Meteorology and Atmospheric Physics

, Volume 130, Issue 4, pp 485–498 | Cite as

A numerical forecast model for road meteorology

  • Chunlei MengEmail author
Original Paper


A fine-scale numerical model for road surface parameters prediction (BJ-ROME) is developed based on the Common Land Model. The model is validated using in situ observation data measured by the ROSA road weather stations of Vaisala Company, Finland. BJ-ROME not only takes into account road surface factors, such as imperviousness, relatively low albedo, high heat capacity, and high heat conductivity, but also considers the influence of urban anthropogenic heat, impervious surface evaporation, and urban land-use/land-cover changes. The forecast time span and the update interval of BJ-ROME in vocational operation are 24 and 3 h, respectively. The validation results indicate that BJ-ROME can successfully simulate the diurnal variation of road surface temperature both under clear-sky and rainfall conditions. BJ-ROME can simulate road water and snow depth well if the artificial removing was considered. Road surface energy balance in rainy days is quite different from that in clear-sky conditions. Road evaporation could not be neglected in road surface water cycle research. The results of sensitivity analysis show solar radiation correction coefficient, asphalt depth, and asphalt heat conductivity are important parameters in road interface temperatures simulation. The prediction results could be used as a reference of maintenance decision support system to mitigate the traffic jam and urban water logging especially in large cities.



This work was supported by the National Natural Science Foundation of China under Grant 41375114.


  1. Bogren J (1991) Screening effects on road surface temperature and road slipperiness. Theor Appl Climatol 43:91–99CrossRefGoogle Scholar
  2. Bonan GB, Oleson KW, Vertenstein M et al (2002) The land surface climatology of the Community Land Model coupled to the NCAR Community Climate Model. J Clim 15:3123–3149CrossRefGoogle Scholar
  3. Bouilloud L, Martin E (2006) A coupled model to simulate snow behaviors on roads. J Appl Meteorol Clim 45:500–516CrossRefGoogle Scholar
  4. Bouilloud L, Martin E, Habets F et al (2009) Road surface condition forecasting in France. J Appl Meteorol Clim 48:2513–2527CrossRefGoogle Scholar
  5. Chapman L, Thornes JE (2005) The influence of traffic on road surface temperatures: implications for thermal mapping studies. Meteorol Appl 12:371–380CrossRefGoogle Scholar
  6. Chapman L, Thornes JE (2006) A geomatics-based road surface temperature prediction model. Sci Total Environ 360:68–80CrossRefGoogle Scholar
  7. Chapman L, Thornes JE, Bradley AV (2001) Statistical modelling of road surface temperature from a geographical parameter database. Meteorol Appl 8:409–419CrossRefGoogle Scholar
  8. Chen F, Mitchell K, Schaake J et al (1996) Modeling of land-surface evaporation by four schemes and comparison with FIFE observations. J Geophys Res 101:7251–7268CrossRefGoogle Scholar
  9. Chen F, Manning KW, Lemone MA et al (2007) Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J Appl Meteorol Clim 46:694–713CrossRefGoogle Scholar
  10. Chen M, Fan S, Zhong J et al (2010) An experimental study of assimilating the global position system-precipitable water vapor observations into the rapid updated cycle system for the Beijing area. Acta Meteorol Sin (in Chinese) 68:450–463Google Scholar
  11. Chen M, Fan S, Zheng Z et al (2011) The performance of a proximity sounding based on the BJ-RUC system and its preliminary implementation in the convective potential forecast. Acta Meteorol Sin (in Chinese) 69:181–194Google Scholar
  12. Crevier LP, Delage Y (2001) METRo: a new model for road-condition forecasting in Canada. J Appl Meteorol 40:2026–2037CrossRefGoogle Scholar
  13. Dai Y, Zeng X, Dickinson RE et al (2003) The Common Land Model. Bull Am Meteorol Soc 84:1013–1023CrossRefGoogle Scholar
  14. Dai Y, Dickinson RE, Wang Y (2004) A two-big-leaf model for canopy temperature, photosynthesis and stomatal conductance. J Clim 17:2281–2299CrossRefGoogle Scholar
  15. Fujimoto A, Saida A, Fukuhara T (2012) A new approach to modeling vehicle-induced heat and its thermal effects on road surface temperature. J Appl Meteorol Clim 51:1980–1993CrossRefGoogle Scholar
  16. Gustavsson T (1990) Variation in road surface temperature due to topography and wind. Theor Appl Climatol 41:227–236CrossRefGoogle Scholar
  17. Henderson-Sellers B (1986) Calculating the surface energy balance for lake a reservoir modeling: a review. Rev Geophys 24:625–649CrossRefGoogle Scholar
  18. Hostetler SW, Bartlein PJ (1990) Simulation of lake evaporation with application to modeling lake level variations of Harney-Malheur Lake, Oregon. Water Resour Res 26:2603–2612Google Scholar
  19. Hostetler SW, Bates GT, Giorgi F (1993) Interactive coupling of a lake thermal model with a regional climate model. J Geophys Res 98(1):5045–5057CrossRefGoogle Scholar
  20. Hu Y, Almkvist E, Lindberg F et al (2016) The use of screening effect in modelling route-based daytime road surface temperature. Theor Appl Climatol 125:303–319CrossRefGoogle Scholar
  21. Junga I, Nurmi P, Hippi M (2013) Statistical modelling of wintertime road surface friction. Meteorol Appl 20:318–329CrossRefGoogle Scholar
  22. Kangas M, Heikinheimo M, Hippi M (2015) RoadSurf: a modelling system for predicting road weather and road surface condictions. Meteorol Appl 22:544–553CrossRefGoogle Scholar
  23. Karsisto V, Nurmi P, Kangas M et al (2016) Improving road weather model forecasts by adjusting the radiation input. Meteorol Appl 23:503–513CrossRefGoogle Scholar
  24. Krsmanc R, Slak AS, Demsar J (2013) Statistical approach for forecasting road surface temperature. Meteorol Appl 20:439–446CrossRefGoogle Scholar
  25. Meng C, Zhang C, Miao S et al (2013) Localization and validation of an urbanized high-resolution land data assimilation system (u-HRLDAS). Sci China Earth Sci 56:1071–1078CrossRefGoogle Scholar
  26. Miao S, Chen F (2008) Formation of horizontal convective rolls in urban areas. Atmos Res 89:298–304CrossRefGoogle Scholar
  27. Miao S, Chen F, Margaret AL et al (2009) An observational and modeling study of characteristics of urban heat island and boundary layer structures in Beijing. J Appl Meteorol Clim 48:484–501CrossRefGoogle Scholar
  28. Mitchell VG, Mein RG, McMahon TA (2001) Modelling the urban water cycle. J Environ Model Softw 16(7):615–629CrossRefGoogle Scholar
  29. Prusa JM, Segal M, Temeyer BR et al (2002) Conceptual and scaling evaluation of vehicle traffic thermal effects on snow/ice-covered roads. J Appl Meteorol 41:1225–1240CrossRefGoogle Scholar
  30. Rodell M, Houser PR, Jambor U et al (2004) The global land data assimilation system. Bull Am Meteorol Soc 85:381–394CrossRefGoogle Scholar
  31. Sailor DJ, Lu L (2004) A top-down methodology for developing diurnal and seasonal anthropogenic heating profiles for urban areas. Atmos Environ 38:2737–2748CrossRefGoogle Scholar
  32. Sass BH (1992) A numerical model for prediction of road temperature and ice. J Appl Meteorol 31:1499–1506CrossRefGoogle Scholar
  33. Shao J, Swanson JC, Patterson R et al (1997) Variation of winter road surface due to topography and application of thermal mapping. Meteorol Appl 4:131–137CrossRefGoogle Scholar
  34. Yang CH, Yun D-G, Sung JG (2012) Validation of a road surface temperature prediction model using real-time weather forecasts. KSCE J Civ Eng 16(7):1289–1294CrossRefGoogle Scholar
  35. Yin J, Yu D, Wilby R (2016) Modelling the impact of land subsidence on urban pluvial flooding: a case study of downtown Shanghai, China. Sci Total Environ 544:744–753CrossRefGoogle Scholar
  36. Zeng X, Shaikh M, Dai Y et al (2002) Coupling of the Common Land Model to the NCAR Community Climate Model. J Clim 15:1832–1854CrossRefGoogle Scholar
  37. Zhang J, Zhao T. (1987) Thermo physics characteristics of frequently used materials in engineering (in Chinese). New Era PressGoogle Scholar
  38. Zhang C, Ji C, Guo Y et al (2005) Numerical simulations of topography impacts on “00.7” heavy rainfall in Beijing. Prog Nat Sci 15(9):818–826CrossRefGoogle Scholar
  39. Zhang C, Chen M, Guo Y et al (2006) Numerical assessing experiments on the individual components impact of the meteorological observation network on the “July 2007” torrential rain in Beijing. Acta Meteorol Sin 20(4):389–401Google Scholar
  40. Zhang C, Miao S, Li Q et al (2007) Impacts of fine resolution land use information of Beijing on a summer severe rainfall simulation. Chin J Geophys CH (in Chinese) 50(5):1372–1382Google Scholar
  41. Zhang C, Chen F, Miao S et al (2009) Impacts of urban expansion and future green planting on summer precipitation in the Beijing metropolitan area. J Geophys Res 114:D02116. doi: 10.1029/2008JD010328 Google Scholar

Copyright information

© Springer-Verlag Wien 2017

Authors and Affiliations

  1. 1.Institute of Urban Meteorology, China Meteorological AdministrationBeijingChina
  2. 2.Hetian Meteorological BureauHetianChina

Personalised recommendations