NTSB CAROL · Event
Event FTW98LA133
Aircraft involved
Probable cause & findings
The pilots improper compensation for the wind conditions and his failure to maintain control of the aircraft. A factor was the high wind condition.
Factual narrative
On February 24, 1998, at 2100 central daylight time, a Cessna 172D airplane, N2526U, was substantially damaged following a loss of control while taxing at the El Paso International Airport, El Paso, Texas. The private, instrument rated pilot and the passenger were not injured. Visual meteorological conditions prevailed for the Title 14 CFR Part 91 personal flight. The flight originated from the Fort Stockton-Pecos County Airport, Fort Stockton, Texas, approximately 1715, and no flight plan was filed. The pilot reported to the FAA inspector that he landed the airplane on runway 24. While exiting from the active runway onto the taxiway, during high wind conditions, the aircraft was "lifted off the ground and flipped over". The airplane came to rest inverted on the taxiway. The winds at the time of the accident were reported from 240 degrees, at 46 knots, with gusts to 51 knots. The FAA inspector who examined the airplane at the site reported that both wing tips were curled. He also observed that the propeller blades were bent. Attempts to obtain a completed copy of the Pilot/Operator Aircraft Accident Report (NTSB Form 6120.1/2), from the pilot, who was reported to have returned to his homeland in Germany, were unsuccessful. The pilot reported to an FAA inspector that he landed the airplane on runway 24. While exiting from the active runway onto the taxiway, in high wind conditions, the airplane was lifted off the ground and flipped over. The winds at the time of the accident were reported from 240 degrees, at 46 knots, with gusts to 51 knots. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_1998_FTW98LA133.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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