Initial Tests of an Automated Freezing Drizzle Detection Algorithm applied to ASOS

Freezing drizzle is a significant icing threat to both air and ground transportation. Currently, the Automated Surface Observing System (ASOS) does not report freezing drizzle and the only operational method for reporting it is from an actual human observer. Since freezing drizzle can easily be misidentified as other precipitation (i.e., freezing fog or mist) or missed altogether, it is important to develop a method for automatically detecting occurrences of freezing drizzle. Ramsey (1999) developed an automated method for ASOS using the BF Goodrich freezing rain sensor. This sensor uses a vibrating rod, approximately one inch in length, which is driven at a vibration frequency of 40,000 Hz. When ice accumulates on the rod, the frequency decreases. This change in frequency is directly related to the amount of ice accretion on the sensor and the rate of change of frequency is related to the precipitation rate. When the frequency reaches a pre-set minimum, a heating cycle is triggered to melt the accumulated ice and the measurement process begins anew.

One-minute data from the Buffalo Niagara International Airport (KBUF) ASOS were collected over a nine-year period and the Ramsey algorithm was used to process the data. The resulting automated observations were then compared against manual observations of freezing drizzle from METAR reports. The results of this comparison are given here, highlighted by several case studies.