An Intercomparison of Mesoscale Forecasts of Aircraft Icing Using SSM/I Retrievals

Abstract A technique for the detection of supercooled liquid water (SLW) from Special Sensor Microwave/ (SSM/I) data is discussed. For this study, these SLW retrievals depict areas of icing that are used to compare against different aircraft icing forecast algorithms. It is shown that currently used automated algorithms give incorrect distributions of SLW events with temperature and include systematically glaciated clouds in icing forecasts. This problem is eliminated when an explicit SLW scheme is used. With this scheme the Canadian forecast system can detect roughly 75% of the SLW signal within an accuracy of 100 km when compared to SSM/I retrievals. The scheme also detects 99% of nonicing events within the same accuracy.

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