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http://dx.doi.org/10.14191/Atmos.2021.31.5.525

Estimation of Road Sections Vulnerable to Black Ice Using Road Surface Temperatures Obtained by a Mobile Road Weather Observation Vehicle  

Park, Moon-Soo (Department of Climate and Environment, Sejong University)
Kang, Minsoo (Climate Change & Environmental Research Center, Sejong University)
Kim, Sang-Heon (Climate Change & Environmental Research Center, Sejong University)
Jung, Hyun-Chae (Mirae Climate Co., Ltd.)
Jang, Seong-Been (Mirae Climate Co., Ltd.)
You, Dong-Gill (Mirae Climate Co., Ltd.)
Ryu, Seong-Hyen (Mirae Climate Co., Ltd.)
Publication Information
Atmosphere / v.31, no.5, 2021 , pp. 525-537 More about this Journal
Abstract
Black ices on road surfaces in winter tend to cause severe and terrible accidents. It is very difficult to detect black ice events in advance due to their localities as well as sensitivities to surface and upper meteorological variables. This study develops a methodology to detect the road sections vulnerable to black ice with the use of road surface temperature data obtained from a mobile road weather observation vehicle. The 7 experiments were conducted on the route from Nam-Wonju IC to Nam-Andong IC (132.5 km) on the Jungang Expressway during the period from December 2020 to February 2021. Firstly, temporal road surface temperature data were converted to the spatial data with a 50 m resolution. Then, the spatial road surface temperature was normalized with zero mean and one standard deviation using a simple normalization, a linear de-trend and normalization, and a low-pass filter and normalization. The resulting road thermal map was calculated in terms of road surface temperature differences. A road ice index was suggested using the normalized road temperatures and their horizontal differences. Road sections vulnerable to black ice were derived from road ice indices and verified with respect to road geometry and sky view, etc. It was found that black ice could occur not only over bridges, but also roads with a low sky view factor. These results are expected to be applicable to the alarm service for black ice to drivers.
Keywords
Black ice; road ice index; road ice vulnerability; road surface temperature; road thermal map;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Rayer, P. J., 1987: The meteorological office forecast road surface temperature model. Meteorol. Mag., 116, 180-191.
2 Robbins, C. C., and J. V. Cortinas Jr., 2002: Local and synoptic environments associated with freezing rain in the contiguous United States. Wea. Forecasting, 17, 47-65.   DOI
3 Rozante J. R., E. R. Gutierrez, P. L. da Silva Dias, A. de Almeida Fernandes, D. S. Alvim, and V. M. Silva, 2020: Development of an index for frost prediction: technique and validation. Meteor. Appl., 27, e1807, doi:10.1002/met.1807.   DOI
4 Sass, B. H., 1992: A numerical model for prediction of road temperature and ice. J. Appl. Meteor. Climatol., 31, 1499-1506.   DOI
5 Theriault, J. M., R. E. Stewart, and W. Henson, 2010: On the dependence of winter precipitation types on temperature, precipitation rate, and associated features. J. Appl. Meteor. Climatol., 49, 1429-1442, doi:10.1175/2010JAMC2321.1.   DOI
6 Todeschini, I., C. D. Napoli, I. Pretto, G. Merler, R. Cavaliere, R. Apolloni, G. Antonacci, A. Piazza, and G. Benedetti, 2016: Thermal mapping as a valuable tool for road weather forecast and winter road maintenance: an example from the Italian Alps. Proc., Fourth Int. Conf. Remote Sens. Geoinf. Environ. (RSCy2016), SPIE 9688, 96880H, doi:10.1117/12.2240484.
7 Kamarainen, M., O. Hyvarinen, K. Jylha, A. Vajda, S. Neiglick, J. Nuottokari, and H. Gregow, 2017: A method to estimate freezing rain climatology from ERAInterim reanalysis over Europe. Nat. Hazards Earth Syst. Sci., 17, 243-259, doi:10.5194/nhess-17-243-2017.   DOI
8 Carriere, J.-M., C. Lainard, C. Le Bot, and F. Robart, 2000: A climatological study of surface freezing precipitation in Europe. Meteor. Appl., 7, 229-238.   DOI
9 Park, M.-S., S. J. Joo, and Y. T. Son, 2014: Development of road surface temperature prediction model using the unified model output (UM-Road). Atmosphere, 24, 471-479, doi:10.14191/Atmos.2014.24.4.471 (in Korean with English abstract).   DOI
10 Bourgouin, P., 2000: A method to determine precipitation types. Wea. Forecasting, 15, 583-592.   DOI
11 Chapman, L., and J. E. Thornes, 2005: The influence of traffic on road surface temperatures: implications for thermal mapping studies. Meteor. Appl., 12, 371-380.   DOI
12 Cho, C., J.-B. Jee, M.-S. Park, S.-H. Park, and Y.-J. Choi, 2016: Comparison of surface temperatures between thermal infrared image and Landsat 8 satellite. J. Korean Soc. Atmos. Environ., 32, 46-56, doi:10.5572/KOSAE.2016.32.1.046 (in Korean with English abstract).   DOI
13 Forbes, R., I. Tsonevsky, T. Hewson, and M. Leutbecher, 2014: Towards predicting high-impact freezing rain events. ECMWF Newsletter, 141, 15-21, doi:10.21957/xcauc5jf.   DOI
14 Gustavsson, T., 1999: Thermal mapping - a technique for road climatological studies. Meteor. Appl., 6, 385-394.   DOI
15 Monteith, J. L., and M. H. Unsworth, 1990: Principles of Environmental Physics. Cambridge University, 291 pp.
16 Kang M., and M.-S. Park, 2017: Mapping of road sections vulnerable to ice in the Seoul Metropolitan Area using a mobile road weather vehicle. In M.-S. Park et al. Eds., Urban Meteorological Observations in the Seoul Metropolitan Area, Lambert Academic Publishing, 119-137.
17 Kwon, S.-H., H.-R. Byun, C.-K. Park, and H.-N. Kwon, 2016: On the freezing precipitation in Korea and the basic schemes for its potential prediction. Asia-Pac. J. Atmos. Sci., 52, 35-50, doi:10.1007/s13143-015-0086-1.   DOI
18 Lufft, 2019: User Manual MARWIS/StaRWIS. Lufft, 59 pp [Available online at https://www.lufft.com/download/manual-lufft-marwis-starwis-en/].