1332 Investigation of the Forecast Icing Product Supercooled Large Droplet Potential Algorithm during Select Cases from the In-Cloud Icing and Large Drop Experiment

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
Daniel R. Adriaansen, NCAR, Boulder, CO; and J. A. Haggerty, A. Rugg, S. Tessendorf, A. Korolev, and M. Wolde

An unprecedented data set focused on Supercooled Large Droplet (SLD) icing conditions was collected during the In Cloud Icing and Large drop Experiment (ICICLE). These SLD icing conditions pose a considerable risk to aircraft, and are the basis for new Federal Aviation Administration (FAA) certification requirements for aircraft manufacturers. Forecasting SLD icing conditions, especially aloft, remains particularly challenging. The operational Forecast Icing Product (FIP) algorithm produces forecasts of SLD icing areas, using Numerical Weather Prediction (NWP) model forecasts with a horizontal grid spacing of 13 km as its input. The SLD icing potential forecasts from the FIP are based on a combination of physically-based processes, explicit NWP model forecast data, and meteorological conditions (weather scenarios). Current research is exploring new approaches to SLD forecasting using the ICICLE data set.

During ICICLE, a new experimental version of the FIP algorithm was available that used the High Resolution Rapid Refresh (HRRR) NWP model as input with a horizontal grid spacing of 3 km. This experimental FIP algorithm was simply the current operational FIP algorithm with only minor adjustments to facilitate the use of the HRRR NWP model as input (i.e., no modifications to the algorithmic parameters were made to this experimental version). The experimental SLD icing forecasts provided much finer detail than the currently operational forecasts but have not yet been evaluated in detail. The dataset from ICICLE completely characterized the drop size distribution, as well as the meteorological conditions governing the SLD droplet growth environment in many cases and will support multiple types of evaluation. In this study, information from ICICLE will be matched with FIP SLD icing forecasts from both the experimental and operational versions to better understand the abilities of the FIP algorithm at different horizontal grid spacing. Performance of the FIP SLD icing algorithm compared to ICICLE observations will be used to drive future development decisions with the goal of improving forecasts of SLD icing conditions.

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

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