NTSB CAROL · Event
Event ANC05CA083
Aircraft involved
Probable cause & findings
The pilot's inadequate compensation for the wind conditions during the landing roll, which resulted in a loss of control and an inadvertent ground-loop. A factor associated with the accident was a crosswind.
Factual narrative
On June 12, 2005, about 1030 Alaska daylight time, a Cessna 170B, N2830C, sustained substantial damage when it ground-looped following a loss of control during landing at Merrill Field, Anchorage, Alaska. The airplane was being operated by the pilot as a visual flight rules (VFR) personal local flight under Title 14, CFR Part 91, when the accident occurred. The private pilot and passenger were not injured. Visual meteorological conditions prevailed, and a VFR flight plan was filed. During a telephone conversation with the National Transportation Safety Board (NTSB) investigator-in-charge (IIC) on June 6, the pilot said he landed on runway 06. He said the Automatic Terminal Information Service (ATIS) was reporting the wind from 040 degrees at 6 knots, but he thought it was more of a direct crosswind, and gusting. The official weather observation for Merrill Field at 1030 reported the wind was from 030 degrees at 9 knots. The pilot said that during the landing roll, the left wing lifted, and he lost directional control. The airplane ground-looped to the left, and the right main landing gear collapsed. The airplane sustained damage to the propeller, main landing gear, fuselage, and left wing. The private pilot reported that the automatic terminal information service at the landing airport indicated the surface wind was from 040 degrees at 6 knots. During the landing roll on runway 6, the pilot stated the left wing lifted, and he lost directional control. The airplane ground-looped to the left, and the right main landing gear collapsed. The airplane sustained structural damage to the landing gear, fuselage, and left wing. The official weather observation taken at the time of the accident indicated that the wind was from 030 degrees at 9 knots. The pilot stated that there were no preaccident mechanical problems with the airplane. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_2005_ANC05CA083.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
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Related research
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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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