青藏高原地形重力波拖曳的初步分析及数值模拟研究

Preliminary analysis of the gravity wave drag on Qinghai-Tibet Plateau and its numerical simulation

  • 摘要: 针对目前对青藏高原大地形激发的重力波拖曳相关问题还不十分清楚,在GRAPES_Meso模式中引入次网格地形重力波拖曳参数化方案,通过数值试验初步研究了青藏高原地区次网格地形重力波拖曳的一些相关参数,结果指出:(1)沿30°N地形重力波拖曳的垂直分布显示,阻塞拖曳主要存在于模式的低层(第1—5层),重力波拖曳主要存在于模式的第5—10层;从水平分布看,模式第3层以阻塞拖曳为主,主要位于青藏高原边缘地区,阻塞拖曳大值区沿喜马拉雅山脉走向和青藏高原东坡;模式第5层以重力波拖曳为主,主要位于青藏高原东部地区和云贵高原的北部边缘。(2)弗劳德数和气流绕流高度分析表明,在青藏高原喜马拉雅山脉一带和高原东部边缘地区,气流爬坡能力强,同时在这一地区绕流高度最高;弗劳德数越大的地区绕流高度距离地表越高。(3)采用次网格地形重力波拖曳参数化方案后,对于低层和高层地形重力波破碎的发生有更准确的描述,地形重力波是向上垂直传播的。(4)个例和批量试验检验结果表明,采用次网格地形重力波拖曳参数化方案对于风场和降水模拟有正效果,提高了模式预报的准确率。

     

    Abstract: The gravity wave drag triggered by the Tibetan Plateau remains unclear at present. To address the problem, a parameterization scheme for subgrid-scale orographic gravity wave drag was introduced into the GRAPES Meso and a suite of numerical experiments were conducted. Several conclusions from the results are as follows. (1) According to the vertical distribution of orographic gravity wave drag along 30°N, the blocking drag mainly exists in the lower levels (from level 1 to 5) while the gravity wave drag mainly exists between level 5 and level 10. According to the horizontal distribution, the blocking drag, which is dominant on level 3, mainly exists in the flanks of the Tibetan Plateau. Large values of blocking drag are located from the eastern Tibetan Plateau to northern Yunnan-Guizhou Plateau. (2) Analysis of the Froude number and the altitude of circumfluent flow show a large gradability, and thus the highest altitude of circumfluent flow is located at the area of Himalayas and eastern flank of the Tibetan Plateau. The larger the Froude number is over a specific area, the higher the altitude of circumfluent flow is in the area. (3) With the adoption of the subgrid-scale orographic gravity wave drag parameterization scheme, the model results reflect more accurate representation of the breaks of orographic gravity waves in lower and higher levels, as well as its upward transport. (4) Moreover, both the single case study and the batch experiments show positive impacts on the simulation of wind field and precipitation, which leads to the improvement of model prediction accuracy.

     

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