Accurate and timely short-term (< 6 h) forecasts of high-impact aviation weather parameters, such as low ceiling, poor visibility, and thunderstorms are critical to the air-transportation industry. The effects of weather on commercial aviation were especially troublesome in 2000 as the number of aircraft delays reached an all-time high, with customer complaints up 16% from a year earlier. Adverse weather conditions not only plague the efficiency of commercial operations, but are also a safety concern, particularly for General Aviation pilots and passengers. A review of weather-related Accident Briefs indicates that the vast majority of fatal accidents (over 80%) is caused by low ceiling, poor visibility, and thunderstorms.

Clearly, improved weather guidance would be of great value to the aviation industry, especially if the guidance provided a measure of the uncertainty associated with the forecasts. Traffic-flow-management personnel recognize the inherent uncertainty when predicting weather and attempt to account for it through careful cost-benefit decision-making – all in an effort to minimize the airlines’ operating costs while maintaining a high level of safety. The uncertainties incorporated into cost-benefit analyses can best be captured, then, via reliable probabilistic forecast guidance. This suggests that short-term, observations-based, statistical forecast techniques that yield a quantitative measure of uncertainty would be most useful to the aviation industry, especially if such techniques would provide sharp, timely, and reliable probabilistic forecasts of high-impact aviation weather parameters.

A framework and demonstration of a prototype automated system that provides probabilistic forecasts of thunderstorms is presented. The system incorporates NEXRAD Information Dissemination Service (NIDS) radar data, surface mesonet data, Velocity Azimuth Display (VAD) and profiler wind information, as well as Rapid Update Cycle (RUC) analysis fields, as input to a stepwise regression procedure. Output consists of probabilistic forecasts of thunderstorms (composite reflectivity > 40 dBZ) for lead times ranging from 6 min to 4 hrs. The system is capable of providing updates every 6 min, thereby accounting for rapid changes in local conditions. The prototype is composed of an array of grid points centered on the Oklahoma City airport (OKC) in order to capitalize on the high-density, high-frequency observations provided by the Oklahoma surface mesonet and the surrounding network of profilers. The grid-point array covers a domain comparable to the approach-control zone around OKC (i.e., the area within a 60 km radius of the airport). In this manner, air-traffic controllers can obtain high-resolution, rapidly-updated guidance for improving air-traffic flow.

It is expected that this observations-based, statistical system will provide an advancement in two aspects of aviation: 1) reliable, short-term probabilistic forecasts will ameliorate air-traffic congestion during convective events, thereby reducing airline costs and passenger complaints, and 2) short-term guidance on hazardous thunderstorms in the near-vicinity of airports can diminish the risk of accidents, especially for general aviation.