NTSB CAROL · Event
Event WPR23LA025
Aircraft involved
Probable cause & findings
The non-certificated pilot’s loss of control for undetermined reasons.
Factual narrative
On October 25, 2022, sometime before 1800 Pacific standard time, an unregistered Airborne Windsports Edge X weight-shift-control aircraft was substantially damaged when it was involved in an accident at Alturas Municipal Airport (AAT), Alturas, California. The non-certificated pilot was fatally injured. The aircraft was operated as Title 14 Code of Federal Regulations Part 91 personal flight. According to law enforcement, an officer was dispatched at approximately 1807 and observed the accident aircraft between the north and southbound runway and taxiway. The responding officer had been on duty earlier and recalled observing the aircraft flying around the area between 1200 and 1500. There were no known witnesses of the accident event. Flight track data of the accident flight was not available.
PERSONNEL INFORMATION
According to the previous owner, before the transfer of ownership he conducted several flight hours with the accident pilot to demonstrate how the aircraft handles. After the previous owner had suggested not to fly the aircraft without first receiving introductory lessons, the accident pilot expressed confidence, and stated he was familiar with flying hang gliders.
AIRCRAFT INFORMATION
The previous owner stated that the engine and propeller had accumulated 7 hours of total time when he sold the aircraft to the accident pilot.
MEDICAL AND PATHOLOGICAL INFORMATION
The autopsy identified severe atherosclerosis in the pilot’s right coroner artery (95%) and the left anterior descending coronary artery (75%), severe aortic atherosclerosis, cardiomegaly (395 grams), and left ventricular hypertrophy (2.0 centimeters). Toxicology testing detected carboxy-delta-9-tetrahydrocannabinol (THC-COOH), in the pilot’s heart blood at 10 nanograms per milliliter (ng/mL) and in urine at 313.1 ng/mL. 11-hydroxy-delta-9-THC (11-OH-THC) was detected in his urine at 33.7 ng/mL, but not in his blood. The non-impairing medication salicylic acid was detected in the pilot’s heart blood and urine. The airplane was observed flying around the area at an unknown time on the day of the accident, but no other information was available about the flight. At about 1800, law enforcement received a notification that aircraft wreckage was discovered on the airport property between a runway and a taxiway. There were no known witnesses to the accident. Before the aircraft was sold to the non-certificated accident pilot, the previous owner had provided the pilot with several familiarization flights and emphasized the importance of obtaining instructional lessons since he had no experience with weight-shift-controlled aircraft. However, the pilot disregarded the advice likely because he assumed the aircraft had similar handling characteristics to hang gliders, which he had previous experience operating. Toxicology testing revealed the pilot had used cannabis; however, since no psychoactive THC or psychoactive metabolite was found in blood samples, it is unlikely that the pilot was under the influence of THC at the time of the accident. Given the pilot’s lack of experience in power gliders and the signature of the terrain impact, it is most likely that the pilot lost control of the power glider for undetermined reasons. 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-Experience/knowledge-Experience/qualifications-Qualification/certification-Not specified
- — Aircraft-Aircraft oper/perf/capability-Performance/control parameters-Descent/approach/glide path-Incorrect use/operation
- — Aircraft-Aircraft oper/perf/capability-(general)-(general)-Unknown/Not determined
Verbatim from NTSB's published report. Source file
NTSB_2022_WPR23LA025.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 ↗