Abstract. In Norway, 30 % of the annual precipitation falls as snow.
Knowledge of the snow reservoir is therefore important for energy production
and water resource management. The land surface model SURFEX with the
detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid
spacing of 1 km over an area in southern Norway for 2 years (1 September
2014–31 August 2016). Experiments were carried out using two different
forcing data sets: (1) hourly forecasts from the operational weather forecast
model AROME MetCoOp (2.5 km grid spacing) including post-processed
temperature (500 m grid spacing) and wind, and (2) gridded hourly observations
of temperature and precipitation (1 km grid spacing) combined with
meteorological forecasts from AROME MetCoOp for the remaining weather
variables required by SURFEX/Crocus. We present an evaluation of the modelled
snow depth and snow cover in comparison to 30 point observations of snow depth
and MODIS satellite images of the snow-covered area. The evaluation
focuses on snow accumulation and snowmelt. Both experiments are capable of
simulating the snowpack over the two winter seasons, but there is an
overestimation of snow depth when using meteorological forecasts from AROME
MetCoOp (bias of 20 cm and RMSE of 56 cm), although the snow-covered area
in the melt season is better represented by this experiment. The
errors, when using AROME MetCoOp as forcing, accumulate over the snow season.
When using gridded observations, the simulation of snow depth is
significantly improved (the bias for this experiment is 7 cm and RMSE 28 cm),
but the spatial snow cover distribution is not well captured during the
melting season. Underestimation of snow depth at high elevations (due to the
low elevation bias in the gridded observation data set) likely causes the
snow cover to decrease too soon during the melt season, leading to
unrealistically little snow by the end of the season. Our results show that
forcing data consisting of post-processed NWP data (observations assimilated
into the raw NWP weather predictions) are most promising for snow
simulations, when larger regions are evaluated. Post-processed NWP data
provide a more representative spatial representation for both high mountains
and lowlands, compared to interpolated observations. There is, however, an
underestimation of snow ablation in both experiments. This is generally due
to the absence of wind-induced erosion of snow in the SURFEX/Crocus model,
underestimated snowmelt and biases in the forcing data.
Remote Sensing of Snow in High Mountain Basins in Norway
