Implementation of RWIS in New Hampshire

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Thursday, 2 February 2006: 11:30 AM
Implementation of RWIS in New Hampshire
A412 (Georgia World Congress Center)
Brendon Hoch, Plymouth State Univ., Plymouth, NH; and J. P. Koermer, W. L. Real, and S. W. Gray

Presentation PDF (149.1 kB)

The New Hampshire Department of Transportation (NHDOT) and the Judd Gregg Meteorology Institute (JGMI) of Plymouth State University (PSU) are collaborating on the deployment of a network Road Weather Information Systems (RWIS). The initial deployment will place 11 stations across the state of New Hampshire along interstate and state highways, with site selection performed in coordination with NHDOT Maintenance District Engineers in order to maximize the impact of data during the pavement maintenance decision process. Each RWIS station will house a suite of meteorological instruments designed to determine current temperature, moisture, and wind conditions. Sensors embedded in the road surface near each station will provide data regarding pavement temperature and will detect melt chemicals applied to the surface. Additionally, ozone sensors manufactured by the University of New Hampshire (UNH) will provide additional data to enhance air quality monitoring throughout the state. Data will be collected from each station from a central server which will connect to each station via NHDOT intranet, cellular modem, or satellite transmission. The central server will feature software which will allow NHDOT personnel to access RWIS data via the web, as well as real time pavement forecasts provided by the vendor. The server will also transfer data to the National Oceanic and Atmospheric Administration's (NOAA) Forecast Systems Laboratory (FSL) Meteorological Assimilation Data Ingest System (MADIS), as well as to JGMI/PSU where it will be used by faculty and graduate students to develop and enhance forecasts for the state. It is envisioned that data will eventually be coupled to a Maintenance Decision Support System (MDSS) and mesoscale numerical weather prediction tools such as the Weather Research and Forecast (WRF) model.