ERA21LA074
2020-12-11 · Dexter, Maine, United States · None · 1 aircraft · Status: Completed
Airport 1B0
Current FAA registration · N19801
- Make / Model
- CESSNA 172M
- Year of manufacture
- 1972 · 48 years old at event
- Engine
- LYCOMING 0-320 SERIES (180 hp)
- Seats / Engines
- 4 seats · 1 engine
- Last airworthiness date
- 19720725
- ADS-B equipped
- Yes — Mode-S A18855
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
The pilot’s loss of directional control while landing, which resulted in an encounter with snow and subsequent nose over.
Factual narrative
The pilot was practicing take-offs and landings and described that during the landing flare, a gust of wind pushed the airplane toward the left side of the runway. The airplane’s left main wheel then encountered snow on the side of the runway that pulled the airplane to the left. When the airplane’s nose wheel hit the snow, the airplane nosed over. The vertical stabilizer and both wings of the airplane were substantially damaged. The pilot reported that there were no mechanical malfunctions or failures of the airplane that would have precluded normal operation. Source: NTSB Aviation Accident Database Retrieved: 2026-02-12
NTSB Findings
FAA avdata. C = Cause, F = Factor.
- — Aircraft-Aircraft oper/perf/capability-Performance/control parameters-Directional control-Not attained/maintained
- — Personnel issues-Task performance-Use of equip/info-Aircraft control-Pilot
- — Environmental issues-Physical environment-Object/animal/substance-Snow/ice-Effect on operation
- — Environmental issues-Conditions/weather/phenomena-Wind-Gusts-Effect on operation
Verbatim from NTSB's published report. Source file
NTSB_2020_ERA21LA074.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
Search this event elsewhere
External sources are reported, not agency: signal that something happened, not fact about what happened.
- TallyAero Live Wire Aviation press
- NTSB CAROL Agency ↗
- NTSB Docket Agency ↗
- Aviation Safety Network Aviation press ↗
- Kathryn's Report Aviation press ↗
- Aviation Herald Aviation press ↗
- AVweb Aviation press ↗
- Pilots of America Community ↗
- Reddit /r/flying Community ↗
- FlightAware Aviation press ↗
- AOPA accident database Aviation press ↗
- Google News News ↗
- DuckDuckGo News ↗
Related research
Matched on aircraft type or causal vocabulary (icing). All research papers
- NASA NTRS 2023 · Reprint (Version printed in journal) Finite Element Simulation of Three Full-Scale Crash Tests for Cessna 172 Aircraft
The NASA Emergency Locator Transmitter Survivability and Reliability project was initiated in 2013 to assess the crash performance standards for the next generation of emergency locator transmitter (E…
- NASA NTRS 2019 · Conference Paper Crash Testing and Simulation of a Cessna 172 Aircraft: Pitch Down Impact Onto Soft Soil
During the summer of 2015, NASA Langley Research Center conducted three full-scale crash tests of Cessna 172 (C-172) aircraft at the NASA Langley Landing and Impact Research (LandIR) Facility.
- NASA NTRS 2019 · Technical Memorandum (TM) Simulating the Impact Response of Three Full-Scale Crash Tests of Cessna 172 Aircraft
During the summer of 2015, a series of three full-scale crash tests were performed at the Landing and Impact Research Facility located at NASA Langley Research Center of Cessna 172 aircraft.
- arXiv 2026 · arXiv preprint Enabling Beyond-Visual-Line-of-Sight Drones Operation over Open RAN 5G Networks with Slicing
Among the foretold claims of the transition from 5G to 6G, Beyond-Visual-Line-of-Sight (BVLoS) drone operation has emerged as a prominent Internet-of-Robots enabler.
- NASA NTRS 2026 · Contractor Report (CR) Icing Physics Studies Using the 3D SIDRM Test Article: 2023 Icing Tests Analysis
In-flight icing is an important safety issue and is a factor that affects aircraft design and performance. Newer regulations are driving a need for improvements in airframe and engine icing simulation…
- arXiv 2025 · arXiv preprint Multi-Agent Deep Reinforcement Learning for UAV-Assisted 5G Network Slicing: A Comparative Study of MAPPO, MADDPG, and MADQN
The growing demand for robust, scalable wireless networks in the 5G-and-beyond era has led to the deployment of Unmanned Aerial Vehicles (UAVs) as mobile base stations to enhance coverage in dense urb…