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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-330
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-330
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model evaluation paper 01 Feb 2019

Model evaluation paper | 01 Feb 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

The road weather model RoadSurf driven by the HARMONIE-Climate regional climate model: evaluation over Finland

Erika Toivonen1, Marjo Hippi1, Hannele Korhonen1, Ari Laaksonen1,2, Markku Kangas1, and Joni-Pekka Pietikäinen1 Erika Toivonen et al.
  • 1Finnish Meteorological Institute, Helsinki, Finland
  • 2Department of Applied Physics, University of Eastern Finland, (PL 1627) 70211 Kuopio, Finland

Abstract. In this paper, we evaluate the skill of the road weather model RoadSurf to reproduce present-day road weather conditions in Finland. RoadSurf was driven by meteorological input data from a high-resolution regional climate model (RCM) HARMONIE-Climate (HCLIM) utilizing ALARO physics (HCLIM-ALARO). Simulated road surface temperatures and road surface conditions were compared to observations between 2002 and 2014 at 25 road weather stations located in different parts of Finland. The main characteristics of road weather conditions were accurately captured by RoadSurf in the study area. For example, the model precisely simulated road surface temperatures with a mean bias of −0.3 °C, RMSE of 2.1 °C, and Pearson's correlation coefficient of 0.93. The RoadSurf's output bias most probably stemmed from the lack of road maintenance operations in the model, such as snow ploughing and salting, and the biases in the input meteorological data. The biases in the input data were most evident in northern parts of Finland, where the regional climate model HCLIM-ALARO overestimated precipitation and had a warm bias in simulated air temperatures during the winter season. In turn, these input data biases seemed to result in a warm bias in simulated road surface temperatures. Furthermore, the lack of road maintenance operations in the model might have affected RoadSurf's ability to simulate road surface conditions: the model tended to overestimate icy and snowy road surfaces and underestimate the occurrence of water on the road. However, the overall good performance of RoadSurf implies that this approach can be used to study the impacts of climate change on road weather conditions by forcing RoadSurf by future climate projections from RCMs, such as HCLIM.

Erika Toivonen et al.
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Erika Toivonen et al.
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Short summary
We evaluated the skill of the road weather model RoadSurf to reproduce present-day road weather conditions in Finland when driven by a high-resolution regional climate model. Simulated road surface temperatures and conditions were compared to observations between 2002 and 2014 at 25 Finnish road weather stations. RoadSurf accurately captured the main characteristics of road weather conditions. Thus, this model can be used to study the future scenarios of road weather in the study area.
We evaluated the skill of the road weather model RoadSurf to reproduce present-day road weather...
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