NTSB CAROL · Event
Event LAX93LA212
Aircraft involved
Probable cause & findings
THE PILOT'S FAILURE IN NOT DETERMINING THE SURFACE WINDS PRIOR TO LANDING WHICH RESULTED IN A LANDING IN CONDITIONS BEYOND THE DEMONSTRATED CAPABILITY OF THE AIRPLANE.
Factual narrative
On May 11, 1993, at about 1019 hours Pacific daylight time, a Cessna 152, N714ZL, nosed over following an on ground loss of control by the pilot while landing at Elko, Nevada. The aircraft was operated by American Aviation, Inc., of Bountiful, Utah, and was rented by the pilot for the personal cross country flight. Visual meteorological conditions prevailed at the time and a VFR flight plan was filed. The aircraft incurred substantial damage. Both the certificated private pilot and his one passenger sustained minor injuries. The flight originated at Salt Lake City, Utah, on the day of the mishap at about 0930 hours as a cross country to Elko. According to a verbal statement the pilot made to an FAA inspector from the Reno, Nevada, Flight Standards District Office, the flight from Salt Lake City to Elko was uneventful. After he arrived over the airport, he attempted unsuccessfully to contact someone on the UNICOM frequency for an airport advisory. When he could not raise anyone on the UNICOM frequency, the pilot tried to find a wind Tee or sock indicator to ascertain the winds and active runway, again without success. The pilot said he then observed other aircraft in the traffic pattern for runway 5 and entered the pattern for that runway. The pilot said that during the landing flare a sudden and strong cross wind gust began blowing the aircraft off the left side of the runway. He reported that he attempted to go around; however, the aircraft contacted the ground, encountered a berm and nosed over. The National Weather Service surface observation taken at the time of the accident was reporting in part winds from 190 degrees at 14 knots with gusts to 26 knots. The cross wind component for runway 5 was calculated at 9 knots with the gust factor to 16 knots. According to Cessna Aircraft, the maximum demonstrated cross wind component for the Cessna 150 and 152 series aircraft is 13 knots. THE PILOT SAID THAT AFTER HE ARRIVED OVER THE AIRPORT, HE ATTEMPTED UNSUCCESSFULLY TO CONTACT SOMEONE ON UNICOM FOR AN AIRPORT ADVISORY. WHEN HE COULD NOT RAISE ANYONE, THE PILOT TRIED TO FIND A WIND TEE OR SOCK INDICATOR TO ASCERTAIN THE WINDS AND ACTIVE RUNWAY, AGAIN WITHOUT SUCCESS. THE PILOT STATED HE THEN OBSERVED OTHER AIRCRAFT IN THE TRAFFIC PATTERN FOR RWY 5 AND ENTERED THE PATTERN FOR THAT RUNWAY. THE PILOT SAID THAT DURING THE LANDING FLARE A SUDDEN AND STRONG CROSS WIND GUST BEGAN BLOWING THE AIRCRAFT OFF THE LEFT SIDE OF THE RUNWAY. HE ATTEMPTED TO GO AROUND, HOWEVER THE AIRCRAFT CONTACTED THE GROUND, ENCOUNTERED A BERM AND NOSED OVER. THE NATIONAL WEATHER SERVICE SURFACE OBSERVATION TAKEN AT THE TIME OF THE ACCIDENT WAS REPORTING IN WINDS FROM 190 DEG AT 14 KTS, WITH GUSTS TO 26 KTS. THE CROSS-WIND COMPONENT FOR RWY 5 WAS CALCULATED AT 9 KTS, WITH A GUST FACTOR TO 16 KTS. ACCORDING TO CESSNA AIRCRAFT, THE MAXIMUM DEMONSTRATED CROSS-WIND COMPONENT FOR THE CESSNA 150/152 SERIES AIRCRAFT IS 13 KTS. Source: NTSB Aviation Accident Database (Pre-2008 Archive) Retrieved: 2026-02-12
Verbatim from NTSB's published report. Source file
NTSB_1993_LAX93LA212.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
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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.
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- Semantic Scholar 2021 · Article (Aviation)
ANALYSIS OF GENERAL AVIATION FIXED-WING AIRCRAFT ACCIDENTS INVOLVING INFLIGHT LOSS OF CONTROL USING A STATE-BASED APPROACH
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Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
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- 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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