Performance analysis of the current icing product (CIP) algorithm under Variations in icing relevant observational datasets

The Current Icing Product (CIP) uses five observational datasets in addition to numerical weather prediction (NWP) model data to diagnose a three-dimensional icing probability and severity. By default, there are thresholds that limit the age of the observational data that CIP uses. If the data time does not meet the threshold and it is a required dataset (surface observations, satellite), then CIP will not run. By utilizing the probability of detection (POD) value of only the positive icing pilot reports (PODy) for both of the required input datasets, it was found that the current thresholds are adequate. Satellite data that are 30 minutes old produce a PODy of 0.82, but that drops to 0.78 when the age of the satellite data reaches 120 minutes. Similar results were found using surface observation data ranging from 60 to 180 minutes old. Understanding the behavior and performance of CIP when it is required to use less than optimal input data is crucial for making improvements to the algorithm and testing additional datasets. A methodology is presented to assist with these tasks in the future, as well as a more in depth look at the effect of varying input dataset age has on PODy of both CIP icing probability and severity.