Inferring the Presence of Freezing Drizzle using Archived Data from the Automated Surface Observing System (ASOS)

Abstract

In its current form, the Automated Surface Observing System (ASOS) provides automated precipitation type reports of rain, snow, and freezing rain. Unknown precipitation can also be reported when the system recognizes precipitation is occurring but cannot classify it. A new method has been developed that can reprocess the raw ASOS one-minute observation (OMO) data to infer the presence of freezing drizzle. This Freezing Drizzle Derivation Algorithm (FDDA) was designed to identify past freezing drizzle events that could be used for aviation product development and evaluation (e.g. Doppler radar Hydrometeor Classification Algorithms, improved numerical modeling methods, etc.) and impact studies that utilize archived datasets (e.g. NTSB investigations of transportation accidents where freezing drizzle may have played a role).Ten years of archived OMO data (2005 – 2014) from all ASOS sites across the conterminous United States were reprocessed using the FDDA. Meteorological Terminal Aviation Routine (METAR) weather reports from human-augmented ASOS observations were used to evaluate and quantify the FDDA’s ability to infer freezing drizzle conditions. Advantages and drawbacks to the methodology are discussed. This method is not intended to be used as a real-time situational awareness tool for detecting freezing drizzle conditions at the ASOS, but rather to determine periods where freezing drizzle may have impacted transportation, with an emphasis on aviation, and to highlight the need for improved observations from the ASOS.