'Use of a Predictive Winter Storm Severity Index and Comparison of Winter Storm Types'

Winter severity indices (WSIs) have been used by transportation agencies as a retrospective tool to help gauge the severity of a winter season and as an evaluation metric for winter maintenance operations. The literature documenting existing WSIs depicts a myriad of approaches that have been developed for specific state Departments of Transportation. The WSI used by the Nebraska Department of Transportation, known as the Nebraska Winter Severity Index (NEWINS), provides an independent framework to determine the severity of a winter season through categorizing individual winter storms. However, a limitation of NEWINS along with most WSIs is that they are not predictive. A recent development among WSIs to overcome this shortcoming is the development of impacts-based predictive winter storm severity indices, such as the Weather Prediction Center’s Winter Storm Severity Index (WSSI). The WSSI is designed for a broad range of National Weather Service partners and stakeholders, and its forecasts are population-weighted and limited to a 24-h temporal resolution.

This study complements the WSSI by developing an impacts-based predictive winter storm severity index with specific use for transportation agencies, referred to as NEWINS Predictive (NEWINS-P). The overall NEWINS-P framework includes five components: snow severity (NEWINS-S), precipitation type, icing, blowing snow, and drifting snow. The components aim to forecast different in-storm and post-storm winter weather hazards over a 72-h duration at a 6-h resolution, a much finer resolution than the WSSI. The NEWINS-P framework is assessed through spatial forecasts across Nebraska and temporal forecasts at select airport locations on select Colorado Low and Alberta Clipper Systems from the 2018–19, 2020–21, and 2022–23 winter seasons. Additionally, forecast trends are examined in the form of a time series at the selected airport locations to demonstrate local forecast changes in the NEWINS-S component. An evaluation metric is developed to assess the most severe forecast period for each case study. Along with forecast data, observational data are obtained from multiple sources, such as surface weather stations and traffic camera imagery, to verify the forecasts and offer comparisons to forecast trends and storm severity.

The results show that the forecasts of the five components composing the NEWINS-P framework properly resolve the different winter weather hazards at the state and local level. At the state level, the Colorado Low Systems displayed greater forecasted impacts than the Alberta Clipper Systems. Moreover, the Colorado Low Systems produced a higher intensity and spatial coverage of NEWINS-S categories, more types of precipitation, and more blowing and drifting snow concerns than Alberta Clipper Systems. Assessing the NEWINS-S time series for the case studies demonstrated the local forecast changes in the categories between NDFD runs. The NEWINS-P is shown to be a tool that can support transportation agencies in their winter maintenance operations for personnel and resource planning in advance of winter storms. Moreover, this framework is complementary to ongoing conversations regarding initiatives such as Pathfinder.