Freezing precipitation is a significant threat for virtually all forms of transportation and represents a serious public safety problem. In particular, icing on airframes reduces the ascending force of aircrafts and causes severe risks to flight operations. Because accurate predictions for these natural hazards are of primary importance, many operational forecast algorithms currently need to be improved. For example, statistically-tuned icing algorithms (Thompson et al., 1997: WAF,12,878) ), use nonphysical relationships which results in overforecasting icing threats. On the other hand, the Canadian operational scheme proposed by Tremblay et al., 1996: WAF, 11, 66) is physically-based but only provides binary yes/no forecasts for icing events and doesn’t give any information for icing intensity. Actual freezing precipitation forecast techniques only address the classical melting ice mechanism (presence of a warm layer aloft) although the nonclassical formation of freezing drizzle (without a warm layer) is commonly observed (Strapp et al., 1996: FAA Int. Conf. on Aircraft In-flight Icing). In an attempt to improve icing and freezing precipitation forecasts, a new mixed-phase cloud scheme has been developed (Tremblay et al., 1996: Tellus, 48A, 483). This scheme has the advantage of being computationally fast and easy to implement and has an operational potential. The scheme has been extensively tested by simulating virtually all ice storms during the 96-97 winter season. The scheme has also been run in a quasi-operational mode for the whole 97-98 winter season to provide forecasts and guidance for research aircraft flight trajectories during the second Canadian Freezing Drizzle Experiment (Isaac et al., 1998: AMS Conf. Cloud Phys., Everett, Wash.). Finally, the scheme was used as a real-time guidance for the Canadian Convair research aircraft missions over the Arctic Sea during the Canadian phase of the FIRE III experiment (April 6 – May 1st 1998).
A brief description of the scheme will first be presented. Typical results from the 96-97 winter simulations will then be discussed to show typical forecast products. Quantitative icing and freezing precipitation forecasts are possible with the new scheme and several examples will be given. Furthermore, both qualitative and quantitative comparisons between simulated and commonly observed meteorological parameters will be described. Finally, it will be demonstrated that the inclusion of a non-classical freezing precipitation mechanism in the forecast algorithm significantly improve both the detection and the bias score for freezing precipitation. These results suggest that the scheme is potentially a useful tool for the prediction of supercooled large droplets.
The 8th Conference on Aviation, Range, and Aerospace Meteorology