Enhancement of the daytime MODIS based aircraft icing potential algorithm using mesoscale model data

Abstract

In this thesis, MM5 mesoscale model data are examined to determine its utility in enhancing satellite based aircraft icing analysis. The algorithm by Alexander (2005) was used to process MODIS imagery on four separate storms in January 2006, and his algorithm was validated using 133 positive and negative pilot reports (PIREPs). MM5 mesoscale model soundings were then analyzed to determine the temperature (T) and dewpoint temperature (Td) at the altitude and location of each PIREP. Relative humidity (RH) was calculated, and fuzzy logic used to determine the aircraft icing potential associated with the T and RH model based parameters through the use of operational Current Icing Potential (CIP) T and RH interest maps, and the T interest map used in Alexander's algorithm. Model icing potential was calculated using 16 different methods, and it was found that weighting RH more in the calculation added the most value to the MODIS based algorithm. It was also found that the Alexander's T interest map added value to the MODIS based algorithm in every case, while the CIP based T interest map only added value when RH was weighted higher.