An Optimized Probabilistic Prediction Method for Aircraft Icing Potential Based on a Convection-Allowing Model

In this paper, a model-based aircraft icing potential probability prediction method is optimized. The algorithm is mainly based on the temperature and humidity output from a convection-allowing model, which can output the aircraft icing potential with high temporal and spatial resolution fitting the aircraft icing situation in China. Compared with the original algorithm, it is optimized from three aspects: cloud layer judgment, convective cloud aircraft icing, and the membership functions. First, the optimized algorithm adopts membership functions that are more suitable for aircraft icing accumulation in China. Second, the variation in the relative humidity threshold with height is used to judge cloud layers, which effectively decreases the missing reports of low- and high-level clouds and the false alarms of middle-level clouds. Third, the prediction of icing conditions in convective clouds is established separately to further improve the prediction accuracy of the icing potential algorithm. Systematically verifying nearly 600 pilot reports of domestic aircraft icing shows that the prediction effect of the optimized aircraft icing potential prediction method is better than that of the original prediction method and the traditional IC algorithm in the high and low threshold ranges. Verified by three cases of aircraft icing detection in central and southern Shanxi Province in March 2020, it is accurate in predicting the presence of icing conditions and consistent with the actual observation results in terms of icing intensity.