Development of a Black Ice Prediction Model for Emergency Preparedness and Response

Black ice is a leading cause of meteorologically related fatalities throughout the United States, surpassing even the combined values of yearly averaged tornado and flood related fatalities. Black ice is a thin layer of ice on a pavement surface resulting from light, wintry precipitation, frozen precipitation melt off, freezing fog, or hoar frost. Typically, drivers are unaware of the presence of black ice due to the clear nature of ice; therefore, providing drivers with adequate warning is essential to the mitigation of societal detriments resulting from this phenomenon. The Oklahoma Department of Transportation (ODOT) has funded an interdisciplinary effort to develop a multi-faceted black ice prediction and warning network across the state of Oklahoma. A team of meteorologists and hydrologists from the University of Oklahoma along with GIS scientists and mechanical engineers from Oklahoma State University were designated for the development of a historical black ice event database, prognostic and analytic Black Ice Risk Index model with corresponding GIS based publicly accessible mapping system, and a network of roadside black ice formation observation stations. Dr. Jeffrey Basara, professor in meteorology at the University of Oklahoma, and Dr. Yang Hong, director of the Hydrometeorology and Remote Sensing Laboratory (HyDROS), were designated as the PI's for the development of the black ice event database and Black Ice Risk Index numerical model.

This presentation focuses on the meteorologically related tasks within the project. Through extensive literary review, a foundation of knowledge on black ice formation resulting from hoar frost and frozen precipitation was developed. Additionally, data from the Oklahoma Mesonet and Oklahoma Automatic Surface Observation Station (ASOS) networks spanning between the years 2000-2012 were analyzed for documented cases of freezing fog, and a probabilistic model was developed to assist in the forecasting of this phenomenon. This probabilistic model was verified using Mesonet and ASOS data from January 2013 to the present. A Numerical Weather Prediction (NWP) model was developed through the combination of the hierarchical system for hoar frost and frozen precipitation related black ice formation with the probabilistic model for freezing fog related black ice formation. The NWP model utilizes the National Digital Forecast Database (NDFD) output from the National Weather Service for prognostic output, and analyzes current Mesonet and ASOS data for analytic output. A team at Oklahoma State University will incorporate the NWP model output into a publicly accessible GIS mapping system to assist Emergency Management and media personnel, along with the general public, in deciphering locations under risk for black ice formation. This collaborative effort will enhance the ability of Emergency Management personnel to visualize and respond to winter weather related roadway impacts.