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首页> 外文期刊>Journal of Geophysical Research, D. Atmospheres: JGR >Noah land surface model modifications to improve snowpack prediction in the Colorado Rocky Mountains
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Noah land surface model modifications to improve snowpack prediction in the Colorado Rocky Mountains

机译:诺亚地表模型修改提高积雪在科罗拉多洛基的预测山

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Simulated snowpack by the Noah land surface model (LSM) shows an early depletion due to excessive sublimation and too early onset of snowmelt. To mitigate these deficiencies, five model modifications are tested to improve snowpack performance: (1) time-varying snow albedo, (2) solar radiation adjustment for terrain slope and orientation, (3) reducing the surface exchange coefficient for stable boundary layers, (4) increase of fresh snow albedo, and (5) adjusting surface roughness length when snow is present. The Noah LSM is executed from 1 November 2007 to 1 August 2008 for the headwater region in the Colorado Rocky Mountains with complex terrain, and its results are evaluated against 1 km Snow Data Assimilation System (SNODAS) output and individual Natural Resources Conservation Service Snowpack Telemetry (SNOTEL) sites. The most effective way to improve magnitude and timing of seasonal maximum snow water equivalent (SWE) is the introduction of the timevarying albedo formulation and the increase in fresh snow albedo. Minor improvement is obtained by reducing nighttime sublimation through adjusting the stable boundary layer surface exchange coefficient. Modifying the surface roughness length over snow surfaces and adding a terrain slope and orientation adjustment for radiation has little effect on average SWE conditions for the entire modeling domain, though it can have a significant effect in certain regions. The net effect of all changes is to improve the magnitude and timing of seasonal maximum SWE, but the snow period end is now somewhat too long. Adding the terrain slope and orientation effects does have an effect on local surface energy flux components depending on the cell slope and orientation.
机译:诺亚地表积雪模拟的模型(LSM)展示了一个早期由于过度消耗升华和融雪的过早发生。减轻这些不足,五个模型修改测试改善积雪性能:(1)时变积雪反照率,(2)太阳辐射对地形坡度和调整取向,(3)减少表面交换系数稳定边界层,(4)增加新鲜的积雪反照率,(5)调整表面粗糙度长度当雪。诺亚LSM是从2007年11月1日执行2008年8月1日的源头地区科罗拉多洛矶山脉和复杂的地形,和结果评估1公里雪数据同化系统(SNODAS)和输出个人自然资源保护服务积雪遥测(SNOTEL)网站。有效改善的大小和时间的方法季节性雪最大的水当量(理念)的引入timevarying反照率制定和增加新鲜的雪反照率。通过调整夜间升华稳定边界层表面交换系数。长度在雪表面和添加一个地形斜率和方向调整辐射平均瑞典文条件没有影响呢整个建模领域,尽管它可以有一个在某些地区显著的影响。的影响是提高级的所有更改和时间的季节性最大瑞典文,但雪期末现在有点太长了。地形坡度和取向有影响影响当地地表能量通量组件根据细胞的斜率和取向。

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