NTSB CAROL · Event
Event WPR25LA120
Registry · N1418C
FAA Aircraft Registry record.
Make / Model
PIPER PA-22
Engine
LYCOMING 0-290 SERIES (140 hp)
Seats / Engines
4 seats · 1 engine
Last airworthiness date
19560430
ADS-B equipped
Yes — Mode-S A0A9DA
Registrant of record
WAAAM VOLUNTEER FLYING ASSOCIATION LLC
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
The pilot’s loss of control during landing roll with gusty winds.
Factual narrative
The pilot reported that prior to landing in the tailwheel equipped airplane, he obtained the current weather of the destination airport, which included the current wind. During the final approach to land the wind was a lot stronger and gustier than expected. He advanced the throttle to make a crosswind correction to the left. About 10 ft above the runway the pilot set up for a three-point landing. Shortly after landing, the left wing began to rise, and the nose stared to turn to the right. Subsequently, the airplane quickly turned to the right and the right wing tip and right elevator impacted the runway, which resulted in substantial damage. The pilot reported no preimpact mechanical malfunctions or anomalies that could have precluded normal operation. At the time of the accident, the pilot was landing on runway 16, with the wind from 150° at 16 knots, gusting to 21 knots. 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-Gusts-Ability to respond/compensate
- — Environmental issues-Conditions/weather/phenomena-Wind-Gusts-Effect on operation
- — Aircraft-Aircraft oper/perf/capability-Performance/control parameters-Directional control-Attain/maintain not possible
Verbatim from NTSB's published report. Source file
NTSB_2025_WPR25LA120.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 ↗