Extended Abstract (92K)12A.3
A quality control algorithm for the ASOS ice free wind sensor
Chester V. Schmitt IV, NOAA/NWS, Silver Spring, MD
The Vaisala NWS 425 sonic anemometer was first installed on ASOS in late 2005 to replace the Belfort Model 2000 cup and vane anemometer, which had a tendency to freeze during icing weather events. Installation of the Vaisala NWS 425, also known as the Ice Free Wind Sensor (IFWS), continued in earnest during 2006 into 2007. However, problems began to surface in 2007 resulting in numerous missing and erroneous wind reports. Further investigation revealed that the problems were the result of birds landing on the sensor. Bird activity generates air currents around the sensor while the presence of the bird often results in a blockage of the paths between the transducers. Additional problems were observed during the 2007-08 winter season due to snow and ice build-up on the sensor. The build-up was the result of the failure of the sensor's internal heater which was caused by either power failure at the site during winter storms or failure of the heater control circuitry in some units. When bird activity or ice build-up occurs, the symptom is similar: the path blockage results in missing or erroneous data. The most remarkable form that the erroneous data takes is an unrealistically high wind gust report. Bogus wind gust observations in excess of 100 knots are not uncommon. In addition to birds and ice build-up, additional factors may result in missing or erroneous IFWS data such as insects or blowing debris in the sample volume, e.g., leaves or needle grass. Erroneous wind data raises concerns over aircraft operations and safety, the quality of real-time meteorological data used by meteorologists and as input into numerical models, and the integrity of the climate record. To mitigate the generation of erroneous wind observations, meteorologists at the National Weather Service have developed a robust quality control algorithm. The algorithm scrutinizes data from the IFWS at its most fundamental level- the 5-second data. Data that are found to be suspect are flagged and achieved, but are not used in the generation of ASOS wind reports. A description of the algorithm is given along with the results of the testing of the algorithm using over 4000 hours of real world data.
Session 12A, Surface Measurements and Trends—I
Thursday, 15 January 2009, 8:30 AM-9:45 AM, Room 130
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