Usage of Existing Meteorological Data Networks for Parameterized Road Ice Formation Modeling

Abstract

road ice prediction model was developed based on existing data networks with an objective of providing a computationally efficient method of road ice forecasting. Icing risk was separated into three distinct road ice formation mechanisms: hoar frost, freezing fog, and frozen precipitation. Hoar frost parameterizations were mostly gathered as-presented in previous literature, with modifications incorporated to account for diffusional ice crystal growth rate complexity. Freezing fog parameterizations were based on previous fog typological analyses under the assumption that fog formation mechanisms are similar in above- and sub-freezing temperatures. Frozen precipitation parameterizations were primarily unique to the developed model, but were also partially based on previous research.Diagnostic analyses use a synthesis of Automated Surface Observation Station (ASOS), Automated Weather Observation Station (AWOS), and Oklahoma Mesonet data. Prognostic analyses utilize the National Digital Forecast Database (NDFD), a 2.5 km gridded database of forecast meteorological variables output from National Weather Service Weather Forecast Offices.A frequency analysis was performed using the diagnostic parameterizations to determine general road icing risk across the state of Oklahoma. The frequency analyses aligned well with expected temporal maximas, and confirmed the viability of the developed parameterizations. Further, a fog typological analysis showed the implemented freezing fog formation parameterizations to capture over 89% of fog events. These results suggest the developed model, identified as the Road-Ice Model (RIM), may be implemented as a robust option for analyzing the potential for road ice development based on the background meteorological environment.