Over a Quarter-Century of Aircraft Icing Diagnoses and Forecasts (Invited Presentation)

A brief history of automated algorithms for diagnosing and predicting aircraft icing will be presented. The earliest forecast algorithms were simple combinations of temperature and relative humidity variables retrieved from numerical weather prediction models, while diagnosis of existing conditions was similarly done from surface observations and/or rawinsonde data. In some ways, those methods remain in place today although refinements using satellite, radar, lightning, and other data have improved their accuracy. Similarly, better weather prediction models using more sophisticated physical parameterizations have partially replaced the generic temperature and humidity variables for predicting aircraft icing with even greater accuracy. Future aircraft icing analyses and forecasts hold even greater potential due to the arrival of sufficient computer power to assimilate highly detailed observations (e.g., GOES‑R and polarimetric NEXRAD) as well as simulate icing at relatively fine scales at sub-hourly temporal resolution for a day or longer. A glimpse at these future icing simulations will be presented in the context of the recent FAA‑funded In-Cloud ICing and Large drop Experiment (ICICLE).

This research is in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA.