NTSB CAROL · Event
Event DFW06CA035
Aircraft involved
Probable cause & findings
The pilot's improper compensation for the existing wind conditions resulting in a loss of control. A contributing factor was the gusty crosswind.
Factual narrative
The 700-hour private pilot was unable to maintain directional control of the tricycle gear single-engine airplane while taking off from Runway 31. The pilot stated that during takeoff, a gust of wind raised the left wing, causing the right wing tip to impact the runway. The nose of the airplane then came down, which resulted in the propeller impacting the ground. The airplane departed the right side of the 5,352-foot long by 100-foot wide runway, coming to rest in an upright position in the grassy area between the runway and taxiway. The pilot reported having accumulated a total of 130 hours in the same make and model. Thirty-six minutes after the accident, the automated weather station, located on the airfield, was reporting winds from 270 degrees at 16 knots, gusting to 22 knots. The 700-hour private pilot was unable to maintain directional control of the tricycle gear single-engine airplane while taking off from Runway 31. The pilot stated that during takeoff, a gust of wind raised the left wing, causing the right wing tip to impact the runway. The nose of the airplane then came down, which resulted in the propeller impacting the ground. The airplane departed the right side of the 5,352-foot long by 100-foot wide runway, coming to rest in an upright position in the grassy area between the runway and taxiway. The pilot reported having accumulated a total of 130 hours in the same make and model. Thirty-six minutes after the accident, the automated weather station, located on the airfield, was reporting winds from 270 degrees at 16 knots, gusting to 22 knots. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_2005_DFW06CA035.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
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Related research
What the literature says.
Academic papers and agency reports matching this event's aircraft type or causal vocabulary (loss of control). Sourced from NASA NTRS, NTSB Safety Studies, FAA CAMI, AOPA Air Safety Institute, Embry-Riddle Scholarly Commons, arXiv, and the Semantic Scholar academic graph.
- Embry-Riddle Scholarly Commons 2025 · Journal article (JAAER)
A Scoping Review of Aviation Loss of Control Inflight Research
Loss of control – inflight (LOC-I) contributes to aircraft accidents at unacceptably high rates. Significant industry efforts and research have aimed to improve LOC-I prevention, detection, and recove…
- SKYbrary (Eurocontrol) 2024 · SKYbrary article
Loss of Control In-Flight (LOC-I) — SKYbrary Knowledge Base
SKYbrary comprehensive knowledge-base entry on Loss of Control In-Flight — definitions, contributing factors, accident case studies (Air France 447, Colgan 3407), and prevention strategies.
- NTSB Aircraft Accident Reports 2022 · Accident report
Loss of Control on Takeoff in Icing Conditions — Citation 560XL
Cessna Citation 560XL fatal takeoff icing accident, March 2018. Investigation of a Citation 560XL loss-of-control takeoff accident in icing conditions.
- Semantic Scholar 2021 · Article (Aviation)
ANALYSIS OF GENERAL AVIATION FIXED-WING AIRCRAFT ACCIDENTS INVOLVING INFLIGHT LOSS OF CONTROL USING A STATE-BASED APPROACH
Inflight loss of control (LOC-I) is a significant cause of General Aviation (GA) fixed-wing aircraft accidents. The United States National Transportation Safety Board’s database provides a rich source…
- NASA NTRS 2021 · Presentation
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
- NASA NTRS 2021 · Conference Paper
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
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