Spatial and temporal variability of turbulence dissipation rate in complex terrain
Spatial and temporal variability of turbulence dissipation rate in complex terrain
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DOI：
10.5194/acp-19-4367-2019
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发表时间：
2019-04
影响因子：
6.3
通讯作者：
Nicola Bodini;J. Lundquist;R. Krishnamurthy;M. Pekour;L. Berg;A. Choukulkar
中科院分区：
文献类型：
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作者：
Nicola Bodini;J. Lundquist;R. Krishnamurthy;M. Pekour;L. Berg;A. Choukulkar
摘要。为了改进数值天气预报模型中湍流耗散率（御柱）的参数化，必须评估御柱的时空变异性。在本研究中，我们探讨了2015年至2017年WFIP2野外试验期间，哥伦比亚河峡谷不同尺度上的λ变异性的影响。我们从部署在约4平方公里区域内的5个声速风速计以及2个扫描多普勒激光雷达和4个剖面多普勒激光雷达中计算出了λ，这些雷达的位置横跨约300公里宽的区域。我们使用二阶结构函数方法从声速计中获取λ，使用方位角结构函数方法从扫描激光雷达中获取λ，并使用一种使用视线速度方差的新技术从轮廓激光雷达中获取λ。湍流耗散率即使在微观尺度上也表现出较大的空间变异性，特别是在夜间稳定条件下。地形特征对御柱的变率影响较大，不同站点御柱之间的相关性受地形影响较大。在复杂地形结构的下风处或风电场尾流处，λ值较大。我们发现了一个明确的日循环，白天对流条件决定的值比夜间稳定条件高出一个数量级。柱也表现出明显的季节循环，夏季和冬季的平均柱值差异大于一个数量级。
Abstract. To improve parameterizations of the turbulence dissipation rate (ϵ) in numerical weather prediction models, the temporal and spatial variability of ϵ must be assessed. In this study, we explore influences on the variability of ϵ at various scales in the Columbia River Gorge during the WFIP2 field experiment between 2015 and 2017. We calculate ϵ from five sonic anemometers all deployed in a ∼4 km2 area as well as from two scanning Doppler lidars and four profiling Doppler lidars, whose locations span a ∼300 km wide region. We retrieve ϵ from the sonic anemometers using the second-order structure function method, from the scanning lidars with the azimuth structure function approach, and from the profiling lidars with a novel technique using the variance of the line-of-sight velocity. The turbulence dissipation rate shows large spatial variability, even at the microscale, especially during nighttime stable conditions. Orographic features have a strong impact on the variability of ϵ, with the correlation between ϵ at different stations being highly influenced by terrain. ϵ shows larger values in sites located downwind of complex orographic structures or in wind farm wakes. A clear diurnal cycle in ϵ is found, with daytime convective conditions determining values over an order of magnitude higher than nighttime stable conditions. ϵ also shows a distinct seasonal cycle, with differences greater than an order of magnitude between average ϵ values in summer and winter.