Intercomparison of In-Flight Icing Algorithms. Part II: Statistical Verification Results

Abstract

Recent research to improve forecasts of in-flight icing conditions has involved the development of algorithms to apply to the output of numerical weather prediction models. The abilities of several of these algorithms to predict icing conditions, as verified by pilot reports (PIREPs), are compared for two numerical weather prediction models (Eta and the Mesoscale Analysis and Prediction System) for the Winter Icing and Storms Program 1994 (WISP94) time period (25 January?25 March 1994). Algorithms included in the comparison were developed by the National Aviation Weather Advisory Unit [NAWAU, now the Aviation Weather Center (AWC)], the National Center for Atmospheric Research?s Research Applications Program (RAP), and the U.S. Air Force. Operational icing forecasts (AIRMETs) issued by NAWAU for the same time period are evaluated to provide a standard of comparison. The capabilities of the Eta Model?s explicit cloud liquid water estimates for identifying icing regions are also evaluated and compared to the algorithm results. Because PIREPs are not systematic and are biased toward positive reports, it is difficult to estimate standard verification parameters related to overforecasting (e.g., false alarm ratio). Methods are developed to compensate for these attributes of the PIREPs. The primary verification statistics computed include the probability of detection (POD) of yes and no reports, and the areal and volume extent of the forecast region. None of the individual algorithms were able to obtain both a higher POD and a smaller area than any other algorithm; increases in POD are associated in all cases with increases in area. The RAP algorithm provides additional information by attempting to identify the physical mechanisms associated with the forecast icing conditions. One component of the RAP algorithm, which is designed to detect and forecast icing in regions of?warm? stratiform clouds, is more efficient at detecting icing than the other components. Cloud liquid water shows promise for development as a predictor of icing conditions, with detection rates of 30% or more in this initial study. AIRMETs were able to detect approximately the same percentage of icing reports as the algorithms, but with somewhat smaller forecast areas and somewhat larger forecast volumes on average. The algorithms are able to provide guidance with characteristics that are similar to the AIRMETs and should be useful in their formulation.