Blowing snow affects surface transportation by reducing visibility and causing drifting snow on road surfaces. The combination of near-zero visibility and drifting snow can cause unexpected travel difficulties and accidents in remote areas during dangerously cold winter weather situations. Economic impacts of blowing and drifting snow include business disruption through product delivery delays and the inability to travel. To mitigate the effects of drifting snow, road maintenance supervisors must dispatch crews to locations where they can be most effective.

Accurate prediction of blowing and drifting snow would allow businesses, road maintenance, police, and others the ability to plan for these weather conditions and take appropriate actions to prevent economic and human losses due to these events.

Models have been developed to quantify drifting snow at fine spatial and temporal resolution. These models are being modified to determine visibility at the same resolution. The ability of these models to quantitatively predict reduced visibility is examined, and other methods of prediction are explored. This improved visibility information is a component of a decision support system for surface transportation.