Diagnosing icing severity and supercooled large drop regions within an operational aircraft icing nowcast system using advanced satellite products

The “current icing product” (CIP) algorithm, an operational aircraft icing nowcast system, combines satellite, radar, surface, lightning, and pilot-report observations with model output to create a detailed three-dimensional hourly diagnosis of the probability of aircraft icing conditions. CIP initially determines the locations of clouds and precipitation then estimates the potential for the presence of supercooled liquid water, supercooled large droplets (SLD), and the icing severity within a given airspace. The GOES imager data used within the operational CIP include 3 channels; visible, short-wave IR, and long-wave-IR. These data are used extensively throughout the algorithm. In this study, data from the GOES imager channels in CIP have been replaced with GOES cloud products from the NASA Advanced Satellite Aviation-weather Products (ASAP). The GOES ASAP fields used in this study include effective temperature, liquid and ice water path, effective radius, and cloud phase.

Fuzzy logic membership functions are created for each of the GOES ASAP fields. These membership functions are then combined with additional membership functions from the other CIP data sets based on the meteorological scenario present to produce the icing diagnoses. The NASA ASAP effective temperature and cloud phase products provide insight to the cloud presence and the cloud top microstructure. The liquid and ice water path products provide details about the potential icing severity, while the effective radius improves the SLD diagnosis. This paper summarizes the inclusion of the ASAP data within the CIP algorithm. The fuzzy logic membership functions are defined and their use within CIP is presented through a case study. The results of a verification exercise comparing the operational CIP and the CIP using the ASAP data is also presented.