The Potential Role for Cloud-Scale Numerical Weather Prediction for Terminal Area Planning and Scheduling

A number of operations in the aviation industry, particularly in the terminal area, are weather-sensitive to local conditions in the short-term (3 to 18 hours). Often, they are reactive due to unavailability of appropriate predicted data at the required temporal and spatial scale. Hence, whatever planning that may be applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or the results of synoptic- to meso-beta-scale weather models. Since this time range is beyond what is feasible with modern now-casting techniques, near-real-time assessment of observations of current weather conditions may have the appropriate geographic locality, by its very nature is only directly suitable for reactive response.

According to the Air Transportation Association, air traffic delays caused by weather cost the airlines about $4.2B in 2000, of which $1.3B was estimated to be avoidable. Hence, meso-gamma-scale numerical weather models operating at higher resolution in space and time with more detailed physics may offer greater precision and accuracy within a limited geographic region such as a terminal area, for problems with short-term weather sensitivity (e.g., Mass et al, 2002; Gall and Shapiro, 2000).

Conceptually, improvements in the quality and lead-time of local weather forecasts derived from such models could enable air traffic controllers and dispatchers to develop more effective alternative flight paths to reroute aircraft around hazardous weather. Airline officials could initiate recovery plans before weather-induced disruptions actually occur, rescheduling passengers and aircraft in affected areas, thereby improving safety and efficiency. Airport terminal operators could more efficiently schedule and staff aircraft deicing and snow removal operations during the winter (e.g., Changnon, 2003 and Dutton, 2002).

Many of these ideas were recognized in the past, although practical deployment with a sufficient balance of physics and throughput has been limited until recently. For example, Carpenter et al 1999 discusses the use of the Advanced Regional Prediction System (ARPS) to support airport terminal operations. They implemented nested forecasts at 27, 9 and 3 km resolution focusing on specific large airports in the midwestern United States.