A Comparison of an Automated Freezing Drizzle Algorithm to Human Observations

Rasmussen et. al, 2006 identified freezing drizzle as a serious ground-icing hazard to aviation capable of causing serious damage to aircraft engines. While techniques have been developed to prevent engine damage during freezing drizzle conditions, it is necessary to properly identify freezing drizzle in a timely manner to implement them. Since the Automated Surface Observing System (ASOS) does not report freezing drizzle, any METAR reports of freezing drizzle are augmented by human observers. Typically, human observations are only performed once an hour which may lead to large lag times in identifying changing conditions. Freezing drizzle in particular can be difficult to identify because it can easily be mistaken for mist or freezing fog.

An automated freezing drizzle algorithm was proposed by Ramsey, 1999 that uses the ASOS freezing rain sensor in combination with other sensors to more accurately identify time periods of freezing drizzle. This algorithm was used to process the 1-minute data collected from the ASOS at Denver International Airport (KDEN), Chicago O'hare (KORD), and Pittsburgh (KPIT) over a nine year period. METAR reports of freezing drizzle from these sites were also collected for the same time period. A comparison of the METAR reports to the automated reports are presented along with statistics showing whether the algorithm is reporting more or less occurrences of freezing drizzle with respect to the observers.