Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway
Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway
复制标题
将挪威南部的每小时气象预报和网格观测相结合，强制使用 SURFEX/Crocus 雪模型
DOI：
10.5194/tc-12-2123-2018
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
M. Homleid
中科院分区：
文献类型：
--
作者：
H. Luijting;Dagrun Vikhamar;T. Aspelien;Å. Bakketun;M. Homleid
抽象的。在挪威，年降水量的30%福尔斯以雪的形式落下.
因此，了解雪库对于能源生产非常重要
和水资源管理。陆面模式SURFEX，
详细的积雪方案Crocus（SURFEX/Crocus）已经运行了网格
在挪威南部一个地区上空间隔1公里，为期2年（9月1日
2014年至2016年8月31日）。实验使用两种不同的
强迫数据集：（1）业务天气预报的每小时预报
AROME MetCoOp模型（2.5 km网格间距），包括后处理
温度（500米网格间距）和风，和（2）网格每小时观测
温度和降水（1公里网格间距）结合
AROME MetCoOp对其余天气的气象预报
SURFEX/Crocus要求的变量。我们提出了一个评估的模型
雪深和积雪与30点雪深观测值的比较
和中分辨率成像光谱仪拍摄的冰雪覆盖地区的卫星图像。评价
重点是积雪和融雪。这两个实验都能
模拟了两个冬季的积雪，但是
使用AROME气象预报时对积雪深度的高估
MetCoOp（偏差为20 cm，RMSE为56 cm），尽管积雪覆盖区
这个实验更好地代表了融化季节。的
当使用AROME MetCoOp作为强迫时，误差在雪季累积。
当使用网格化观测时，雪深的模拟为
显著改善（本实验的偏差为7 cm，RMSE为28 cm），
但在冬季，
融化的季节在高海拔地区低估积雪深度（由于
网格化观测数据集中的低海拔偏差）可能导致
积雪在融化季节过早减少，导致
到赛季结束的时候雪会少得不切实际。我们的结果显示
强迫数据包括后处理的数值预报数据（观测同化
进入原始的NWP天气预测）是最有希望的雪
模拟，当较大的区域进行评估。后处理数值预报资料
为两座高山提供了更具代表性的空间表示，
和低地，与插值观察相比。然而，
在两个实验中雪消融的低估。这通常是由于
在SURFEX/Crocus模型中没有风引起的雪侵蚀，
低估了融雪和强迫数据的偏差。
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.
影响因子：
4.7
作者：
Essery, Richard;Morin, Samuel;Menard, Cecile B.
通讯作者：
Menard, Cecile B.