Aircraft Icing Study Using Integrated Observations and Model Data

Faisal S. Boudala

doi:10.1175/WAF-D-18-0037.1

Aircraft Icing Study Using Integrated Observations and Model Data

2019-05-02

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AI

DOI：10.1175/WAF-D-18-0037.1

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Light (LGT) to moderate (MOD) aircraft icing (AI) is frequently reported at Cold Lake, 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 Defence deployed a number of ground-based instruments that include a microwave radiometer, a ceilometer, disdrometers, and conventional present weather sensors at the Cold Lake airport (CYOD). A number of pilot reports (PIREPs) of icing at Cold Lake during the 2016/17 winter period 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 upsilon(a) of 60 and 89.4 m s(-1), and the Canadian numerical weather prediction model forecasts. It was found that the algorithms IAR(2) with upsilon(a) = 89.4 m s(-1) and IAR(1) with upsilon(a) = 60 m s(-1) underestimated (overestimated) the LGT (MOD) icing events, respectively. The algorithm IAR(2) with upsilon(a) = 60 m s(-1) appeared to be more suitable for forecasting LGT icing. Over all, the hit rate score was 0.33 for the 1200 UTC model run and 0.6 for 0000 UTC run for both algorithms, but based on the individual icing intensity scores, the IAR(2) did better than IAR(1) for forecasting LGT icing events.

Keywords:

Instrumentation

sensors

Profilers

atmospheric

Clouds

Model evaluation

performance

Numerical weather prediction

forecasting

Icing

Aircraft Icing Study Using Integrated Observations and Model Data

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