Snow and ice monitoring technique for the contaminated runway

摘要

Braking action classifies runway slipperiness on the runway according to the coefficient of friction, and it is an important indicator for aircraft operations in winter. On the other hand, according to International Civil Aviation Organization's (ICAO) new methodology for assessing and reporting runway surface conditions from 2020, slipperiness is determined by the snow and ice conditions (type and thickness) of runway surfaces. However, there is no sensor that can measure snow and ice conditions on runway surfaces. Consequently, the Japan Aerospace Exploration Agency (JAXA) and other collaborators are developing a runway embedded sensor, the Ground Laser Sensor for Snow Monitoring (GLASS), which identifies the type and depth of snow and ice by light scattering and AI. This paper describes the results of the third generation GLASS3 demonstration. Summarily, GLASS3 identified 5 types of snow at the precisions of 86% and the thickness of snow with a mean absolute error of 3.1 mm. We will continue to improve the estimation accuracy of the snow and ice conditions, and we will have a demonstration on the runway in early 2020 by collaborating with airline and civil aviation authorities.