NTSB CAROL · Event
Event WPR23LA233
Registry · N8223A
FAA Aircraft Registry record.
Make / Model
CESSNA 170B
Engine
CONT MOTOR C145 SERIES (145 hp)
Seats / Engines
4 seats · 1 engine
Last airworthiness date
19560806
ADS-B equipped
Yes — Mode-S AB3B0B
Registrant of record
SMITH MATHIAS N
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
The pilot’s loss of control after encountering a dust devil during takeoff.
Factual narrative
The pilot of the tailwheel-equipped airplane reported that the winds at the airport were light and variable each of the three times he checked before takeoff. He performed a three-point takeoff, and as soon as he applied back pressure to the yoke, he observed a dust devil to the right and in front of the airplane. The airplane rolled aggressively left and he applied corrective control inputs as the airplane drifted to the left side of the runway. The main gear impacted vegetation, the airplane rolled, and came to rest inverted. The airplane sustained substantial damage to the wings and fuselage. The pilot reported that there were no preaccident mechanical failures or malfunctions with the airplane that would have precluded normal operation. A review of the Federal Aviation Administration Aviation (FAA) Weather Handbook (FAA-H-8083-28) and the FAA Aeronautical Information Manual found no detailed information listed about dust devils or the potential hazards of flying through dust devils. Source: NTSB Aviation Accident Database Retrieved: 2026-02-12
NTSB Findings
Hierarchical cause / factor breakdown from the FAA bulk avdata database. Each finding tagged C (Cause) or F (Factor).
- — Environmental issues-Conditions/weather/phenomena-Wind-Dust devil/whirlwind-Contributed to outcome
- — Aircraft-Aircraft oper/perf/capability-Performance/control parameters-Directional control-Attain/maintain not possible
Verbatim from NTSB's published report. Source file
NTSB_2023_WPR23LA233.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.
Browse the full corpus — academia portal ↗