A Comparison of Forecast Icing Product Based Cloud Identification Using the HRRR and the RAP

Forecasts of in-flight icing from the Forecast Icing Product (FIP) rely on the accurate depiction of clouds. The FIP uses physically based fuzzy logic techniques applied to WRF Rapid Refresh (RAP) model forecasts to locate the bases, tops, and layers associated with clouds and subsequently the potential for in-flight icing conditions. These techniques were originally developed on the Rapid Update Cycle (RUC) model and have since been updated, evolving in parallel with the RUC and RAP models. The newest rapidly updating model, the High Resolution Rapid Refresh (HRRR) model, provides forecasts with finer horizontal grid spacing (3 km) than previous models used by the FIP. Understanding the behavior of the underlying fuzzy logic techniques employed by the FIP at this grid spacing is critical to transitioning toward higher resolution forecasts of in-flight icing conditions. Specifically, comparing the interest maps currently used for identifying clouds and their attributes in the FIP is a major focus of this work. As a method of comparison, soundings will be used to evaluate the cloud identification capabilities of the FIP over a winter season. Satellite and METAR data will also be utilized to augment the sounding data in estimating cloud tops and bases, respectively. Evaluation of the cloud top height, cloud base height, and cloud layer components of the FIP applied to both HRRR and RAP forecasts will be presented.

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.