A numerical weather prediction based road icing index for informed winter road maintenance and management decision-making

Abstract

Timely and accurate assessment of road surface conditions during winter weather events is critical for ensuring safety, reducing crash rates, and optimizing roadway maintenance activities. To address this need, we propose the Road Icing Index (RII), a new metric that identifies regions with elevated road icing risk based on outputs from numerical weather models such as the Weather Research and Forecasting (WRF) model. The RII integrates multiple atmospheric parameters, including near-surface air temperature (T2), wet-bulb temperature (Tw), precipitation type and rate (QRAIN, QSNOW, RAINNC), relative humidity (RH2), wind speed (U10, V10), and top-layer soil temperature (TLST), into a single spatially explicit metric. These variables are validated by studies such as Tamang et al. (in: On changes of global wet-bulb temperature and snowfall regimes, https://doi.org/10.48550/arXiv.1905.07776) for wet-bulb temperature significance, Stewart et al. (Bull Amer Meteor Soc 96(4):623–639. https://doi.org/10.1175/BAMS-D-14-00032.1, 2015) for precipitation type impacts, and Gustavsson and Bogren (Geogr Ann A 89(4):263–271. https://doi.org/10.1111/j.1468-0459.2007.00325.x, 2007) for road weather modeling. Drawing on established meteorological principles, transportation meteorology research, and operational insights, this paper outlines the RII’s methodological framework, implementation process, and applications. In addition, a use case demonstrates its utility in identifying areas of heightened icing potential, offering practical guidance for road maintenance decision-makers and enhancing traveler safety. Future work aims to incorporate vehicle-based observations, advanced precipitation-type algorithms, and refined parameter weighting to improve forecast accuracy and decision-making.