TY - JOUR
T1 - Modelling of road surface temperature from a geographical parameter database. Part 2: Numerical
AU - Chapman, Lee
AU - Thornes, John
AU - Bradley, AV
PY - 2001/12/1
Y1 - 2001/12/1
N2 - A new ice prediction strategy is presented based on the numerical modelling of surveyed geographical parameters. This approach enables the thermal projection of road surface temperatures across the road network entirely by model predictions and without the need for thermal maps. The influence of eight geographical parameters (latitude, altitude, sky-view factor, screening, roughness length, road construction, traffic density and topography) is investigated by means of sensitivity analyses. The sky-view factor is highlighted as the dominant control on road surface temperature, particularly at high levels of atmospheric stability. A numerical road weather model incorporating all eight parameters was run over 20 nights using forecast and retrospective meteorological data. The model has the ability to explain up to 72% of the variation in road surface temperature purely by thermally projecting surface temperature using geographical variables. Retrospective results produce an average r.m.s. error of 1degreesC which is comparable to existing UK road weather models.
AB - A new ice prediction strategy is presented based on the numerical modelling of surveyed geographical parameters. This approach enables the thermal projection of road surface temperatures across the road network entirely by model predictions and without the need for thermal maps. The influence of eight geographical parameters (latitude, altitude, sky-view factor, screening, roughness length, road construction, traffic density and topography) is investigated by means of sensitivity analyses. The sky-view factor is highlighted as the dominant control on road surface temperature, particularly at high levels of atmospheric stability. A numerical road weather model incorporating all eight parameters was run over 20 nights using forecast and retrospective meteorological data. The model has the ability to explain up to 72% of the variation in road surface temperature purely by thermally projecting surface temperature using geographical variables. Retrospective results produce an average r.m.s. error of 1degreesC which is comparable to existing UK road weather models.
U2 - 10.1017/S1350482701004042
DO - 10.1017/S1350482701004042
M3 - Article
SN - 1469-8080
VL - 8
SP - 421
EP - 436
JO - Meteorological Applications
JF - Meteorological Applications
IS - 4
ER -