Comparing Road and Bridge Surface Temperatures During the Winter Season in Ohio

Unfavorable road conditions due to winter weather have major impacts throughout the United States. Road management agencies deploy significant resources to reduce the risks associated with winter precipitation. One of the key factors in their decision-making is road surface temperature. While direct observations of surface temperature are collected in many states, station coverage is sparse, and sensors tend to be located primarily on major highways. To allow for more universal monitoring and analysis of winter road conditions, NSSL created a probabilistic model that predicts the likelihood of sub-freezing roads within the U.S. This model provides important information that can aid communicative efforts during inclement road weather conditions. However, one major caveat of this model is that it does not differentiate between road and bridge surfaces, and bridges are commonly known to ice before roads do. This study investigates how road and bridge surface temperatures differ throughout the 2022-2023 winter season using hourly road sensor observations from Road Weather Information System (RWIS) stations in Ohio. Air temperature readings from RWIS towers are used to compare how bridge and road surface temperatures behave in cold environments. Results suggest that roads are typically warmer than bridges when both surface temperatures are below/near freezing. Additionally, if only one surface is below freezing, it is most likely to be the bridge rather than the road. Differences between air and surface temperatures also provide some evidence that suggests surface temperatures on bridges act differently than surface temperatures on roads. However, in environments near freezing (0 deg C), both road and bridge surfaces are typically 1-3 deg C warmer than the air temperature. Since roads and bridges behave similarly near 0 deg C, this suggests that a separate model for bridges may not be necessary as these small deviations will unlikely result in significant changes to the probabilistic output of the model. However, these findings can be helpful for forecasters to understand how to better communicate risk to the public during hazardous road conditions in near 0 deg C environments.