Aircraft Icing Study Using Integrated Observations and Model Data

AbstractLight (LGT) to moderate (MOD) aircraft icing (AI) is frequently reported at Cold Lake (CL), Alberta, but forecasting AI has been a big challenge. The purpose of this study is to investigate and understand the weather conditions associated with AI based on observations in order to improve the icing forecast. To achieve this goal, Environment and Climate Change Canada in cooperation with the Department of National Defense deployed a number of ground-based instruments that include a Microwave Radiometer, a Ceilometer, Disdrometers, and conventional present weather sensors at the CL airport (CYOD). A number of pilot reports (PIREPs) of icing at CL during the 2016-2017 winter periods and associated observation data are examined. Most of the AI events were LGT (76%) followed by MOD (20%) and occurred during landing and takeoff at relatively warm temperatures. Two AI intensity algorithms have been tested based on an ice accumulation rate (IAR) assuming a cylindrical shape moving with airspeed (νa) of 60 ...