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- Hu, Yumei, et al.
(författare)
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The use of screening effects in modelling route-based daytime road surface temperature
- 2016
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Ingår i: Theoretical and Applied Climatology. - : Springer Science and Business Media LLC. - 0177-798X .- 1434-4483. ; 125:1, s. 303-319
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Tidskriftsartikel (refereegranskat)abstract
- © 2015 The Author(s) Winter road maintenance is essential for road safety. Accurate predictions of the road surface temperature (RST) and conditions can enhance the efficiency of winter road maintenance. Screening effects, which encompass shading effects and the influence of the sky-view factor (ψs), influence RST distributions because they affect road surface radiation fluxes. In this work, light detection and ranging (Lidar) data are used to derive shadow patterns and ψs values, and the resulting shadow patterns are used to model route-based RST distributions along two stretches of road in Sweden. The shading patterns and road surface radiation fluxes calculated from the Lidar data generally agreed well with measured RST values. Variation in land use types and the angle between the road direction and solar azimuth may introduce uncertainties, and accounting for these factors may improve the results obtained in certain cases. A simple shading model that only accounts for the direct radiation at the instant of measurement is often sufficient to provide reasonably accurate RST estimates. However, in certain cases, such as those involving measurements close to sunset, it is important to consider the radiation accumulated over several hours. The inclusion of ψs improves the model performance even more in such cases. Overall, RST models based on the accumulated direct shortwave radiation offered an optimal balance of simplicity and accuracy. General radiation models were built for country road and highway environments, explaining up to 70 and 65 %, respectively, of the observed variation in RST along the corresponding stretches of road.
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- Hu, Yumei, et al.
(författare)
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Modeling Road Surface Temperature from Air Temperature and Geographical Parameters-Implication for the Application of Floating Car Data in a Road Weather Forecast Model
- 2019
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Ingår i: Journal of Applied Meteorology and Climatology. - : American Meteorological Society. - 1558-8424 .- 1558-8432. ; 58:5, s. 1023-1038
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Tidskriftsartikel (refereegranskat)abstract
- Precise forecasts of road surface temperature (RST) and road conditions allow winter roads to be maintained efficiently. The upcoming "big data" application known as "floating car data" (FCD) provides the opportunity to improve road weather forecasts with measurements of air temperature T-a from in-car sensors. The research thus far with regard to thermal mapping has mainly focused on clear and calm nights, which occur rarely and during low traffic intensity. It is expected that more than 99% of the FCD will be collected during conditions other than clear and calm nights. Utilizing 32 runs of thermal mapping and controlled T-a surveys carried out on mostly busy roads over one winter season, it was possible to simulate the use of T-a and geographical parameters to reflect the variation of RST. The results show that the examined route had several repeatable thermal fingerprints during times of relatively high traffic intensity and with different weather patterns. The measurement time, real-time weather pattern, and previous weather patterns influenced the spatial pattern of thermal fingerprints. The influence of urban density and altitude on RST can be partly seen in their relationship with T-a, whereas the influence of shading and sky-view factor was only seen for RST. The regression models with T-a included explained up to 82% of the RST distribution and outperformed models that are based only on the geographical parameters by as much as 30%. The performance of the models denotes the possible utility of T-a from FCD, but further investigation is needed before moving from controlled T-a measurements to T-a from FCD.
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