Exploring GOES-16 Data to Improve Aircraft Icing Diagnoses

The threat for aircraft icing in clouds is a significant hazard that routinely impacts aviation operations. Accurate diagnoses and forecasts of aircraft icing conditions requires identifying the location and vertical distribution of clouds with super-cooled liquid water (SLW) droplets, as well as the characteristics of the droplet size distribution. Traditional forecasting methods rely on guidance from numerical models and conventional observations, neither of which currently resolve cloud properties adequately on the optimal scales needed for aviation. Satellite imagers provide measurements over large areas with high spatial resolution that can be interpreted to identify the locations and characteristics of clouds, including features associated with adverse weather and storms. An algorithm has been developed to infer the aircraft icing threat from theoretically based retrievals of cloud parameters from satellite data. For unobscured low clouds, the icing threat is determined using empirical relationships developed from correlations between satellite imager retrievals of liquid water path and droplet size with icing conditions reported by pilots (PIREPS). For deep ice over water clouds such as those associated with winter storms, ice and liquid water content profiles are derived by using the imager cloud properties to constrain climatological information on cloud vertical structure and water phase obtained apriori from radar and lidar observations, and from cloud model analyses. The embedded super-cooled liquid water contents are then mapped to the icing threat using guidance obtained from an airfoil modeling study. While this strategy provides an unprecedented level of accuracy overall for determining aircraft icing threat estimates at the scales needed by the aviation community, a number of challenges remain that could be addressed with the new Advanced Baseline Imager (ABI) data taken from GOES-16. These include poor instantaneous knowledge of cloud vertical structure and high uncertainties in the satellite cloud retrievals over snow. The ABI data provide spectral signatures that can potentially be used to address these problems which to some degree have previously been demonstrated with MODIS and SEVIRI data. However, the ABI data are unique in that for the first time the required spectral information is obtained with high frequency over the United States where routine pilot reports of icing conditions are available, thus enabling a new testbed to advance and evaluate satellite remote sensing of icing conditions and clouds in general. This paper provides a brief review of our current algorithm, highlighting known problem areas in certain cloud conditions and over snow covered surfaces, and explores the complimentary use of GOES-16 ABI data and icing PIREPS to address these challenges.