NTSB CAROL · Event
Event ANC22LA078
Registry · N24BR
FAA Aircraft Registry record.
Make / Model
DEHAVILLAND DHC-2
Year of manufacture
1953 · 69 years old at event
Engine
P&W R-985 SERIES (450 hp)
Seats / Engines
8 seats · 1 engine
Last airworthiness date
19930528
ADS-B equipped
Yes — Mode-S A22DCF
Registrant of record
RIVER FRONT AKN LLC
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
The pilot’s loss of control during takeoff in gusting wind conditions.
Factual narrative
The pilot was departing from a lake in a float-equipped airplane with passengers and hunting gear. He stated that the departure plan was to get the airplane on step into the wind, and then turn lengthwise and depart with a crosswind. During the takeoff run, the airplane “used a fair amount of water on step” before a gust of wind lifted the right wing. In an effort to correct for the gust, the pilot lowered the wing and added power, but the airplane was too close to the bank of the lake, which resulted in an impact with terrain and substantial damage to the fuselage, right wing and tail. The pilot reported no preimpact mechanical malfunctions or failures with the airplane that would have precluded normal operation. In the recommendation section of NTSB Form 6120.1, the pilot stated that a step turn departure may have been more appropriate. 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).
- — Personnel issues-Task performance-Use of equip/info-Aircraft control-Pilot
- — Aircraft-Aircraft oper/perf/capability-Performance/control parameters-Directional control-Not attained/maintained
- — Environmental issues-Conditions/weather/phenomena-Wind-Gusts-Contributed to outcome
Verbatim from NTSB's published report. Source file
NTSB_2022_ANC22LA078.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 ↗