Modelling of road surface temperature from a geographical parameter database. Part 1: Statistical

Chapman, Lee; Thornes, John E.; Bradley, Andrew V.

doi:10.1017/s1350482701004030

Cited by 70 publications

(47 citation statements)

References 32 publications

(40 reference statements)

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“…The correlation of these two data sets is dependent on the synchronization of instruments in the ®eld. Errors of synchronization can now be reduced due to the development of a method to geolocate hemispherical images using the Global Positioning System (GPS), to produce a database of SVF (Chapman et al, 2001b). Using the SVF estimates of geo-located sample points it is now possible to take accurate detailed transects through urban canyons.…”

Section: Introductionmentioning

confidence: 99%

A method to assess the variation of urban canyon geometry from sky view factor transects

Al-Jiboori

Yumao

Yongfu

2001

Atmospheric Science Letters

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Abstract:It is now possible to generate large quantities of sky view factor (SVF) values due to advances in digital techniques for calculating SVF from hemispherical images. Coupling image acquisition with Global Positioning System (GPS) data now enables transects to be accurately placed, enabling site-speci®c SVF analysis in relation to street geometry. This paper examines SVF transects through four land cover classes. The ®ndings indicate that although SVF averages are distinctly different in each of the land cover types, an average SVF value for a particular land use type is not a suf®cient estimate at street resolution.*

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Section: Introductionmentioning

confidence: 99%

A method to assess the variation of urban canyon geometry from sky view factor transects

Al-Jiboori

Yumao

Yongfu

2001

Atmospheric Science Letters

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“…Commonly, road ice is forecast using mathematical models that reproduce the physical interactions between the road and the atmosphere (Sass 1992;Shao and Lister 1995;Best 1998;Chapman, Thornes, and Bradley 2001b;Crevier and Delage 2001;Korotenko 2002). Such models take into account meteorological parameters, such as air temperature, precipitation, wind direction, wind speed, humidity, and dew point, and predict both road surface temperature and road conditions (Chapman, Thornes, and Bradley 2001b).…”

Section: Discussionmentioning

confidence: 99%

“…Such models take into account meteorological parameters, such as air temperature, precipitation, wind direction, wind speed, humidity, and dew point, and predict both road surface temperature and road conditions (Chapman, Thornes, and Bradley 2001b). However, despite the high level of detail, their predictions are not always accurate (Shao 1998).…”

Section: Discussionmentioning

confidence: 99%

“…The costs of winter road maintenance are high, but the losses due to road closures are much higher so accurate ice forecasts are needed (Shao 1998;Chapman, Thornes, and Bradley 2001a). Currently, forecasts of road ice come primarily from numerical road prediction models or numerical weather prediction models.…”

Section: Introductionmentioning

confidence: 99%

“…The physical process that produces ice on roads is complex and involves the interaction of weather with road surface conditions (Chapman, Thornes, and Bradley 2001a;Thornes, Cavan, and Chapman 2005). Here we simplify it and we assume that ice will form at a point on the road if precipitation occurs and the air temperature is at or below freezing.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Weather Forecasting for Winter Road Maintenance

Berrocal

Raftery

Gneiting

et al. 2010

Journal of the American Statistical Association

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Winter road maintenance is one of the main tasks for the Washington State Department of Transportation. Anti-icing, that is, the preemptive application of chemicals, is often used to keep the roadways free of ice. Given the preventive nature of anti-icing, accurate predictions of road ice are needed. Currently, anti-icing decisions are usually based on deterministic weather forecasts. However, the costs of the two kinds of errors are highly asymmetric because the cost of a road closure due to ice is much greater than that of taking anti-icing measures. As a result, probabilistic forecasts are needed to optimize decision making.We propose two methods for forecasting the probability of ice formation. Starting with deterministic numerical weather predictions, we model temperature and precipitation using distributions centered around the bias-corrected forecasts. This produces a joint predictive probability distribution of temperature and precipitation, which then yields the probability of ice formation, defined here as the occurrence of precipitation when the temperature is below freezing. The first method assumes that temperatures, as well as precipitation, at different spatial locations are conditionally independent given the numerical weather predictions. The second method models the spatial dependence between forecast errors at different locations. The model parameters are estimated using a Bayesian approach via Markov chain Monte Carlo.We evaluate both methods by comparing their probabilistic forecasts with observations of ice formation for Interstate Highway 90 in Washington State for the 2003-2004 and 2004-2005 winter seasons. The use of the probabilistic forecasts reduces costs by about 50% when compared to deterministic forecasts. The spatial method improves the reliability of the forecasts, but does not result in further cost reduction when compared to the first method.

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