ANALYZING WEATHER OBSERVATION DATA TO IMPROVE EMERGENCY SERVICES PILOT RISK ASSESSMENT IN MARGINAL WEATHER CONDITIONS
Emergency services (ES) pilots operate in a dynamic, high-risk team environment, as a subset of general aviation (GA) operations. The time constraints associated with ES operations means that ES pilots must make flight decisions quickly and often with limited or incomplete information (Worm, 1999). Due to the nature of ES operations, the consequences of an incorrect flight decision can be severe, including loss of life. ES operations are often initiated by extreme weather events, and ES pilots are frequently required to fly on the boundary between marginal visual flight rules (MVFR) weather conditions and instrument meteorological conditions (IMC). Unfortunately, an unintended transition into IMC is the leading cause of fatal accidents in GA operations (Ayiei et al., 2020). Mission objectives dictate that most ES pilots fly below 1,500’ above ground level (AGL) for extended periods of time, and low-altitude flight in hazardous weather can reduce a pilot’s outside visual reference, thus leading to spatial disorientation, loss of control, or controlled flight into terrain. To mitigate this problem, ES pilots must be able to accurately assess weather conditions before and during flight. However, the current method of presenting meteorological aerodrome reports (METARs) on weather displays can be misleading to pilots. Weather conditions in the areas between weather observation stations can be different than what is reported by the METAR observations at those stations. This can cause current or forecasted weather conditions between weather stations to be incompletely represented. However, pilots are given no obvious indication of how incompletely represented weather conditions can affect weather-related risk. This research demonstrates that a Kth Nearest Neighbor (KNN) analysis can be used to identify areas where the variability of conditions between weather stations (and thus weather-related risk) is incompletely represented by METAR observations. In addition, it is shown that areas where there is an increased risk of an unintended transition from MVFR to IMC can be identified among areas with incompletely represented conditions and depicted to pilots on aviation weather displays. Machine learning tactics are proposed as a way to consider additional inputs in future KNN analyses, and several emerging technologies are proposed as mediums to collect additional weather observations. The ability for an ES pilot to more accurately assess weather-related risk in MVFR conditions using the proposed technologies is evaluated, the benefits to ES pilots and the GA community are discussed, and the requirements and limitations of the study are examined.