대기 (Atmosphere)

  • 제24권4호
  • /
  • Pages.471-479
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  • 2014
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  • 1598-3560(pISSN)
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  • 2288-3266(eISSN)
한국기상학회 (Korean Meteorological Society)
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UM 자료를 이용한 노면온도예측모델(UM-Road)의 개발

Development of Road Surface Temperature Prediction Model using the Unified Model output (UM-Road)

  • 박문수 ((재)기상기술개발원 차세대도시농림융합기상사업단) ;
  • 주승진 ((사)대기환경모델링센터) ;
  • 손영태 (명지대학교 교통공학과)
  • Park, Moon-Soo (Weather Information Service Engine, Center for Atmospheric Science & Earthquake Research) ;
  • Joo, Seung Jin (Center for Atmospheric and Environmental Modeling) ;
  • Son, Young Tae (Department of Transportation Engineering, Myongji University)
  • 투고 : 2014.08.22
  • 심사 : 2014.10.29
  • 발행 : 2014.12.31
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초록

A road surface temperature prediction model (UM-Road) using input data of the Unified Model (UM) output and road physical properties is developed and verified with the use of the observed data at road weather information system. The UM outputs of air temperature, relative humidity, wind speed, downward shortwave radiation, net longwave radiation, precipitation and the road properties such as slope angles, albedo, thermal conductivity, heat capacity at maximum 7 depth are used. The net radiation is computed by a surface radiation energy balance, the ground heat flux at surface is estimated by a surface energy balance based on the Monin-Obukhov similarity, the ground heat transfer process is applied to predict the road surface temperature. If the observed road surface temperature exists, the simulated road surface temperature is corrected by mean bias during the last 24 hours. The developed UM-Road is verified using the observed data at road side for the period from 21 to 31 March 2013. It is found that the UM-Road simulates the diurnal trend and peak values of road surface temperature very well and the 50% (90%) of temperature difference lies within ${\pm}1.5^{\circ}C$ (${\pm}2.5^{\circ}C$) except for precipitation case.

키워드

참고문헌

  1. Atallah, D., P. Bridge, M. DeVries, and P. Leviakangas, 2012: Road weather information system & maintenance decision support system economic value tool. Standing international road weather commission ID0025.
  2. Bodri, L., and V. Cermak, 2003: Prediction of surface air temperature by neural network, example based on three-year temperature monitoring at Sporilov station. Stud. Geophys. Geod., 47, 173-184. https://doi.org/10.1023/A:1022211924646
  3. Crevier, L.-P., and Y. Delage, 2001: METRo: A new model for road-condition forecasting in Canada. J. Appl. Meteorol., 40, 2026-2037. https://doi.org/10.1175/1520-0450(2001)040<2026:MANMFR>2.0.CO;2
  4. Dupont, S., and P. G. Mestayer, 2006: Parameterization of the urban energy budget with the submesoscale soil model. J. Appl. Meteor. Climatol., 45, 1744-1764. https://doi.org/10.1175/JAM2417.1
  5. Goodin, W. R., G. J. McRae, and J. H. Seinfeld, 1979: A comparison of interpolations methods for sparse data: Application to wind and concentration fields. J. Appl. Meteorol., 18, 761-771. https://doi.org/10.1175/1520-0450(1979)018<0761:ACOIMF>2.0.CO;2
  6. KMA, 2013: Monthly weather report 2013.03. Korea Meteorological Administration, Publication number 11-1360000-000002-06, 118 pp.
  7. Lee, J., S.-O. Han, and, K.-Y. Chung, 2012: The improvement of forecast accuracy of the Unified Model at KMA by using an optimized set of physical options. Atmosphere, 22, 345-356. https://doi.org/10.14191/Atmos.2012.22.3.345
  8. ASHRAE, 2003: ASHRAE Handbook Heating, Ventilating, and Air-Conditioning Applications. American Society of Heating, Refrigeration and Air-Conditioning Engineers Research.
  9. Park, S.-U., 1994: The effect of surface physical conditions on the growth of the atmospheric boundary layer. J. Korean Meteor. Soc., 30, 119-134.
  10. Pielke, R. A., 2002: Mesoscale meteorological modeling (2nd Edition). Academic Press, 676 pp.
  11. Rens, L., 2009: Concrete roads: a smart and sustainable choice. European Concrete Paving Association, Brussels, 32 pp.
  12. Sass, B. H., 1992: A numerical model for prediction of road surface temperature and ice. J. Appl. Meteorol., 31, 1499-1506. https://doi.org/10.1175/1520-0450(1992)031<1499:ANMFPO>2.0.CO;2
  13. Shao, J., 1998: Improving nowcasts of road surface temperature by a backpropagation neural network. Wea. Forecasting, 13, 164-171. https://doi.org/10.1175/1520-0434(1998)013<0164:INORST>2.0.CO;2
  14. Rayer, P. J., 1987: The meteorological office forecast road surface temperature model. Meteorol. Mag., 116, 180-191.
  15. Yang, C.-H., M.-S. Park, and D.-G. Yoon, 2011: A road surface temperature prediction modeling for road weather information system. J. Korean Soc. Transport., 29, 123-131.

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  2. High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea vol.10, pp.4, 2017, https://doi.org/10.5194/amt-10-1575-2017