89th American Meteorological Society Annual Meeting

Sunday, 11 January 2009
New England Winter Severity Indices
Phoenix Convention Center
Anthony R. Fusco, Plymouth State University, Plymouth, NH
Severity indices are designed to help warn people of dangerous atmospheric conditions. An example is the heat index which forecasts the likelihood of oppressive heat. Winter severity indices (WSIs) are a relatively new type of prediction tool. WSIs essentially quantify the combined effects of weather conditions and forecast the need for maintenance operations.

Winter severity indices (WSIs) were developed for five maintenance regions in the state of Maine. Originally, indices were to be formulated for all New England states' maintenance districts. Indices were designed using New England climate zones identified by Webb (2007). Previous studies had suggested the use of specific climate regions to help design WSIs. The proposal for this research was approved and funded by the New England Transportation Consortium (NETC).

Five years (2001-2006) of climate data were used from over 300 automated and observer weather sites from across New England to develop the WSIs. Climate data included temperature, snowfall, freezing rain, frost and blowing snow. Average winter conditions were obtained at climate zone centroids. These values were then interpolated to maintenance districts through areal distribution analysis. A total of 10,652 different equations were developed from the interpolated climate data, various percent weight assignments and critical values of each index term. Transportation maintenance cost data were requested from all New England state Departments of Transportation (DOT) offices. The data were needed to help validate the WSIs. Data requested included labor, equipment and road chemical costs as well as usage rates of road chemicals. Linear regression analysis determined which of the 10,652 equations best forecast costs and usage by comparing index outputs and transportation maintenance data. Correlation coefficients were generated to establish the best predictor of costs and usage.

The equations that best forecast costs and usage for the state of Maine had correlation coefficients that ranged from 0.5018 to 0.6787 with one outlier of 0.3893 for one of the maintenance regions. These are found throughout the five maintenance regions. The validation of the other New England states' WSIs was not possible without the transportation maintenance data requested.

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