A blowing and drifting snow algorithm supporting winter road maintenance decision making

The occurrence of blowing and drifting snow presents a significant transportation safety concern due to the reduction of driver visibility and roadway mobility. The management of blowing and drifting snow onto and across roadways is also a major expense to highway maintenance operations. In lieu of widespread mechanical blowing and drifting snow suppression through the use of snow fences and special roadway designs, the use of computational methods is required to provide guidance on the degree of blowing and drifting snow that can be expected during winter weather conditions. The need for this computational support within a maintenance decision support environment has been identified as a priority research area by a state pooled fund research program involving Maintenance Decision Support System (MDSS) development.

The University of North Dakota has developed a two-dimensional road environment snow transport model to support MDSS efforts. The model builds upon research in blowing and drifting snow simulation and prediction found within the snow pack hydrology community in order to transfer the technologies to a roadway environment. The present model includes a robust atmospheric sciences physics package closely coupled with a mesoscale weather prediction model to simulate blowing and drifting snow conditions. This effort is being tested during winter 2005-06 MDSS field experiments across the Upper Midwest. A field validation effort is being conducted to measure the effectiveness of the blowing and drifting snow algorithm in supporting maintenance decision making.

This paper describes the framework of the roadway blowing and drifting snow model. This framework includes a mesoscale ensemble weather prediction model downscaled to a fine-resolution grid across the study domain. The downscaled data is coupled at each grid point to a two-dimensional blowing snow model that provides snow mass flux products used to generate visibility and snow drift estimates. The paper includes preliminary results of field tests of the algorithm conducted as part of the ongoing state pooled fund research MDSS field trials.