Differentiating Freezing Drizzle And Freezing Rain In Hrrr Model Forecasts

Significance Statement Aircraft icing is a hazardous condition that can lead to aviation accidents. We are working to improve forecasts of when aircraft icing conditions occur, especially with respect to a special type of icing condition that occurs from larger supercooled liquid drops that can more detrimentally impact some aircraft than icing from small drops. We investigated a method to distinguish two types of large drop icing in operational forecast models: freezing drizzle and freezing rain. These findings suggest that forecast models are capable of distinguishing these two types of icing, which can provide valuable information to aviation weather forecasters. However, there are improvements that still need to be made to improve how often models accurately predict supercooled large drop icing.Supercooled large drop (SLD) icing poses a unique hazard for aircraft and has resulted in new regulations regarding aircraft certification to fly in regions of known or forecast SLD icing conditions. The new regulations define two SLD icing categories based upon the maximum supercooled liquid water drop diameter (Dmax): freezing drizzle (100-500 mu m) and freezing rain (>500 mu m). Recent upgrades to U.S. operational numerical weather prediction models lay a foundation to provide more relevant aircraft icing guidance including the potential to predict explicit drop size. The primary focus of this paper is to evaluate a proposed method for estimating the maximum drop size from model forecast data to differentiate freezing drizzle from freezing rain conditions. Using in situ cloud microphysical measurements collected in icing conditions during two field campaigns between January and March 2017, this study shows that the High-Resolution Rapid Refresh model is capable of distinguishing SLD icing categories of freezing drizzle and freezing rain using a Dmax extracted from the rain category of the microphysics output. It is shown that the extracted Dmax from the model correctly predicted the observed SLD icing category as much as 99% of the time when the HRRR accurately forecast SLD conditions; however, performance varied by the method to define Dmax and by the field campaign dataset used for verification.