Observing and forecasting road surface temperatures

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http://hdl.handle.net/10138/306438
Title: Observing and forecasting road surface temperatures
Author: Karsisto, Virve
Date: 2019-10
Belongs to series: Finnish Meteorological Institute Contributions 153
ISBN: 978-952-336-079-2
ISSN: 0782-6117
URI: http://hdl.handle.net/10138/306438
Abstract: Wintertime weather conditions can be hazardous for road traffic. Icy roads and poor visibility caused by snowfall increase the accident risk. Accurate forecasting of road conditions is important, because reliable and precise forecasts help the road maintenance personnel to plan their operations accordingly. Well timed maintenance operations increase safety and enable economical savings as unnecessary actions can be avoided. Drivers can also adjust their route plan and driving behaviour appropriately when warnings of hazardous conditions are given well beforehand. Road conditions are forecasted in the Finnish Meteorological Institute (FMI) with specialized road weather model. Before executing the actual forecast, the model is first initialized by feeding it with observation data. The quality of this data is essential for forecast accuracy, as the forecast is greatly dependent of the initial model state. Road weather stations have traditionally been one of the main sources of information, but their density is sparse especially in rural areas. Road surface temperature can vary considerably across the road network, so observations should be done in dense enough spatial scale. Nowadays it is possible to gather real time information from vehicles. Mobile sources provide observations with high spatial density and thus facilitate detecting the road stretches most prone to freezing. However, the quality of mobile observations should be assessed before implementing them to the road weather forecasting systems. This dissertation aims to answer to two research questions. Firstly, it has been studied how to best use available surface temperature observations in the road weather model initialization. Secondly , it has been studied how differences in two road weather models’ physics affect to the surface temperature forecast accuracy. A method called coupling was implemented to the FMI road weather model. The main idea of the method is to adjust the incoming radiation flux so that the modelled surface temperature fits to the last observed value. The results show that this method improves considerably the short range surface temperature forecasts. Mobile surface temperature observations done with Teconer RTS411 were compared to road weather station measurements to assess the mobile data quality. According to the results, the mobile observations were on average 0.62 °C warmer than the road weather station measurements at 0 °C and in dry conditions. It was found out that the difference between mobile observations and road weather station measurements was dependent on the road status. A calibration equation for mobile observations was developed using linear mixed models to get mobile observations more in line with road weather station measurements. The effect of the mobile observations to the road surface temperature forecast accuracy was studied. According to the results, using the mobile observations calibrated with the developed equation improved the accuracy of road surface temperature forecasts compared to a theoretical situation where there would not be other surface temperature observations available. However, for an area with a dense road weather station network the accuracy of forecasts assimilating mobile observations with correction were on par with the accuracy of forecast assimilating interpolated surface temperature values. Studying model physics and comparing behaviour of different models is beneficial for model development. In this work, the verification results of the FMI’s and the Royal Netherlands Meteorological institute’s (KNMI) road weather models were compared to each other. In addition, the model physics were studied to find out the reasons for differences in the surface temperature forecasts. The forecasts of the KNMI model were found to be slightly more accurate than the forecasts of the FMI model. Although the core physics of the models were rather similar, there were large differences in some physical parameters and the number and the thickness of the ground layers. Individual reason for the better performance of the KNMI model could not be found, as the effects of different physical properties eventually sum up to surprisingly similar modelled surface temperature values.
Subject: Road weather
Road surface temperature
Mobile observations
Energy balance model
Road weather forecast
Model comparison
Forecast verification


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