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2 edition of winter road surface temperature prediction model with comparison to others found in the catalog.

winter road surface temperature prediction model with comparison to others

Jianmin Shao

winter road surface temperature prediction model with comparison to others

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  • 13 Currently reading

Published by University of Birmingham in Birmingham .
Written in English


Edition Notes

Thesis (Ph.D.)-University of Birmingham, School of Geography.

Statementby Jianmin Shao.
ID Numbers
Open LibraryOL17221937M

Numerical road prediction models use weather forecasts and road condition data as inputs and forecast future road conditions applying a surface energy-balance equation, which describes the flux of energy between the atmosphere and a road (e.g. Crevier and Delage, ). forecasts of road surface temperature and surface state (dry, wet, icy, snow, etc.) once sensor data are available or updated. The model can easily be re-run every time that new observations are available; however in typical practical applications nowcasts are generated when the road surface temperature falls below a given threshold. 3. Auto-tuning. Pavement Temperature Models The LTPP model was compared with the SHRP models at per-cent reliability. This comparison is illustrated in figure 2. It shows the pavement surface temperature calculated using SHRP and C-SHRP for any latitude and pave-ment surface temperature calcu-lated using the LTPP model for three different latitudes (30, A winter service vehicle (WSV), or snow removal vehicle, is a vehicle specially designed or adapted to clear thoroughfares of ice and snow. Winter service vehicles are usually based on a dump truck chassis, with adaptations allowing them to carry specially designed snow removal equipment. Many authorities also use smaller vehicles on sidewalks, footpaths, and cycleways.

  Special Update (July 7th, ): Firsthand Weather’s early winter forecast will be released on Sunday, July 19th at 2 pm ET on this site. Please come back to check it out, and sign up for the newsletter for the latest updates.. If you have been following Firsthand Weather for any amount of time, you know that I am not a big fan of long .


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winter road surface temperature prediction model with comparison to others by Jianmin Shao Download PDF EPUB FB2

In this paper, based on the surface energy balance method, the numerical model to calculate road surface temperature in the highway is established. A detailed comparison is made between the prediction and observation of the road surface temperature from two road weather stations in the highway.

Road surface temperature prediction model. Shao, J.: a, A Winter Road Surface Temperature Prediction Model with Comparison to Others’, PhD thesis, University of Birmingham, UK, pp. Google Scholar Shao, J.: b, ‘Calculation of Sunshine Duration and Saving of Land Use in Urban Building Design’, Energy and Buildings 15–16, –Cited by: Canadian Journal of Civil Engineering,46(6):A geomatics-based road surface temperature prediction model.

Science of the Total Environment (1–3): Crossref, A winter road surface temperature prediction model with comparison to others.

University of : Lian Gu, Tae J. Kwon, Tony Z. Qiu. Shao, J. () A winter road surface temperature model with comparison to others. Unpublished Ph.D. thesis, University of Birmingham. Shao J () Improving nowcasts of road surface temperature by a back propagation neural by: 8.

In this paper, based on the surface energy balance method, the numerical model to calculate road. surface temperature in the highway is established. A detailed comparison is made between the prediction.

and observation of the road surface temperature from two road weather stations in the highway. COMPARISON OF DEVELOPED MODELS WITH SHRP AND LTPP MODELS High Pavement Temperature Model COMPARISON OF DEVELOPED MODELS WITH SHRP AND LTPP MODELS Low Temperature Model DETERMINATION OF PG BINDER FOR GHADAMS REGION High temperature (at a depth of 20 mm): C Low temperature (at the surface.

Yang et al. () developed a road surface temperature prediction model using heat-energy balance principle between road surface and atmosphere.

Their road surface temperature prediction model consisted of two modules: Canopy 1 (description of heat exchange between road surface and atmosphere) and Canopy 2 (reflection of pavement. con rming the high performance of the proposed multi-level model.

For road surface condition forecasting, a novel conceptual framework for short-term road surface condition forecasting is proposed, under which the short-term changing pro-cess of surface temperature, friction level and contaminant layer depths, is comprehensively explored and.

model using the FMI model as a reference. Therefore, other road weather models are not included in the present study, but comparison with other models could be an important research topic in the future.

Section 2 defines the physical and technical properties of both the FMI and KNMI models. Section 3 introduces the ob-servations and the. Jianmin Shao has written: 'A winter road surface temperature prediction model with comparison to others' Asked in Movies What are the release dates for Fall and Winter.

(1) The road surface temperature prediction model has been tested on data for two road weather stations in the summer and ation coefficients between prediction and observation of road surface temperature are about under three weather conditions (sunny, overcast, cloudy).Cited by:   The /05 winter was simulated with the ISBA-Route/Crocus coupled model to evaluate the simulated road surface temperature with data that were not available in real time and to assess the quality of the prediction of the snow by: A GIS-based model for the prediction of road surface temperature is presented that has the ability to explain up to 74% of the spatial variation in road surface temperature in the West Midlands, UK.

The approach combines basic spatial data sets with a synergy of surveying techniques to produce a geographical parameter database that drives the Cited by: establish the forecast model, learning models of the road surface temperature using three sets of input var-iables at a specific site were created.

These learning models were then applied to other sites along an ex-pressway, and the correct classification of the road surface temperature was examined.

The results of the. conducted in order to better a forecast model on surface temperature predictions that was shown to be inaccurate.

Data was used from the – winter from three cities across Indiana. The data included variables such as air and surface temperatures, precipitation, wind speed, and other variables that could affect the road temperature.

A GIS-based model for the prediction of road surface temperature is presented that has the ability to explain up to 74% of.

Operational experience with ICEWARN model (METRO-CZ) in comparison with other tools Henry Odbert (UK, Met Office) Verification results for road surface temperature forecasts utilizing mobile observations Bujňák, R., Vivoda, J., Application of a road weather forecast model at Slovak Hydrometeorological Institute.

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 by: Virve Karsisto: FMI’s road weather model Abstract: Finnish Meteorological Institute’s (FMI) road weather model has been in operational use for almost 20 years.

The main outputs of the model are road surface temperature and amounts of water, snow and ice on the road. Based on these values, the model determines also the road condition (e.g. wet. It is based on a 1D radiative transfer model that makes use of meteorological input from different numerical weather prediction models and the INCA-BE nowcasting model used by the RMI weather office.

The output (road surface temperature and condition) is generated for about 90 road weather station locations in Flanders and 50 in Wallonia, and. Ice Prediction. • The key difference here was the inclusion of a forecast model. • This was a simple 0D energy balance model which provided a site.

specific 24 hour forecast of road surface temperatures. • This was issued at midday so that decisions regarding treatment. for the forthcoming night could be made. Most accident prediction models belong to the count data regression models, in particular the negative binomial model, which assumes all data or cases are statistically independent.

This assumption, how-ever, may be violated when repeated observations over multiple periods (e.g., yearly accident counts) at the same locations (e.g. a prediction of road-surface temperature, as well as water, ice, or snow on the road, by the following calculations: FIGURE 2 Evolution during the last 3 hr of road temperature and dewpoint.

VRES-KOhm expresses the road surface condition by a resistance of electric current between two electrodes in road surface, but it remains at maximum value. Ice and Snow are important risks for road traffic. In this study we show several events of slipperiness in Switzerland, mainly caused by rain or snow falling on a frozen surface.

Other reasons for slippery conditions are frost or freezing dew in clear nights and nocturnal clearing after precipitation, which goes along with radiative cooling. The main parameters of road weather. driven prediction models for road surface condition related parameters forecasting. Liu developed a road surface temperature prediction model based on gradient extreme learning machine boosting algorithm [22].

Solol developed a road surface temperature prediction model based on energy balance and heat conduction models [23]. The objective of this paper was to investigate a statistical approach for thermal mapping, based on PCA, to build a road surface temperature forecast for a wide variety of weather situations and temperature ranges.

Overall, PCA provided a good forecast of road surface temperature, explaining up to 80% of measurements over a by: 7. Unsymmetrical trend model (UTm): this model provides a decreasing of the ECA with temperature if the surface is Hydrophilic, and an increasing of ECA with temperature if the surface is by: 8.

Description and Verification of a Road Ice Prediction Model J. SHAO, J. THORNES, AND P. LISTER The IceBreak model developed by Vaisala TMI is described in its physical bases. The model is veri~ied and ~o~pared wit~ the U.K. Meteorological Office model usmg roadside mputs of wmter at 11 sites in the United Kingdom.

The results show. The alert trigger temperatures are customisable for each site, allowing you to tailor the action level based on your clients exact requirement. Reports are emailed up to 3 times a day as the latest forecast data comes in, and you can choose the format – csv, Excel spreadsheet or PDF, or any combination of the three.

You can sort of compare and contrast my forecast to the graphics above, which is really just for fun. In reality, you have to take long-range models with a grain of salt, even though this particular model has done a bit better than other models.

Click here to get your region-by-region winter breakdown, and click here to read my detailed analysis. Variation of Pavement Temperature. Figure 6 shows the pavement temperature in July As can be seen, although the pavement temperature decreases with the increase of depth from road surface, the overall temperature of asphalt pavement in Beijing is very high in summer, and the maximum temperature can reach 35°C at the depth 5 cm from the road by: 4.

An Indirect Method for Predicting Road Surface Temperature in Coastal Areas with Snowy Winters JASON COVERT1 AND ROBERT HELLSTRÖM2 EXTENDED ABSTRACT In places that experience snow and ice, road clearing and deicing operations are a necessity to ensure that road networks remain open and safe for travel.

Such operations, however, are costly to. Weather prediction model Weather stations Maintenance data Floating car data SUPPORT SYSTEM FOR WINTER MAINTENANCE TO SUPPORT THE RIGHT DECISION TO OPTIMIZE THE MAINTENANCE COSTS TO INCREASE SAFETY ON ROADS SSWM is a complex system for road condition and temperature prediction.

It uses a sophisticated prediction core and a. Jianmin Shao has written: 'A winter road surface temperature prediction model with comparison to others' Asked in Authors, Poets, and Playwrights What has the author Shelley Anne Harris written.

There is disclosed a method and system for classifying road surface conditions. In an aspect, the method comprises: acquiring a digital image of a road surface at a given location and time; processing the acquired digital image to generate one or more feature vectors for classifying winter road surface conditions; acquiring values for auxiliary data to create feature vectors Cited by: spatial variation in heat fluxes beneath the road surface to be modelled simplistically in a route-based road weather model.

However, whilst this was an acceptable first approximation, the parameterization of sub-surface temperatures based on an ordinal classification lacks the sophistication exhibited by other components of the model.

An accurate prediction of road weather conditions is important for providing safer roads (Fridstrøm et al., ; Norrman et al., ), minimizing the environmental damage from over‐salting (Ramakrishna and Viraraghavan, ) and for cutting the winter road Cited by: Although winter highway maintenance has improved significantly over time (for instance, between tothere was a 26% decline in crashes during sleet and snow weather conditions (Goodwin, )), road users still experience delays and crashes due to unsatisfactory road conditions that result from poor winter weather.

Effective winter maintenance of motorways is highly dependent on local topography and weather and can be a significant economic factor in overall maintenance costs.

In the present paper, a temperature profile of a highway road surface is obtained through infrared thermography measurements and then compared to numerical weather forecasts using the.

temperature, dew point, wind speed, and road surface temperature from archived RWIS observations were used as inputs (perfect forecast), with verification using yes/no frost observations collected by the Iowa Department of Transportation maintenance garages.

During the winter ofthe frost model was tested. Washington, DC: The National Academies Press. NRC. b. Fair Weather: Effective Partnerships in Weather and Climate Services. Washington, DC: The National Academies Press. NRC. c. Tracking and Predicting the Atmospheric Dispersion of Hazardous Material Releases: Implications for Homeland Security.

Washington, DC: The .rates and submit other values to see how the road condition predictions might change. In Figure 6, a chemical concentration display shows the results of two scenarios. The green trace shows the dilution rate of sodium chloride on the road surface if no additional treatments of chemicals are applied.

In this case, given the forecast weather.In Sect. 2, we describe the model simulations, the station observations used for evaluation and the analysis methods.

In Sect. 3, we present a detailed analysis of near-surface air temperature–snow depth–soil temperature relationships in winter. In Sect. 4, we discuss the roles of atmospheric forc-ing and model by: