Improving Diagnoses of In-Flight Icing Conditions in Regions of Sparsely Distributed Surface Observations

The accurate diagnosis of in-flight icing conditions is dependent on surface observations of cloud coverage, cloud base height, and surface precipitation type. However, the network for collecting these data over the the United States is neither contiguous nor evenly distributed. Surface observational gaps exist over much of the domain where in-flight icing conditions are diagnosed. Due to the way these observations are treated when diagnosing inflight icing conditions, the result is often circles where icing conditions are possible next to areas with no icing conditions diagnosed due to absence of surface observations. To avoid these visually unappealing and scientifically inconsistent artifacts, a method was developed to create surrogate surface observation data from numerical weather prediction model output. Using the individual condensate fields, accumulated precipitation, and temperature all three of the required datasets used from surface observations in diagnosing in-flight icing conditions were derived. The Current Icing Product (CIP) shows in-flight icing diagnoses created with the model derived surface observations that are similar to those created when using only real observations.