The road weather model RoadSurf (v6.60b) driven by the regional climate model HCLIM38: evaluation over Finland
2019
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 cycle 38 of the
high-resolution regional climate model (RCM) HARMONIE-Climate (HCLIM38) with
ALARO physics (HCLIM38-ALARO) and ERA-Interim forcing in the lateral
boundaries. 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 simulated road surface temperatures with a mean
monthly bias of −0.3 ∘ C and mean absolute error of 0.9 ∘ C. The RoadSurf's output bias most probably stemmed from
the absence of road maintenance operations in the model, such as snow
plowing 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 HCLIM38-ALARO overestimated precipitation
and had a warm bias in near-surface air temperatures during the winter
season. Moreover, the variability in the biases of air temperature was found
to explain on average 57 % of the variability in the biases of road
surface temperature. On the other hand, the absence 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 in Finland
by forcing RoadSurf with future climate projections from RCMs, such as HCLIM.
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