Observing and forecasting road surface temperatures

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http://urn.fi/URN:ISBN:978-952-336-079-2
Title: Observing and forecasting road surface temperatures
Author: Karsisto, Virve
Contributor: University of Helsinki, Faculty of Science, Department of Physics
Doctoral Programme in Atmospheric Sciences
Finnish Meteorological Institute
Publisher: Helsingin yliopisto
Date: 2019-11-01
URI: http://urn.fi/URN:ISBN:978-952-336-079-2
http://hdl.handle.net/10138/305417
Thesis level: Doctoral dissertation (article-based)
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.Talviset sääolosuhteet, kuten jäiset tiet ja lumisateen aiheuttama heikko näkyvyys, lisäävät onnettomuuksien riskiä. Teiden turvallisena pitämisessä auttavat tarkat ja luotettavat tiesääennusteet, joiden avulla auraukset ja suolaukset voidaan suunnitella hyvissä ajoin. Ilmatieteen laitos ennustaa keliolosuhteita kyseistä tarkoitusta varten kehitetyn tiesäämallin avulla. Väitöstyössä tutkittiin, miten pintalämpötilahavaintoja voidaan parhaiten käyttää tiesäämallin alkutilan määrityksessä. Perinteisesti havaintojen tärkein lähde ovat olleet tiesääasemat, mutta nykyisin on mahdollista saada reaaliaikaisia havaintoja uusista lähteistä, kuten autoista. Mobiilimittausten laatua selvitettiin vertaamalla autoon kiinnitettävällä laitteella tehtyjä tienpintalämpötilamittauksia tiesääasemien mittauksiin. Tulosten mukaan mobiilihavainnot olivat keskimäärin 0.62 ͦC tiesääasemahavaintoja lämpimämpiä, kun tie oli kuiva ja lämpötila nollassa. Tutkimuksessa havaittiin, että ero mobiilien ja tiesääasemamittausten välillä oli riippuvainen siitä, oliko tienpinta kuiva vai esimerkiksi märkä tai jäinen. Tutkimuksessa kehitettiin kalibrointiyhtälö mobiilihavaintojen korjaamiseksi. Mobiilihavaintojen vaikutusta tienpintalämpötilaennusteen tarkkuuteen tutkittiin vertaamalla mobiileja tienpintalämpötilahavaintoja hyödyntävää ennustetta kahteen kontrolliennusteeseen. Kalibroitujen mobiilihavaintojen käyttö paransi ennusteita verrattuna teoreettiseen tilanteeseen, jossa tienpintalämpötilahavaintoja ei ollut saatavilla. Sen sijaan mobiilihavaintoja käyttävät ennusteet antoivat suunnilleen yhtä tarkkoja ennusteita kuin interpoloituja tienpintalämpötilahavaintoja käyttävä malliajo ennustealueella, jolla on tiesääasemia tiheässä. Väitöstyössä verrattiin lisäksi Suomen Ilmatieteen laitoksen ja Alankomaiden Ilmatieteen laitoksen tiesäämallien ennustetuloksia keskenään. Myös mallien fysikaalisia ominaisuuksia verrattiin, jotta saataisiin selville, mistä erot tienpintalämpötilaennusteissa johtuvat. Alankomaiden mallin tienpintalämpötilaennusteiden havaittiin olevan hieman tarkempia kuin Suomen Ilmatieteen laitoksen mallin. Vaikka perusfysiikka oli melko samankaltainen molemmissa malleissa, joittenkin fysikaalisten parametrien arvoissa oli suuria eroja. Fysikaalisista eroista huolimatta eri tekijät malleissa summautuvat siten, että mallien ennustama pintalämpötila oli huomattavan samanlainen.
Subject: Meteorologia
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