XRWIS: the use of geomatics to predict winter road surface temperatures in Poland

Transcription

1 Meteorol. Appl. 1, 3 9 (5) doi:1.117/s x XRWIS: the use of geomatics to predict winter road surface temperatures in Poland John E. Thornes, Gina Cavan & Lee Chapman School of Geography, Earth and Environmental Sciences, University of Birmingham, UK j.e.thornes@bham.ac.uk A new method (XRWIS) to predict the minimum road surface temperature for the winter maintenance of roads, using geomatics, has been tested in Poland. A geographical database was constructed for a km test route from Krakow to the Slovakian border. A GPS survey to measure sky-view factor was carried out as part of the COST 719 project. A computer energy balance model IceMiser was run retrospectively to predict road surface temperature every minutes for every m along the road using hourly weather data recorded at two adjacent climate stations: at low elevation in Krakow and at high elevation in Zakopane, in March 3. A GIS was used to visualise the predicted road surface temperatures. The IceMiser model was verified by comparing the predicted road surface temperatures with measured road surface temperatures at a number of road weather outstations along the route. The results for four road weather outstations are discussed. The best results are for Libertow ( m) and Myslenice (3 m) close to Krakow, whereas the results for Skawa (51 m) and Piatkowa Gora (9 m) at high elevation are not as good, probably due to their distance from the Zakopane climate station. 1. Introduction Traditionally highway engineers around the world have used Road Weather Information Systems (RWIS) to help them decide when to salt roads under their jurisdiction. The engineer has to interpret road weather forecasts for site-specific locations in order to decide which salting routes need salting, at what time, and how much salt or brine to use. Thermal mapping has been used to give the engineer a spatial view of the likely minimum road surface temperature along the salting routes (Thornes 1991). This RWIS approach has been used in Europe for almost years and has recently become the standard in North America as well. It is now time to consider how this RWIS model can be improved to take on board more recent developments in geomatics, computer processing power, communications and especially the use of the internet. A new paradigm next generation Road Weather Information Systems (XRWIS) has been developed at the University of Birmingham. This new system uses geomatic surveys to help create a geographical database every m to m (depending on topography) along each salting route (latitude, longitude, altitude, sky-view factor, thermal map residual, aspect, slope, cold air drainage, road construction, traffic, land use, etc.). A mesoscale weather forecast is then used to provide a road weather forecast (using the IceMiser energy balance model) for each individual salting route so that the engineer is presented with a route-based forecasting system (Thornes et al. 5). This paper represents the results of a retrospective trial of XRWIS route-based forecasting in Poland. It is important to show that the methodology works in climates outside the UK where the method was developed. Other XRWIS trials have been carried out in Japan on Highway 17, where the major problem is snow rather than ice (Thornes et al. ).. The Polish XRWIS trial Poland has a transitional climate with both maritime and continental influences. The dominance of maritime or continental air masses causes annual variations in the seasons. Winters are variable, sometimes mild and sometimes severe, and similarly, summers can be cool and rainy or hot and dry. Sub-zero road temperatures are usually recorded between November and March. Annual precipitation varies between the lowlands and mountains, with totals ranging between 5 mm and mm. New technology has recently been invested in Poland s road network, including the installation of a network of about 7 road weather information systems (RWIS). This paper considers the use of a geomatic survey and the new road weather prediction model IceMiser that has been tested on a study road that links the city of Krakow to the mountainous border with Slovakia, as shown in Figure 1. This is a very busy road, approximately km in length, marked by a 3

2 John E. Thornes, Gina Cavan & Lee Chapman Figure 1. Study area in Krakow, Poland. variety of land uses, different types of road construction and changes in topography. There are 1 RWIS along the road and four have been used to verify the model. Krakow has an average winter temperature of 3 C and an average temperature in March of 1 C. This marginal winter weather in Poland creates a problem for road maintenance, and there is little emphasis on ice prediction to optimise salt use. This means that roads are often treated up to three or four times a day (Bartlett & Logiewa, ), at a great cost to the road maintenance budget and to the environment. The rapid growth of commercial off the shelf geomatics technology, including GIS and GPS, has enabled the development of a new ice prediction technique called IceMiser (Chapman & Thornes 3). The GIS program ArcView is used in this study to run the IceMiser model. 3. IceMiser numerical model Numerical models provide a tool for investigating inaccessible phenomena, such as environments in the future, which cause concern with regard to possible impacts of decisions made for future generations (Lane 3). There are two approaches to mathematical modelling: empirical and physically based. Lane (3) notes that it is possible to conceive of a continuum from models that are largely built around a set of observations (empirical models) to models that are built around a set of laws (physically based models). The IceMiser model was developed by Chapman et al. (1b), and comprises an empirical geographical database within a physically based numerical model. IceMiser accounts for the influence of site-specific geographical parameters on the micro-climatology of the road. The model simulates the energy transfer regime at a location by calculating the unique equilibrium temperature which balances the energy flow across a road surface: (1 α)(q+ q) + σ T sky σ T = LE + H + S where α is surface albedo, Q is direct beam solar radiation, q is diffuse radiation, σ is the Boltzman constant, T sky is the radiation temperature of the sky hemisphere, T is the surface temperature, LE is latent heat flux, H is sensible heat flux and S is heat flux to the road construction. Thus, it uses a zerodimensional energy balance approach (Chapman & Thornes 1) and meteorological data is combined with a high resolution geographical parameter database (incorporating sky view factor, land use and elevation data) in the forecast model, to predict the road surface condition at thousands of sites around the road network (at spatial and temporal resolutions of approximately metres and minutes respectively). This enables the RST to be displayed for any site along the road network, at any particular time (Chapman et al. 1b). This model is a vast improvement on existing techniques that utilise forecast thermal maps. Recent research on geographical parameters affecting RST has focused on the measurement of sky view factor (SVF) (e.g. Chapman et al. 1a; 1b; Bradley et al. ; Chapman & Thornes ), which has been found to be the dominant control on RST together with land use, at high levels of atmospheric stability (Chapman et al. 1b). The SVF is the proportion of visible sky at a location, and varies between zero (when the sky is completely obscured) and 1 (in an open area where the whole sky is visible). The SVF has an important role in the radiation budget, and influences RST by reducing the incoming solar radiation and controlling the loss of long-wave radiation

3 Table 1. Meteorological and geographical database inputs. XRWIS and the prediction of winter road surface temperatures Survey technique to obtain Meteorological data Geographical data geographical data RST at noon Latitude GPS Air temperature Longitude GPS Dew point Altitude GPS Wind speed Sky-view factor GPS Rainfall Cloud cover Cloud type Nine values at 1:, 15:, 1:, 1:, :, 3:, :, 9:, 1:. Eight values averaged over the time periods 1: 15:, 15: 1:,1: 1:, 1: :, : 3:, 3: :, : 9:, and 9: 1:. at night, through shading of road surfaces (Thornes 1991). Until recently, this parameter was difficult and time consuming to include in climate studies. However, advances in technology have allowed existing techniques using fish-eye imagery to be processed algorithmically, providing an instant measurement in real-time, as well as rapid and inexpensive methods of SVF collection entirely by using GPS (Chapman et al. ). Chapman & Thornes () note that due to climate data being typically point-source in nature, one of the biggest challenges facing meteorology is the extrapolation of point climate data across a wide spatial domain. This can be overcome by the extraction of climate data using digital terrain models (DTMs), which enable a good estimate of an area s climatology without the need for extensive climate records and networks of weather stations. Unfortunately, a DTM was not available for this study, and instead, meteorological data from two climate stations were chosen as representative of two geographical regions.. Meteorological database The meteorological database is comprised of retrospective data from two climate stations in Poland: Krakow and Zakopane. The meteorological data input into the model are listed in Table 1. These parameters were recorded hourly and were obtained for the month of March 3. Retrospective data were chosen because this would identify the validity of the IceMiser model, rather than highlight errors in the forecasting of meteorological variables. Road surface temperature data was obtained from 1 RWIS along the route, and was recorded at 1-minute intervals. The locations of these RWIS are shown in Figure. Validation data for road surface temperature were also used from the RWIS. A simple cloud classification procedure was used to assign cloud types to three classes as supplied by the Polish Meteorological Office. 5. Geographical database The geographical database is comprised of variables collected using a Geomatic Survey carried out in April 3, during a short-term scientific mission (STSM) for COST 719. The geographical variables collected and the method of data collection are listed in Table 1. Land use, road construction type, aspect, slope, drainage and topography were not collected for this trial. These parameters have been set to default values in the model. This does affect the accuracy of the results, as results will be most accurate with the maximum input into the model. These parameters could be collected at a later stage and input into the model to improve these preliminary results. The geographical variables provided included eastings, northings, altitude, latitude and sky view factor, and enabled the creation of a shapefile. This enabled the road to be displayed spatially in ArcView as a series of points. Each point has a field representing the geographical data which were input into the database created in Excel. This allowed the creation of two separate shapefiles for each of the geographical regions of Krakow and Zakopane, based on altitude. Experiments with the number of classes (using the natural breaks technique) indicated a clear divide in the road at an altitude of m. Krakow was chosen to be representative of the lower topography ( m). Zakopane is situated on the mountainous Polish border, and was chosen to represent mountainous topography and climate (+ m).. The accuracy of the IceMiser model forecasts The IceMiser model was run for two RWIS from each section of the road (four RWIS in total) and the results were compared with actual RST data from the RWIS. A -hr forecast was made for each day in March for each of the meteorological stations (Krakow and Zakopane), and this was displayed in ArcView as a series of colourcoded maps. The nearest coordinate to each of the four selected RWIS along the road was obtained, and actual and forecast data were compared for each day. 5

4 John E. Thornes, Gina Cavan & Lee Chapman Figure. Location of the study area and RWIS. 7. Results from the IceMiser model IceMiser forecasts were displayed in ArcView as hourly thermal projections for each night. Figure 3 shows that the RST falls below zero first at the higher altitude sites around : h (at the settlement of Klikuszowa) and remains below freezing for about 1 hours. These are the coldest areas of the route throughout the night. Krakow city centre remains above freezing throughout the night, and the RST along the route does not fall below freezing until it reaches the settlement of Lubien (see Figure for location). It is clear that the route in the higher altitude area of Zakopane is colder than Krakow throughout the night. At 9: h

5 XRWIS and the prediction of winter road surface temperatures Figure 3. Hourly time-slices of the numerically predicted RST along the study route. 7

6 John E. Thornes, Gina Cavan & Lee Chapman Forecast and Actual Minimum RST /3 March 5. Forecast 3. Actual Forecast and Actual Minimum RST /5 March.. Forecast Actual Forecast and Actual Minimum RST 3/ March 5. Forecast 3. Actual Forecast and Actual Minimum RST 5/ March 1. Forecast. Actual Forecast and Actual Minimum RST 7/ March 1 Forecast and Actual Minimum RST /9 March 1. - Forecast Actual Forecast Actual -. Forecast and Actual Minimum RST 11/1 March 1. Forecast 1. Actual Forecast and Actual Minimum RST 1/13 March 1. 1 Forecast 1 Actual Figure. Sample forecast versus actual minimum RST at Libertow (. m). in the morning, the road is above freezing at all sites. This information is of significance for winter maintenance engineers. It indicates that the Zakopane region needs priority salting, and only selective salting needs to take place in the Krakow region around : h. The IceMiser model was run for each of the four stations for the number of nights available: Libertow (1), Myslenice (), Skawa (1) and Piatkowa Gora (5). Model outputs were then plotted against actual RST data from the RWIS. These were plotted in graphs of forecast versus actual RST, and samples are displayed in Figures and 5. Figure shows the mean hourly root mean square (RMS) error for Libertow. Table illustrates sample IceMiser results for Libertow. It can be seen that on most days, the model predicts RST extremely accurately, and the hourly RMS error decreases to below.5 C. The minimum RST occurs at Libertow at about : h every night, and the model predicts the temperature at this time very accurately, with an average RMS error of 1. C. Analysis of the frequency of snow at Libertow revealed that snow occurred on 1 of the 1 nights far more frequently than at the other road monitoring stations. Despite

7 XRWIS and the prediction of winter road surface temperatures (a) Forecast Minimum Forecast v Actual Minimum RST at Libertow Type 1 Type Actual Minimum (deg.c) (b) Forecast Minimum (deg.c) Forecast v Actual Minimum RST at Myslenice Type 1 Type Actual Minimum (deg.c) (c) Forecast Minimum (deg.c) Forecast v Actual Minimum RST at Skawa Type 1 Type Actual Minimum (deg.c) (d) Forecast Minimum (deg.c) Forecast v Actual Minimum RST at Piatkowa Gora Type 1 Type Actual Minimum (deg.c) Figure 5. Actual versus predicted minimum road surface temperature for (a) Libertow (b) Myslenice (c) Skawa (d) Piatkowa Gora. Degrees C Mean Hourly Root Mean Square Libertow March3 Hour Figure. Sample root mean square errors in the forecast for Libertow. suggestions that the model is insensitive to snow, these results indicate that a high accuracy is achieved at Libertow. Table 3 shows the overall results of forecast quality for the limited number of nights considered. The results for the two lower-level sites, Libertow and Myslenice, are better than those of the two higher-level sites. This is probably because the weather data for the high-level sites came from Zakopane which is higher and well to the south of the two forecast sites at Skawa and Piatkowa Gora. Forecasts for Myslenice, Skawa and Piatkowa Gora did contain a number of Type 1 errors (i.e. road temperature actually fell below zero when forecast to stay above zero) as shown in the miss rate. None of the sites had any Type errors (i.e. road temperature stayed above zero when forecast to fall below zero) as shown in a false alarm rate of at all sites. Libertow had a perfect set of forecasts with 1% accuracy in the trial.. Conclusions The results indicate that using a geomatic survey and the IceMiser model XRWIS was able to forecast RST for the study route in Poland with a high level of accuracy. At best, the model forecast RST with 1% accuracy, and a mean error of just.15 C. Additionally, the model was able to forecast very accurately the time of freezing and daily minimum RST 9

8 John E. Thornes, Gina Cavan & Lee Chapman Table. Actual and forecast minimum RST at Libertow. Libertow Actual Forecast Category 1/ March 1. F/F 1. /3 March.. F/F 3/ March F/F 1.1 /5 March.1 7. F/F 1.1 5/ March F/F. 7/ March F/F. /9 March /1 March /13 March F/F /1 March F/F.5 1/15 March F/F. 15/1 March /17 March F/F. /7 March / March /9 March /3 March /31 March 1... Mean F/F = forecast below zero correct. Table 3. Summary of forecast quality for each road station. Road station Libertow Myslenice Skawa Piatkowa Gora No. of nights % correct Bias Miss rate.1.1. False alarm rate (which occurred close to dawn each morning). Forecasts of RST using meteorological data from Krakow were found to be the most accurate, possibly due to the closer proximity of the RWIS, or the higher SVF in an urban area. Further research will enable real time trials of XRWIS route-based forecasting in Poland. Acknowledgements The authors would like to thank members of the COST 719 GIS in Meteorology and Climatology Management Committee, especially Hartwig Dobesch, Izabela Dyras, Zbigniew Ustmul and Mojca Dolinar References Bartlett, R. & Logiewa, R. () Poland: Looking to a Wider Europe. World Highway Road Notes. Online at Bradley, A. V., Thornes, J. E., Chapman, L., Unwin, D. & Roy, M. () Modelling spatial and temporal road thermal climatology in rural and urban areas using a GIS, Climate Research : Chapman, L. & Thornes, J. E. (1) A blueprint for 1st century road ice prediction. Proceedings of the 9 11th SIRWEC Conference, January, Sapporo, Japan. Chapman, L. & Thornes, J. E. () The use of geographical information systems in climatology and meteorology. Prog. in Phys. Geog. 7: Chapman, L. & Thornes, J. E. (3) Geomatics inspire winter maintenance revolution. APWA Reporter 7: 3. Chapman, L., Thornes, J. E. & Bradley, A. V. (1a) Modelling of road surface temperature from a geographical parameter database. Part 1: Statistical. Meteorol. Appl. : Chapman, L., Thornes, J. E. & Bradley, A. V. (1b) Modelling of road surface temperature from a geographical parameter database. Part : Numerical. Meteorol. Appl : 1 3. Lane, S. N. (3) Numerical modelling in physical geography: understanding, Explanation and Prediction. In: N. J. Clifford & G. Valentine (eds.), Key Methods in Geography, Sage Publications, 3 9. Thornes, J. E. (1991) Thermal mapping and road weather information systems for highway engineers. In: A. H. Perry & L. J. Symons (eds.) Highway Meteorology, London: E&FN Spon, Thornes, J. E., Chapman, L & Sato, N. () The prediction of winter road weather on a section of Highway 17 in Japan. Final Report (online at Thornes, J. E., Chapman, L. & White, S. (5) The X-Factor, The Surveyor 19, 1 1.