Road Weather Model Verification: Revised Approach Alexander Mahura, Claus Petersen, Bent Sass Danish Meteorological Institute (DMI), Lyngbyvej 100, Copenhagen, DK-2100, Denmark (E-mail:
[email protected] / Phone : +45-3915-7423 / Fax: +45 3915-7400)
ABSTRACT The DMI operational Road Weather Modelling (RWM) system forecasts road conditions at more than 400 road stations of the Danish road network represented by a large number of the roads/driving lanes in different communes. As input the system uses continuous observations from the synoptic and road stations together with the meteorological output from the numerical weather prediction model so-called High Resolution Limited Area Model (HIRLAM). Recently, focus has shifted to forecasting along the roads’ stretches (located at distances of 1 km). These account in total more than 17 thousand stretches (identified based on GPS coordinates) and cover entire road network (except, Bornholm Island). Such detailed spatial forecasts require additional information not only at the road stations but also at the road stretches. For that reason, the re-verification of the RWM forecasts at all Danish road stations and road stretches of several roads has been conducted using routine measurements at road stations and thermal mapping data along selected roads.
Road Stretches Forecasting
Introduction
Variability of Thermal Mapping vs. Forecasting Data
Due to increased resolution in the numerical weather prediction models, assimilation of satellite cloud related data on an hourly basis, and integration of measurements of road conditions from road maintenance vehicles allowed to improve quality and accuracy of road weather forecasting. The DMI RWM system uses these changes for operational forecasting of road conditions at more than 400 road stations of the Danish road network. Recently the focus has shifted to forecasting along roads’ stretches each with a length of 1 km.
Road Weather Model (RWM) System During the last two decades DMI in cooperation with DRD has developed and used the RWM system. This system is used to forecast (every 30/60 minutes) the main road conditions at selected locations of the Danish road station network (black dots in Fig. 1) based on output from the HIgh Resolution Limited Area Model (HIRLAM).
Figure 6. Mean absolute error, MAE and bias, BIAS (at 95% confidence interval) of the road surface temperature (Ts) and air temperature (Ta) by road stretches of the VA-4 road.
Verification as Function of Road Station Characteristics
Bornholm
Jylland Road Stations
Sjelland
Fyn
Figure 1. Network of Danish road stations (black dots) and DMI-HIRLAM RWM system models: 15 (R15) and 5 (R05) km resolutions
The DMI produces road forecasts every hour for the network of road stations for the model domain. The model consists of a NWP model called DMI-HIRLAM and an in-line 1-D Road Condition Model (RCM, Fig. 2). The 1-D model is forced by the NWP model every time step with respect to the upper atmospheric conditions. The main characteristics of the NWP model are the following: 40 vertical levels; 82 by 98 grid points in a rotated system of coordinates; dynamic and physical time steps are 50 and 450 s, respectively; boundary and first guess ages are 0-5 and 1 h, respectively; and data assimilation — 3 h.
Figure 3. Danish roads stretches (appx. 17000) and geographical location of the Danish roads where the thermal mapping measurements were conducted during the road winter seasons and with road stations (black dots) and .3 hour forecasts of the road surface temperature at the road stretches in the Ribe Amt county for 19 Feb 2007 at 00-06 of local standard time.
Such advanced forecasts have an advantage, because based on road stretches forecasts it can influence the decision making process and allow for driver, having operational on-line access to these forecasts, to optimize where and when exactly the salting activities should be performed and hence, reduce the amount of salt spreaded during the road weather seasons. Moreover, consequently, it will positively influence environmental conditions within areas surrounding the roads.
Spatial Distribution of Icing Conditions at Roads
Figure 7. Diurnal variability of bias of the road surface temperature (Ts) at the road stations (classes : CC — seashore station with a distance of less than 1 km, CM — 1-5 km, CL — land station, i.e. more than 5 km from the shore) as a function of the disposition /location/ (DN—normal, DS—on a slope, DP—on a platoe, DVDS and DSDV—in valley from valley to slope and opposite) and as a function of the slope orientation (X—normal, S-N & N-S, E-W & W-E, NE-SW & NWSE, SE-NW & SW-NE) of where the road station is located.
Future — Fine-Scale Road Stretch Forecasting
Figure 2. Schematic view of fluxes in RCM, where : G — ground heat flux, S — direct insolation, D — diffuse insolation, R — infrared radiation, H — sensible heat flux, L — latent heat flux, F — flux correction.
Thermal Mapping Data (ThMD) During the road winter seasons thermal mapping measurements has been conducted along several Danish roads/driving lanes. Thermal mapping is a measurement of spatial variation of road surface temperature under different weather conditions using infrared thermometers. The device is mounted on the vehicle at such a height that the sensor will have a clear sight to the road surface. Mostly such measurements are performed during road winter seasons with a focus on nighttimes. ThMD has been provided by the DRD through an online VINTERMAN database. It contains detailed information about number of the driving, measuring, and salting activities’ parameters, road surface and air temperatures. Since the measurements are done at non-equal discrete time intervals, the temperatures were recalculated by averaging at 1 min interval. The datasets were restructured by reassigning pairs of measured temperatures at exact local times when simultaneously measuring and forecasting are done for corresponding coordinates of the road stretches.
Figure 4. Spatial distribution of icing conditions (for Ts, red alert cases) within the Danish road network based on long-term observations during road weather seasons for (top) October, (middle) January and (bottom) April months by road forecasting regions.
Overall Verification for Recent Road Weather Seasons
Acknowledgments This study is a part of the joint DRD and DMI project entitled “Road Segment Forecasts” (20062008) within framework of the VIKING-6 Projects.
Figure 5. Mean absolute error, MAE and bias, BIAS of the road surface (Ts), air (Ta), and dew point (Td) temperatures for the recent road weather seasons.
New possibilities have appeared to improve very local forecasts. Until now very detailed information of the orography and physiography or the terrain have not been available. Recently KSM (Kort og matrikel styrelsen) have developed a very detailed database for the Danish area. This has resulted in a very high resolution database of the geography of Denmark with a resolution of the surface of about 1-2 meter. Still the numerical weather model which form the base for road forecasting have a resolution of apx. 5 km and it will be increased to apx. 1+ km. This is good enough to predict the weather conditions such as cloud cover, precipitation and it also provide us with air temperature and wind of reasonable quality. However very local features like shadows, sky view angle and sheltering effects have very large impact on road surface temperature, wind and air temperature especially under certain weather conditions which typically results in a large variance in the road conditions. At the moment these effects are not well described in any NWP models. The idea would be: to use this new database and develop methods to predict these local features. Another source of local information which is of great importance are the road characteristics. References GlatTerm, 2004: Brugervejledning til GlatTerm version 2.73. Technical Documentation for the DMI Road Weather Modelling system, Sep 2004, 31p. Sass, B. 1992: A Numerical Model for Prediction of Road Temperature and Ice. Journal of Applied Meteorology, 31, pp. 1499-1506. Sass, B. 1997: A Numerical Forecasting System for the Prediction of Slippery Roads. Journal of Applied Meteorology, 36, pp. 801-817. Petersen C., Sass B., Mahura A., T.S. Pedersen, 2007: Road Weather Model System : Verification for 2005-2007 Road Weather Seasons. DMI Technical Report, 07-11, 19 p. Mahura A., C. Petersen, B. Sass, P. Holm, T. Pedersen, 2007: Road Stretch Weather Forecasting: Thermal Mapping Data Applicability. DMI Scientific Report, 07-07, 33 p.
