Significant Updates for the Current Icing Product (CIP) and Forecast Icing Product (FIP) Following the 2019 In-Cloud Icing and Large-Drop Experiment (ICICLE)

Event
International Conference on Icing of Aircraft, Engines, and Structures
Authors Abstract
Content
The Current Icing Product (CIP; Bernstein et al. 2005) and Forecast Icing Product (FIP; Wolff et al. 2009) were originally developed by the United States’ National Center for Atmospheric Research (NCAR) under sponsorship of the Federal Aviation Administration (FAA) in the mid 2000’s and provide operational icing guidance to users through the NOAA Aviation Weather Center (AWC). The current operational version of FIP uses the Rapid Refresh (RAP; Benjamin et al. 2016) numerical weather prediction (NWP) model to provide hourly forecasts of Icing Probability, Icing Severity, and Supercooled Large Drop (SLD) Potential. Forecasts are provided out to 18 hours over the Contiguous United States (CONUS) at 15 flight levels between 1,000 ft and FL290, inclusive, and at a 13-km horizontal resolution. CIP provides similar hourly output on the same grid, but utilizes geostationary satellite data, ground-based radar data, Meteorological Terminal Air Reports (METARS), lightning data, and voice pilot reports (PIREPs) in addition to the RAP model output to provide near-realtime icing guidance. This paper presents recent enhancements to the prototype versions of CIP and FIP (CIP v2.0 and FIP v2.0, respectively). The enhancements described are intended to take better advantage of enhanced model resolution and microphysics parameterization as well as state-of-the-art observations for icing diagnosis and forecasting.
Meta TagsDetails
DOI
https://doi.org/10.4271/2023-01-1487
Pages
15
Citation
Rugg, A., Haggerty, J., Adriaansen, D., Serke, D. et al., "Significant Updates for the Current Icing Product (CIP) and Forecast Icing Product (FIP) Following the 2019 In-Cloud Icing and Large-Drop Experiment (ICICLE)," Advances and Current Practices in Mobility 6(3):1363-1372, 2024, https://doi.org/10.4271/2023-01-1487.
Additional Details
Publisher
Published
Jun 15, 2023
Product Code
2023-01-1487
Content Type
Journal Article
Language
English