AbstractA road ice prediction model was developed on the basis of 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: hoarfrost, freezing fog, and frozen precipitation. Hoarfrost 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 subfreezing 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 Observing System (ASOS), Automated Weather Observing System (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 maxima and confirmed the viability of the developed parameterizations. Further, a fog typological analysis showed the implemented freezing-fog-formation parameterizations to capture 89% of fog events. These results suggest that 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.
Journal of Applied Meteorology and Climatology – American Meteorological Society
Published: Jul 30, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera