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Friday, August 7, 2020 | History

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

by Jianmin Shao

  • 26 Want to read
  • 7 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

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. E. THORNES, AND P. J. 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.


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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.