Sensor accuracy maintenance and calibration

Normally at a typical outstation several road surface sensors are linked to a single set of atmospheric sensors which measure wind speed and

Table 3.3 Possible errors in the measurement of road surface temperature by surface

sensors

Note: Systematic errors can be eliminated or minimised by careful design of the sensors and associated communication systems (e.g. 1, 2, 3, 4, 5, 6, 7, 10), but the other errors may be random and are more difficult to deal with.

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direction, air temperature and humidity, precipitation and perhaps radiation. The atmospheric sensors are often considered optional by the engineers, but are very useful to the meteorologist.

The temperature measurements are normally expected to have an accuracy of 0.5°C, with a resolution of 0.1 °C. The wind measurements are expected to be within ±5% accuracy, and the relative humidity within ±2%. It is very difficult to check the accuracy of the sensors without regular calibration and maintenance visits. Humidity is particularly difficult to measure accurately (especially automatically), and monthly calibration is ideally required. Preventive maintenance is ideal, and a compromise is to calibrate all the sensors every two months in winter with a minimum of once at the start of the winter and a second in January or February. The Department of Transport protocol MCE2020G (Department of Transport 1988a) gives minimum standards for sensor performance. Road sensors should ideally be calibrated just before dawn on a cloudy night with a wet road surface to reduce measurement errors.

Automatic self-calibration checks are now possible for temperature measurements which may speed up calibration visits, and it is hoped that eventually all the sensors will be self-calibrating.

An international standard for road/atmospheric sensor accuracy may be forthcoming from the COST 309 programme, so that manufacturers of equipment are competing equally. A more difficult matter is the policing of such standards: certificates of compliance provided by manufacturers provide one solution.

Figure 3.8a shows the highway authorities that have installed road weather outstations in the UK, and Figure 3.8b shows those countries that had installed outstations up to the end of 1990.

3.4 COMPUTER AND COMMUNICATION NETWORKS

Information from sensors and weather offices has to be combined and distributed to the engineer, other emergency services and the general public if required. Several countries have experimented with some form of teletext service to distribute the information as widely as possible. As yet teletext graphics and communications are slow but no doubt improvements will prove this to be a popular solution. Teletext systems normally allow information to be displayed only, and not manipulated, unless the information is downloaded onto a computer. This has meant the development of dedicated computer systems and networks for road weather, leading to a proliferation of terminals on an engineer’s desk, unless existing computer equipment has been utilized. Micro-computers

Figure 3.8 (a) Number of outstations in the UK by county/region and (b) number of outstations by country.

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are obviously popular in that they can be used for a variety of purposes: terminals to minicomputers or mainframes, stand-alone word processors/report generators, and terminals to a variety of applications such as road-weather, and other highway maintenance activities. These ‘workstations’ as they have come to be known, can also access teletext, act as docfax/telex machines etc. if required. The problem is one of universal compatability, although IBM compatability has gone a long way towards solving this problem. To use a road-weather system requires the engineer to become familiar with computers but this is less of a problem than it was.

Most road-weather systems require the highway authorities to have their own central processor unit, or central station, to collect the sensor data at regular intervals. This is normally accomplished via a modem link using conventional telephone networks. In certain circumstances it may be possible to use a radio link, especially for small line-of-site systems. In the future satellite communication will be common as it becomes more cost-effective.

The central processor unit has to communicate the sensor information to the weather office and local workstations. A clearly defined communication protocol has been developed in the United Kingdom to enable weather offices to communicate with any commercially available sensor system. This has been in successful operation since 1986. More recent developments in the United Kingdom have eliminated the need for all authorities to have their own central processor units. At present in the United Kingdom each highway authority is responsible for its own commitment to ice prediction systems, but there are already signs of regional systems developing, as in Wales (Perry and Symons 1986). Also several computer bureaux have been installed in both weather centres and manufacturer’s offices to facilitate communication between highway authorities and also to keep costs down.

3.5 ICE-PREDICTION MODELS

The Open Road service provided by the UK Meteorological Office is distributed to more than fifty counties/regions by fourteen weather centres. An ice-prediction computer model is run for forecast sites in defined climatic zones within each county/region. These forecasts are monitored to assess their accuracy, and to provide feedback to the forecaster as to how well the road surface temperature and wetness are modelled. Figure 3.9 shows the forecast accuracy for part of the winter of 1987/88 for the Ray Hall outstation in the West Midlands. Figure 3.10 shows the mean forecast accuracy in frost prediction (whether or not the

Thermal mapping and road-weather systems 6 0

road-surface temperature fell below zero) for eight forecast sites in three counties, issued by Birmingham Weather Centre during the winter of 1988/89. Two forecast curves are issued, one realistic and one pessimistic. The realistic forecast is produced from the forecasters’ best estimate of the likely weather conditions which are fed into the computer model as shown in Table 3.4. The pessimistic forecast repre-sents the worst scenario

Table 3.4 Forecast input sheet for road surface temperature model

Figure 3.9 Forecast accuracy for the Ray Hall outstation in the West Midlands, winter 1987/88.

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that is likely to happen on a given night. Figure 3.9 shows that the mean error in the minimum road-surface temperature forecast (for 113 nights) for the realistic curve was –0.3°C, and for the pessimistic curve was –3.4°C. Figure 3.10 shows that for the 151 nights considered, the accuracy of the realistic forecast in determining whether or not the road surface temperature would fall below zero was 87.1%; for the pessimistic forecast it was 68.8%. There are two types of error in the forecast that are highlighted in Figure 3.10. The shaded area represents those nights (8.7%) when frost was forecast but no frost occurred, and the solid area (4.2%) those nights when no frost was forecast but frost actually occurred. A frost in this context just represents road-surface temperatures falling below zero. The potential consequences of these two types of error are very different:

Type 1 error: No frost forecast/frost occurs: potential accidents Type 2 error: Frost forecast/no frost occurs: potential wasted salt Obviously one wants to reduce both types of error to a minimum but the Type 1 error is the more serious. Approximately 33% of the errors in the realistic forecast are Type 1 errors (4.2% out of 12.9%). If both curves were used when most appropriate the potential accuracy was 91.5%, with a type 1 error on just one night. (Thornes and Fairmainer, 1989; Thornes, 1989).

Further research (Thornes and Shao, 1991) is leading to an improvement in the ice prediction models, not just for road-surface temperature modelling, but also for improved prediction of the

Figure 3.10 Mean forecast accuracy in frost prediction, Birmingham Weather Centre. Data compiled for eight forecast sites, in three counties, for winter 1988/ 89. Segment names: forecast condition/actual condition, (a) Realistic forecast. Percentage correct forecasts: 87.1% and (b) Pessimistic forcast. Percentage correct forecasts: 68.8%.

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occurrence and timing of ‘wet frosts’. Two models have been used in the Open Road programme, the Thornes model developed at the University of Birmingham (Thornes, 1985; Parmenter and Thornes, 1986), and the Meteorological Office model (Rayer, 1987). Both models have been the subject of much recent research, and Table 3.5 shows results for 65 nights at Chapmans Hill on the M5 compared to a new model developed by Thornes and Shao (1991) called Icebreaker. Note that these results are for both retrospective modelling (RSP) using actual figures for air temperature, cloud, wind, humidity and precipitation observed at the University of Birmingham and for real time forecasting (RTP) using open road input data from Birmingham Weather Centre.

3.6 COST/BENEFIT OF ROAD-WEATHER SYSTEMS

It is always difficult to carry out an objective analysis of the benefits of a road-weather system. The costs of installation and maintenance are clearly easier to arrive at. The benefits of an effective winter-maintenance service, such as reduced travel time, reduced accidents, and reduced environmental damage, are difficult to quantify in the short term.

Table 3.5 Comparison of Retrospective (RSP) and ‘Real-time’ (RTP) Runs

(Chapman’s Hill, winter 1988/89, 65 days)

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Nevertheless, the few studies that have been carried out, for instance in Finland (Ministry of Communication, Finland 1982) and in the United Kingdom (Thornes 1989), suggest that considerable benefits are immediately apparent following the installation of a roadweather system.

3.6.1 Winter index versus salt usage in two UK counties