The design of a quality control algorithm for environmental sensor stations

The use of environmental sensor stations (ESS) to monitor atmospheric and road conditions has flourished in the United States over the past thirty years. At present over 2,500 ESS installations exist nationwide. These ESS provide important information to the transportation and surface transportation weather service provider communities in support of roadway maintenance. In recent years the quality of the data from ESS has been called into question, particularly those data associated with monitoring pavement conditions. Unfortunately, while effective techniques exist for performing quality control of the atmospheric sensors of the ESS, no effective operational technique exists for quality control of pavement sensor data.

The paper describes research underway at the University of North Dakota to advance solutions for effective operational quality control techniques of ESS pavement sensor data. The paper provides a compilation of known quality control techniques and extrapolates theoretical best fit techniques for pavement sensors. These methods include but are not limited to techniques involving Bayesian methods, a complex quality control method developed by Gandin, and variational methods of quality control. The preliminary results of the comparison of these techniques for data collect at a dedicated road weather field research facility are presented. This research facility was established in 2004 by the University of North Dakota Surface Transportation Weather Research Center to support university road weather research. The site includes numerous meteorological instruments as well as sensors for monitoring road and roadway environment conditions. The instrumentation includes temperature, relative humidity and wind probes at 2-, 5-, 10-, and 15-meters, multiple precision precipitation measurement systems, an array of ultra-sonic snow depth sensors, and various pavement and sub-pavement sensors. The pavement sensors perform measurements of temperature, freeze point determination, and condition (dry, wet, etc) of the road surface.