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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.

Advancements in numerical weather prediction (NWP) models continue to enhance the quality of in-flight icing forecasts and diagnoses. When diagnosing current in-flight icing conditions, observational datasets are combined with NWP model output to form a more accurate representation of those conditions. Surface observations are heavily relied upon to identify cloud coverage and cloud base height above observing stations. One of the major challenges of using these point-based or otherwise limited observations of cloud properties is extending the influence of the observation to nearby points on the model grid. An alternate solution to the current method for incorporating these point-based observations into the in-flight icing diagnoses was developed. The basis for the new method is rooted in a concept borrowed from signal and image processing known as dithering.

The Current Icing Product (CIP) provides an hourly diagnosis of the severity of icing occurring based on multiple data sources. Pilot reports (PIREPs) and surface observations (METARs), as well as satellite, numerical weather prediction (NWP) model, radar, and lightning data are all utilized within the algorithm. The accurate identification of cloud base is a large factor in the algorithm's determination of icing severity. Current methods employ the METAR observation of ceiling to identify the cloud base over a specified area within the CIP domain. The temperature from the Rapid Update Cycle (RUC) NWP model at the height of the observed METAR ceiling can be utilized as a proxy for the amount of condensate in the cloud. The likelihood of a large amount of condensate in the identified cloud increases with increasing cloud base temperature. As the amount of liquid water diagnosed by CIP severity increases, so does the estimated icing severity.