AvMet Applications, Inc. — National Aeronautics and Space Administration SBIR Phase I: A3

Description

Unanticipated adverse path-based wind shear can significantly affect airspeed resulting in non-optimal arrival flows into an airport or possibly result in a loss of separation known as wind compression. Stronger than expected tailwinds result in more aircraft entering airspace than expected or manageable ndash; which results in sector saturation, holding, and reverting to Miles-In-Trail (MIT). Whereas stronger than expected headwinds results in fewer aircraft entering the airspace than expected, introduces unrecoverable delay and underutilizes airport capacity. Without accurate, clear, and operationally relevant prediction of accurate wind profiles in the terminal and arrival airspace, significant impacts may occur or, at a minimum, non-optimal operations may develop as air traffic operators are caught by surprise by the unexpected wind profiles impacting arrival flows.This proposal provides an accuracy-adjusted wind profile data set focusing on the terminal airspace and arrival area which is based on in-situ observation, upper-level wind forecasts, machine learning, and artificial intelligence that can be used for wind compression alerts and arrival flow planning. Using machine learning and artificial intelligence, this research uses all available airborne aircraft flight paths to calculate the wind profile throughout the ascent and descent into an airportrsquo;s airspace to improve the wind forecast. This unique approach using passive participation by aircraft (i.e., no aircraft wind measurement equipment or communication of winds required) will help to create a more accurate and high-resolution wind profile for the atmosphere near the airport and along the approach paths which can be used for flow optimization and mitigation of other impacts to aviation due to winds.