter? (Check all that apply)

 Unable to maintain altitude 

 Lost/unsure of position 

 Landed below published IFR minimums 

 VFR flight in IMC 

 Made IFR approach without an IFR rating 

 Landed VFR in IMC 

 Landed without clearance 

 Penetrated controlled airspace 

 Loss of aircraft control 

 Controlled flight toward terrain 
B4 

 Precautionary landing or off-airport landing 

 Runway excursion 

 Other (please specify): ___________________________ 

C.5 What factors enabled you to recover from the weather encounter? (Check all that apply)

 Aircraft warning system  Descended through hole in undercast 

 Instrument approach  Deviated around weather 

 ATC offered assistance  Ground proximity warning system 

 Broadcast on 121.5  Landed at destination 

 Broke out on top  Landed en route 

 Cancelled IFR  Vector to airport 

 Climb/attempted to climb  Vector to VMC 

 Descended to stay below weather  180 degree turn 

 Radar vector 

 Contacted ATC and requested IFR clearance 

 Other (please specify): ___________________________ 

Section D — Aircraft Equipment 

NOTE 1.......Aircraft make and model is captured in ASRS Full-Form data.

NOTE 2.......We do not believe that aircraft year of manufacture provides useful information as many older aircraft have 

extensively modified avionics packages. 

D.1 What type(s) of navigation and communication equipment were onboard the aircraft at the time of the incident, did you 

use it and, if equipped, was any of it unserviceable? (Check all that apply)

COMMUNICATION EQUIPMENT .............................. EQUIPPED ...... USED UNSERVICEABLE 

 No communication equipment 

Single VHF transceiver ...............................................  ...............  

Dual VHF transceiver .................................................  ...............  

Combination NavCom.................................................  ...............  

HF transceiver...........................................................  ...............  

ACARS .....................................................................  ...............  

Skyphone .................................................................  ...............  

Cell Phone ................................................................  ...............  

Other (please specify): ____________ ........................  ...............  

NAVIGATION / FLIGHT MANAGEMENT .................... EQUIPPED ...... USED UNSERVICEABLE 

 No navigation equipment 

Single VOR receiver .................................................... ............... .....................

Dual VOR receiver ...................................................... ............... .....................

ILS ........................................................................... ............... .....................

ADF .......................................................................... ............... .....................

DME.......................................................................... ............... .....................

GPS .......................................................................... ............... .....................

INS/IRS .................................................................... ............... .....................

Integrated area navigation .......................................... ............... .....................

Moving map ............................................................... ............... .....................

Terrain Warning System .............................................. ............... .....................

Other (please specify): ____________ ......................... ............... .....................

D.2 Was the aircraft equipped with any of the following weather-avoidance equipment, and if it was, what equipment was 

used during the incident? (Check all that apply)

EQUIPMENT EQUIPPED USED UNSERVICEABLE 

 No weather avoidance equipment 

Weather radar............................................................ ............... .....................

Lightning Detector/Stormscope .................................... ............... .....................

Weather data link (NEXRAD, METARS, etc) .................... ............... .....................

Other (specify) _____________ ................................... ............... .....................
B5

D.3 What type of de-ice or anti-ice or other equipment did the aircraft have at the time of the incident, did you use it, and if

equipped was any of it UN-serviceable?

(Check all that apply)

EQUIPMENT EQUIPPED USED UNSERVICEABLE 

 No de-ice, anti-ice or other related equipment was installed 

Wing/tail De-icing boots .................................................... ...............  ............... 

Wing/tail Bleed-air anti-ice ................................................ ...............  ............... 

Wing/tail electrical anti-ice ................................................ ...............  ............... 

Wing/tail TKS (fluid-type anti-ice) ...................................... ...............  ............... 

Propeller de-ice or anti-ice................................................. ...............  ............... 

Engine (air) inlet anti-ice ................................................... ...............  ............... 

Windshield de-ice or anti-ice .............................................. ...............  ............... 

Other (specify) _____________ ......................................... ...............  ............... 

D.4 Did the aircraft have an autopilot and were you using it at the time of the incident? (Indicate one type only, and note if 

used or unserviceable) 

EQUIPMENT EQUIPPED USED UNSERVICEABLE 

 No, the aircraft did not have an autopilot or wingleveler 

Wing leveler .................................................................... ...............  ............... 

Basic autopilot autopilot .................................................... ...............  ............... 

Approach-capable autopilot................................................ ...............  ............... 

Section E — Instrument-Rated Pilot Information 

If you are instrument rated, please answer the questions in this Section; if you are NOT instrument rated, skip to Section F. 

E.1 How many actual instrument hours have you flown? 

____________________ (total hours) 

E.2 How many simulated (under the hood) instrument hours have you flown? 

____________________ (total hours) 

E.3 How many simulator hours have you had? 

____________________ (total hours) 

E.4 How many instrument approaches have you conducted in actual instrument conditions in the past year? 

____________________ (number) 

E.5 How many instrument approaches have you conducted in actual instrument conditions in the past 90 days? 

____________________ (number) 

E.6 In the majority of your instrument flights, has the actual weather been better than, the same as, or worse than the 

reported weather at the following? (Check one only in each category) 

E.10.a Departure airport ............................................  Better than... /  Same as... /  Worse than... 

E.10.b. En route........................................................  Better than... /  Same as... /  Worse than... 

E.10.c. Destination airport..........................................  Better than... /  Same as... /  Worse than... 

Section F — Summary 

F.1 Why do you think the incident occurred? 

F.2 In retrospect, is there anything you would have done differently? 

F.3 What would you recommend that others do to avoid a similar occurrence? 

– END REPORTER SUPPLEMENTAL CODING FORM – 
B6 

PART 2 — ASRS Full-Form Coding Fields

(Included in Final Data Set) 

Note 3 .....These fields are contained in all ASRS Full-Form records, and will not be required on the mail-out 

supplemental coding form. 

Section G — Administrative / General / Environment 

G.1 Accession Number: 

[ASRS accession number, numeric value, 6 digits, unique]

G.2 Date of Incident: 

[numeric value, MMYYYY] 

G.3 Day: 

[Sun, Mon, Tue, Wed, Thu, Fri, Sat]

G.4 Local Time of Day: 

[0001 to 0600, 0601 to 1200, 1201 to 1800, 1801 to 2400]

G.5 Locale Reference: 

[Airport (ID), NAVAID (ID & Type), Intersection (ID), Special Use Airspace (ID & Type), ATC 

Facility (ID & Type)]

G.6 State Reference: 

[ state ]

G.7 Altitude: 

[AGL — Single Value (feet), Lower Boundary (feet), Upper Boundary (feet); MSL — (Single 

Value (feet), Lower Boundary (feet), Upper Boundary (feet)]

G.8 Flight Conditions: 

[VMC, IMC, Mixed, Marginal, Special VFR]

G.9 Weather Elements: 

[Fog, Ice, Rain, Snow, Thunderstorm, Turbulence, Windshear, Other (element)]

G.10 Light: 

[Dawn, Daylight, Dusk, Night]

G.11 Ceiling: 

[Single Value (feet), Lower Boundary (feet), Upper Boundary (feet), CLR]

G.12 Visibility: 

[Single Value (statute miles), Lower Boundary (statute miles), Upper Boundary (statute 

miles)]

Section H — Aircraft Data 

H.13 RVR: 

[Single Value (feet), Lower Boundary (feet), Upper Boundary (feet)]

H.14 Controlling Facilities: 

[ARTCC (ID), TRACON (ID), Tower (ID), Military (ID)]

H.15 Coordinating Facilities: 

[FSS (ID), CTAF (ID), UNICOM (ID), Commercial Radio (ID)]

H.16 Operator: 

[Common Carrier (Air Carrier, Air Taxi, Charter); General Aviation (Corporate, Instructional, 

Private); Other (Government, Military Other). Additional data may be derived from the “Operating under FAR Part” field: Part 91, Part 119, Part 121, Part 125, Part 129, Part 135, Other 

(part number)] 

H.17 Aircraft Make & Model: 

[______________________]

H.18 Aircraft Configuration 

[Number of Engines: ___________] 

[Propulsion: None, Reciprocating, Turboprop/Turboshaft, Turbojet] 

[Surface: Land, Sea, Amphibian] 

[Gear: None, Fixed, Retractable] 

[Wings: None, High, Mid, Low, Biplane, Rotary] 

H.19 Operating Under FAR Part: 

[Part 91, 119, 121, 125, 129, 135, Other _______]

H.20 Flight Plan: 

[VFR, IFR, IFR combined VFR, DVFR, None]

H.21 Mission: 

[Aerobatics, Agriculture, Ambulance, Banner Tow, Business, Ferry, Freight, Passenger, Photo 
B7

Shoot, Pleasure, Refueling, Repositioning, Skydiving, Tactical, Test Flight, Traffic Watch, Training, Other __________]

H.21 Navigation In Use: 

[Localizer, ILS, FMS/FMC, GPS/Area Nav, INS, NDB, Pilotage, VOR, Other _____]

H.22 Flight Phase: 

[Ground: Parked, Pushback, Maintenance, Preflight, Taxi, Holding, Position and Hold, Takeoff Roll, Other 

____] 

[Climbout: Takeoff, Initial, Intermediate Altitude, Vacating Altitude, Other ____] 

[Cruise: Level, Holding, En route Altitude Change, Other ____] 

[Descent: Approach, Holding, Intermediate Altitude, Vacating Altitude, Other ____] 

[Landing: Hold Short, Roll, Touch and Go, Go-Around, Missed Approach, Other ____] 

[Other: Other ____] 

H.23 Airspace Occupied: 

[Class A (ID), Class B (ID), Class C (ID), Class D (ID), Class E (ID), Class G (ID), SUA (ID), 

Temp Use (ID)] 

H.24 Component: 

[Component Code _____, Design Deficiency, Failed, Improperly Operated, Malfunctioning, Not Installed, 

Incorrect Part] 

Section I — Reporter Data 

I.25 Involvement (truncated): 

[...Pilot Flying, Pilot Not Flying, Receiving Instruction, Other ____]

I.26 Affiliation: 

[Government: FAA, Foreign, Military, Other ___] 

[Company: Air Carrier, Air Taxi, Charter, Corporate, Other ___] 

[Other: Contracted Service Instructional Personal, Other ___]

I.27 Flight Crew: 

[Single Pilot, Captain, First Officer, Relief Pilot, Second Officer, Navigator, Load Master]

I.28 Qualification: 

[Student, Private, Instrument, Multi-engine, Commercial, ATP, CFI, Flight Engineer, Military]

I.29 Flight Time: 

[Total (hours) ______] 

[Last 90 Days (hours ______] 

[Type (hours) ______] 

C1

APPENDIX C 

Aircraft Classification Categories 

7 A109 PA-28 Cherokee Arrow IV 4

 Aeronca Champion PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

 Aircoupe A2 PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

2 Airliner 99 PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

7 AS 350 Astar/Ecureuil PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

7 AS 350 Astar/Ecureuil PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

3 Baron 55/Cochise PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

2 Beechcraft Twin Turboprop Jet Undifferentiated or Other Model PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

 Bird Dog 305/321 PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

7 BO105 PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

4 Bonanza 33 PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

4 Bonanza 33 PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

4 Bonanza 35 PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

4 Bonanza 36 PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

 Caravan 1 208A PA-28 Cherokee/Archer II/Dakota/Pillan/Warrior 

4 Cardinal 177/177RG PA-31 Navajo Chieftan/Mojave/Navajo T1020 

4 Cardinal 177/177RG PA-32 Cherokee Six/Lance/Saratoga 4

 Cessna 150 PA-32 Cherokee Six/Lance/Saratoga 4

 Cessna 150 PA-34-200 Seneca I 3

 Cessna 150 PA-34-200T Turbo Seneca II 3

 Cessna 150 Sail Plane 8

 Cessna 150 Skyhawk 172/Cutlass 172 

 Cessna 150 Skyhawk 172/Cutlass 172 

 Cessna 150 Skyhawk 172/Cutlass 172 

 Cessna 152 Skyhawk 172/Cutlass 172 

4 Cessna 210 Centurion / Turbo Centurion 210C, 210D Skyhawk 172/Cutlass 172 

4 Cessna 210 Centurion / Turbo Centurion 210C, 210D Skyhawk 172/Cutlass 172 

4 Cessna 210 Centurion / Turbo Centurion 210C, 210D Skyhawk 172/Cutlass 172 

3 Cessna 310/T310C Skyhawk 172/Cutlass 172 

3 Cessna 340/340A Skyhawk 172/Cutlass 172 

3 Cessna 402/402C/B379 Businessliner/Utiliner Skyhawk 172/Cutlass 172 

 Cessna Single Piston Undifferentiated or Other Model Skyhawk 172/Cutlass 172 

4 Cessna Stationair/Turbo Stationair 7/8 Skyhawk 172/Cutlass 172 

1 Challenger Jet Undifferentiated or Other Model Skyhawk 172/Cutlass 172 

 Cheetah, Tiger, Traveler Skyhawk 172/Cutlass 172 

 Cheetah, Tiger, Traveler Skyhawk 172/Cutlass 172 

 Christen Eagle II Skyhawk 172/Cutlass 172 

1 Citation V Skylane 182/RG Turbo Skylane/RG 4

3 Duchess 76 Skylane 182/RG Turbo Skylane/RG 4

 Experimental Aircraft Skylane 182/RG Turbo Skylane/RG 4

 Experimental Aircraft Skylane 182/RG Turbo Skylane/RG 4

1 Gulfstream V Skylane 182/RG Turbo Skylane/RG 4

7 Jet Ranger Undifferentiated or Other Model Skylane 182/RG Turbo Skylane/RG 4

7 Jet Ranger/Kiowa/206 Skylane 182/RG Turbo Skylane/RG 4

4 M-20 B/C Ranger Skylane 182/RG Turbo Skylane/RG 4

4 M-20 K (231) Skylark 175 

Key Skywagon 185 

1 Jet Small Aircraft, High Wing, 1 Eng, Fixed Gear 

2 Turboprop Small Aircraft, Low Wing, 1 Eng, Fixed Gear 

3 Twin-engine piston SR20 4

4 Single-engine retractable gear OR 6-passenger-OR TAA SR22 4

 (blank) Single-engine, fixed gear Super King Air 200/Huron 2

6 Seaplane (none here) Twin Otter DHC-6 3

7 Helicopter Viking 

8 Glider 

D1

APPENDIX D 

Synopsis of Interview Section F, Free-Response Explanations of Why the Incident Occurred 

Table D1 tabulates the 100 missions, grouped by three levels of pilot training (ATP, non-ATPinstrument rated (non-ATP IR), and non-IR pilots). 

Table D1. Flight mission categories. "Night" includes dusk. Ambulance Business Ferry Freight Miscellaneous Passenger Pleasure

Training

N Day Night 

ATPs 18 3 5 4 4 1 1 

non-ATP IR 55 2 8 2 3 4 6 19 6 5 

non-IR 27 1 1 15 5 5 

column totals 100 5 14 2 3 4 11 38 12 11 

Air Transport Pilots 

There were 18 ATPs, falling into five primary mission categories. 

Category 1. (Ambulance, n=3) 

This was fairly straightforward to explain. Two of the three pilots flew helicopters; the remaining pilot 

flew a turboprop (i.e., above-average aircraft). All unexpectedly hit VFR-into-IMC. 

What makes this category stand out is that all three missions were highly motivated due to a seriously ill 

patient onboard. All were single-pilot missions. Additionally, only one pilot reported filing an IFR flight plan, 

two incidents occurred at night, and the remaining pilot reported having a distinct “lack of weather reporting 

stations” along his route. All this is to say that task demands were high, multiple factors were involved, time 

was critical, and the pilots were undoubtedly counting on a certain amount of luck that failed to materialize. 

By nature, medical flights are caught in a unique and constant three-way squeeze between medical pressures, 

weather pressures, and business cost constraints. 

Category 2. (Business, N=5). All problems involved reduced visibility. All were daylight operations. One 

pilot’s reason for reporting simply centered around landing below minimums while trying to escape weather. 

The remaining four had more serious problems. Two could not maintain altitude due to ice, one encountered 

a thunderstorm and thought he’d been struck by lightning, and the last pilot was beaten by a hurricane 

accompanied by severe radio interference. 

What typically unites business pilots is their mission, their motivation, their equipment, and their 

professionalism. Here, the mission is to make money. Motivation is high, yet not routinely honed to the lifeor-death edge of the air ambulance pilot. Equipment tends to be good (though not always). Here, we had two 

jets, one turboprop, one twin piston, and a lone single-engine, fixed-gear plane (the ambitious-but-luckless 

pilot who encountered a hurricane).1

Category 3 (Passenger, n=4) 

All four cases share the unspoken motivation to get passengers to a destination on time. All four were 

daylight operations, one helicopter, one jet, one 4-person single-engine, high-performance, and one twin 

piston. All but one pilot ran into weather worse than expected (and that remaining one expected 

thunderstorms). The first pilot particularly blamed pressure to reach the destination on time. The second saw 

the airport but ran into fog during the descent. The third blamed inaccurate en route weather reports for 

failing to emphasize the thunderstorms. The fourth blamed ATC for failing to understand the situation and 

giving “absurd requests” in the face of a thunderstorm encounter. 

 

1

 Interestingly, that pilot reported 24,000 flight hours experience, with 9000 instrument hours (probably meaning “hours flying an 

IFR flight plan”). Still—hardly a novice. 
D2 

Category 4 (Pleasure, n=5) 

Four of these pleasure flights were daytime, with the remaining flight at dusk. All were single-engine 

aircraft with, at most, retractable gear. None had any weather avoidance equipment or de-icing equipment. 

Remarkably, all pilots had and used both GPS and moving map displays. All but one encountered weather 

worse than predicted, with the remaining pilot admittedly trying to outrun approaching darkness and visibly 

deteriorating weather. Two pilots filed an IFR flight plan, the remaining three filed no flight plan at all. 

Pilot 1 admittedly failed to get a good preflight briefing (partly due to weather products being out of 

service at his departure FBO). Subsequently, he encountered severe turbulence and trouble maintaining 

altitude. Pilot 2 was (aforementioned) trying to outrun darkness and weather. Pilot 3 also admitted “gethome-itis,” had not filed a flight plan, and landed at an alternate after breaking cloud-clearance regulations. 

Pilot 4 blundered near thunderstorms and went VFR-into-IMC. Pilot 5 said he “delayed departure too long,” 

got too close to a thunderstorm, and penetrated controlled airspace. 

Category 5 (Training, n=1) 

This case was straightforward. The pilot had filed no flight plan, “didn’t account for... nocturnal cooling 

affects [sic]” and then went VFR-into-IMC during a night operation. 

Non-ATP Instrument Rated Pilots 

The 55 non-ATP IR incidents fell into considerably more categories than any other group. 

Category 1 (Ambulance, n=2) 

This category was, in many ways, similar to ATPs—high time pressure, single-pilot helicopter operations 

that ran into weather after filing no flight plan. Both of these flights were in daylight, however. 

One pilot reported having 160 actual instrument hours, the other failed to answer that question. Both 

failed to answer the questions about 12 m and 90 d instrument approaches. So, by default, we have to suspect 

that these pilots were relatively inexperienced with weather, especially given the usual platform they fly. 

Considering the ATP ambulance pilots, along with the present ones, one suspects that the modus 

operandi of ambulance operations is “Fly first, apologize afterward.” And who among us would challenge that 

policy once we found ourselves in need of such services? Medical operations have always been granted greater 

latitude in society, out of common sense and necessity. They do need to be considered in a category by 

themselves. 

Category 2 (Business, N=8) 

Only two pilots here filed an IFR flight plan, but all ran into reduced visibility conditions as the primary 

problem. 

Beyond that, the secondary problems showed more scatter. The first reported time pressure due to fuel 

shortage (previous airport would not accept his credit card). Two more pressed on, believing conditions 

would improve. The fourth was insufficiently familiar with new GPS equipment. The fifth misjudged the 

service ceiling of his airplane. The sixth checked his departure ATIS, but not the weather at his destination 

airport. The seventh got caught in weather with a non-IFR certified plane. And the last simply admitted to 

“get-home-itis.” 

Category 3 (Passenger, n=6) 

These were all single-pilot operations in moderately equipped aircraft ranging from single-engine, fixedgear to twin piston. All but one flight was in daylight. 

A uniting theme of these flights was that “other factors” came into play in every flight. Half were 

psychological pressures, the other half were equipment problems. Three pilots reported feeling pressured (one 

“felt pressured to get our guests back to Honolulu,” another “let management talk me into attempting the 

flight I had already turned down,” and the third was tired, felt ill, and wanted to get home). Three more 

pilots had equipment failures (one had an intermittent problem with the PTT [push-to-talk] switch that 

contributed to a loss of position awareness, a second “had problems with transponder and attitude indicator,” 

and the third, night-flying, pilot lost cabin lights). All these factors contributed to unintended rule violations 

(primarily VFR-into-IMC), hence the ASRS reports. 
D3

Category 4 (Pleasure) 

Category 4a (Pleasure, Night and Dusk, n=6). 

There were 25 non-ATP IR pleasure flights. All were single-pilot operations. Since night flights present 

significantly greater challenges during weather encounters than equivalent daytime flights, the pleasure flights 

were, therefore, grouped by time of day, with one dusk flight grouped in with the night flights. 

Understandably, all six night flights reported trouble with visibility, with fog, clouds, and undercast being 

the main problems. All ended in rule violations, five being VFR-into-IMC. 

Oddly, despite everyone reporting at least one phase of flight as MVMC or worse, only three pilots filed 

an IFR flight plan. One of these subsequently had a brush with a thunderstorm. The other three pilots filed 

no flight plan at all. These three reported having just 50, 12, and 12 hours actual instrument experience, 

respectively (compare with 400, 360, and 20 for the pilots who filed IFR). Half the pilots openly admitted to 

“get-home-itis” as a motivator. 

It is natural to wonder if these pilots may have had special equipment to give them an edge in nighttime 

flying. Five were both ILS- and GPS-equipped (but so were 19 of the 25 in the Pleasure category), with 3 of 

those having moving map displays. Of the three, two had lightning detection and autopilot. So these two 

were relatively well-equipped, and that may have been inspired confidence in their pilots. 

Finally, one has to ponder the remaining pilot who had none of the above, was flying a Cessna 150 at 

night, into predicted rain and fog, with just 50 hours’ instrument experience, and no flight plan on file. This 

was clearly a risky flight, given the circumstances. 

Category 4b (Pleasure, Day, n=19) 

This is the largest single category of any group, and is easiest summed up in Table D2. 

Table D2. Possible causal factors, non-ATP IR pilots, daylight pleasure flights. Pilot Ran into unexpectedly bad weather Less than 50 actual instrument h Poor preflight/inflight weather briefing "Get-home-itis" Lack of experience Complacency Cockpit distractions Knowingly taking risk Equipment problems Didn't contact ATC fearing cert. action Physiological (fatigue, vertigo) ATC uncooperative or too busy Below radar or poor radio coverage Unable to maintain altitude Lost/unsure of position Poor avionics 

No flight plan filed 

1 X X X X X X 

2 X X X 

3 X X X X 

4 X X X X 

5 X X X X

6 X X X X X X 

7 X X X X X X 

8 X X 

9 X X 

10 X X X X

11 X X X X 

12 X X X X X 

13 X X X X X X X 

14 X X X 

15 X X X

16 X X X X X 

17 X X X X 

18 X X X 

19 X X X 

These were all single-pilot flights except for #5. The most sophisticated airframe was a twin piston (#19). 

All others were single-engine piston, #1-7 being fixed-gear, #8-18 being retractable-gear, six-passenger, or 

known as technically advanced aircraft (TAA). 

The most graphic truth that Table D2 illustrates is how multiple factors conspire to form an incident. The 

other thing it shows is how great the diversity is among causal factors, even within a logically defined group. 

This particular problem has plagued the field of accident investigation as long as it has existed. 
D4 

Category 5 (Training, n=5) 

These were fairly straightforward reports. All shared the common theme of an individual filing to avoid 

possible punishment. All involved single-engine, fixed-gear aircraft engaged in flight training. 

Pilot 1 took off into IMC forecast for all three phases of flight, but the pilot was fatigued and became 

spatially disoriented after flying into clouds, leading to loss of aircraft control. Pilot 2 ran into a “fast-moving, 

low-level broken cloud layer” during touch-and-goes, resulting in VFR-into-IMC because he had not filed a 

flight plan. Pilot 3, with a total of 3.5 actual instrument hours, became unsure of his position and penetrated 

controlled airspace after being confused by complexity in the sectional map. The aircraft in question was 

relatively ill-equipped, having little in the way of advanced navigational aids. Pilot 4, an instructor, reported 

“student froze on controls,” causing VFR-into-IMC on a no-flight plan-filed flight. The fifth and final report 

appeared completely prophylactic. An instructor took off with a student on an IFR flight plan into forecast 

VMC, but ran into snow showers, successfully deviated, and filed—simply to head off any feared punishment 

by the FAA. 

Category 6 (Ferry, n=2) 

Two daylight ferrying flights were reported, both involving single-engine, fixed-gear aircraft, both 

straightforward. Pilot 1 reported becoming unsure of position, resulting in VFR-into-IMC. He blamed 

“Time pressure. My boss was rushing me and pushing me to go,” despite his facing deteriorating weather 

(primarily clouds and rain). These problems were compounded by an in-flight malfunction of both radio and 

ILS. Pilot 2 encountered rain, fog, and icing, and struggled to maintain altitude while fatigued and being 

“Unfamiliar with aircraft, area, and no real IMC experience” (9 hours actual instrument experience). 

Category 7 (Freight, n=3) 

These were high-hour professional pilots. Obviously, none of these incidents resulted in injury, and 

reports were filed to explain procedural deviations in case of a problem with corporate management. Pilot 1 

encountered severe icing in a single-engine, fixed-gear plane that was otherwise well-equipped (weather radar, 

de-icing boots, prop and engine de-ice). This resulted in diverting to an alternate airport. Pilot 2 was flying a 

very well-equipped turboprop, encountered an out-of-service AWOS at destination, and continued to another 

airport rather than land without a weather report. Pilot 3 was flying a twin piston and also encountered icing 

severe enough to limit climb to 50 fpm. ATC was slow in granting clearance to climb beyond the icing layer. 

Finally, he inadvertently disconnected his autopilot on descent. 

Category 8 (Miscellaneous, n=4) 

Three of the four pilots left the mission category blank, hence they were grouped as miscellaneous by 

default. All were single-pilot, daylight flights. Pilot 1 was a 16 actual-instrument-hour pilot who went on his 

very first mission on a “Traffic Watch” flight. With no flight plan filed, he proceeded to fly VFR-into-IMC 

with near-CFIT because he “wanted to make a good impression on the traffic watch reporter that I was flying 

with.” Pilot 2, on an IFR flight plan, started picking up ice, experienced a delay in getting ATC descent 

clearance, and descended to warmer altitude. This was exercising judgment as pilot-in-command but was still 

technically a violation. Pilot 3 got squeezed between undercast and rising cloud tops and ended up 

inadvertently penetrating controlled airspace before being vectored to VMC by ATC. Pilot 4 ran into 

unexpected thunderstorms and went VFR-into-IMC, blaming the weather briefer at the FSS for failing to 

convey proper weather information, and blaming himself for failure to execute a 180 turn and leave the area 

while still possible. 

Non-IR Pilots 

The 27 non-instrument-rated pilots fell primarily into two main mission categories of pleasure and 

training. 
D5

Category 1a (Pleasure, Daytime, n=15) 

Table D3 best summarizes the results. 

If lack of experience seems to play a bigger factor here than it does with IR pilots on pleasure flights 

(Table D2), keep in mind that that is just a side-effect of our having grouped pilots by rating in the first 

place. 

More strikingly, several categories seem relatively under-populated, for instance, cockpit distractions, 

overt acknowledgment of knowingly taking risk, fear of certificate action, ATC uncooperative/busy, and 

being out of radio or radar coverage. It is difficult to know what to make of such a lack of information, other 

than to intuitively sense a pattern, that pattern being summed up by the old saying, “We don’t know what we 

don’t know.” In other words, the more seasoned IR pilots—particularly the ATPs—seem to know far better 

what to say on these reports than do these more casual, less experienced pilots. They seem more adept at 

telling the story of what happened to them and why. 

Table D3. Possible causal factors, non-IR pilots, daylight pleasure flights. Pilot Ran into unexpectedly bad weather Less than 50 actual instrument h Less than 500 flight h Poor preflight/inflight briefing Poor planning / decision making "Get-home-itis" Lack of experience / unfamiliarity w. procedures Perceptual misjudgment Complacency / Inattention Cockpit distractions Knowingly taking risk Equipment problems Didn't contact ATC fearing cert. action Physiological (fatigue, vertigo) 

ATC uncooperative or too busy Below radar or poor radio coverage Unable to maintain altitude Lost/unsure of position Poor avionics No flight plan filed 

1 X X X 

2 X X X X X 

3 X X X X X 

4 X X X X X X X

5 X X X X 

6 X X X X X X 

7 X X X X X 

8 X X X X X X

9 X X X X X 

10 ? X X X X X 

11 X X X 

12 X X X X X

13 ? X ? X ? 

14 X X X X X X X X 

15 X X X X 

Category 1b (Pleasure, Dusk and Night, n=5) 

Flights 1-2 were dusk, 3-5 were night. Flights 1-3 were single-engine, fixed gear aircraft, 4-5 were singleengine, 4-passenger, fixed-gear. No pilot had filed a flight plan. Four of five pilots encountered weather worse 

than expected. Pilot 2 reported 55 hours actual instrument time, Pilot 6, reported 6 hours, the rest 0 hours. 

Pilot 1 encountered ceiling and visibility degradation, had trouble contacting ATC because they were 

busy, had to wait for vectors, and wound up going VFR-into-IMC with a near-CFIT. Pilot 2 took off for a 

very brief hop of 75 miles without a preflight weather briefing, encountered a thunderstorm exacerbated by a 

dropout in radio reception, and ended up landing without clearance. Pilot 3 took off to practice night touchand-goes, supposedly after briefing with the FSS (although all items pertaining to forecasts are “conveniently” 

unanswered). He encountered fog, contacted ATC, declared an emergency, and ATC vectored him to land at 

a military airport. Pilot 4 ran into unpredicted rain, undercast, and turbulence near destination, lost 

positional awareness, requested vectors, and ended up penetrating controlled airspace without clearance. Pilot 

5 took off from an uncontrolled field to work on his night currency, ran into undercast, missed the airport 

cues, and went VFR-into-IMC. 

What unites these pilots is that everyone reported loss of visibility and subsequent loss of position 

awareness. Low-light conditions of dusk or night obviously played a major factor in this. Moreover, even 
D6 

though Pilots 1 and 5 had GPS and ILS, either lack of experience, confusion, or both prevented them from 

successfully getting out of trouble by themselves. Fortunately, all were cool-headed enough to contact ATC, 

and this is undoubtedly what saved them from a tragic ending. 

Category 2 (Training, n=5) 

All five of these flights occurred during daylight. Four involved single pilots flying single-engine, fixedgear aircraft; the remaining flight involved a two-place glider with an instructor and a student. All resulted in 

VFR-into-IMC. No one filed a flight plan. Three pilots had less than 500 flight h experience. Two were 

working on an instrument rating, but both had less than 7 hours’ actual instrument time. Only one had an 

autopilot but it was unserviceable. 

The individual stories within this group are sketchy. In a few cases there is a suspicious amount of critical 

missing information here. For instance, one pilot reported checking preflight weather but failed to specify 

what source he consulted. Four pilots simply reported the cause of their incident being weather closing in 

faster and more severely than predicted. The remaining (glider) pilot reported being caught in an updraft and 

swept up into IMC, claiming “ATC would not provide clearance,” yet immediately contradicting himself, 

saying he “Did not feel ATC services were required.” 

Category 3 (Miscellaneous, n=2) 

This contained two single-pilot VFR-into-IMC flights, one business flight (single-engine, fixed gear), one 

passenger flight (a tour helicopter). No flight plans were filed. Both pilots claimed to be working on their 

instrument rating, but the business pilot claimed only 5 h of actual instrument time (195 total flight h), the 

other left that item blank (1350 total flight h). The business pilot blamed the incident on an inaccurate 

weather report. The tour helicopter was playing “follow the leader” with another helicopter and flew into 

clouds hidden within haze. 

The only destination weather source either pilot consulted was ATIS, with the business flight also 

consulting ASOS at departure. That immediately puts these two pilots into the “Inadequate weather briefing” 

category.