Statistics of convective cloud turbulence from a comprehensive turbulence retrieval method for radar observations
Statistics of convective cloud turbulence from a comprehensive turbulence retrieval method for radar observations
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DOI：
10.1002/qj.3462
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发表时间：
2019-01
影响因子：
8.9
通讯作者：
Matthew M. Feist;C. Westbrook;P. Clark;T. Stein;H. Lean;A. Stirling
中科院分区：
文献类型：
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作者：
Matthew M. Feist;C. Westbrook;P. Clark;T. Stein;H. Lean;A. Stirling
湍流混合过程在决定对流云的演变和对流降水的产生方面是重要的。然而，由于观察有限，这些影响的确切性质仍然不确定。模式模拟表明，在参数化湍流的假设可以有一个显着的影响模拟云的特性。这导致对流允许数值天气预报（NWP）模式的预报存在显著的不确定性。本文提出了一种利用多普勒天气雷达研究观测云中湍流的综合方法。该方法包括分离多普勒速度谱宽度的湍流分量，将湍流强度表示为涡流耗散率，通过将这种方法应用于使用（0.28°波束宽度）Chilbolton高级气象雷达（CAMRA）在英国南部收集的大型观测数据集，给出了对流云湍流的统计数据。两个对比的情况下天检查：一个浅的“阵雨”的情况下，和一个“深对流”的情况下，表现出更强，更深的上升气流。在我们的观测中，平均风速通常在10−3到10−1 m2/s3之间，最大值出现在对流上升气流内部、周围和上方。垂直廓线表明，深层对流中的湍流要强得多;第95百分位值随高度从0.03增加到0.1 m2/s3，而在整个阵雨云的深度中，近似恒定的值为0.02-0.03 m2/s3。在这两天的上升气流区，95百分位数的风切变与上升气流速度和上升气流速度中的水平切变有显著的正相关关系（p < 10−3），与上升气流尺度的正相关关系较弱。所提出的ε-检索方法考虑了非常广泛的条件，为使用高分辨率多普勒天气雷达进行湍流检索提供了可靠的框架。在许多观测中应用这种方法时，导出的湍流统计数据将形成评估NWP模式中湍流参数化的基础。
Turbulent mixing processes are important in determining the evolution of convective clouds, and the production of convective precipitation. However, the exact nature of these impacts remains uncertain due to limited observations. Model simulations show that assumptions made in parametrizing turbulence can have a marked effect on the characteristics of simulated clouds. This leads to significant uncertainty in forecasts from convection‐permitting numerical weather prediction (NWP) models. This contribution presents a comprehensive method to retrieve turbulence using Doppler weather radar to investigate turbulence in observed clouds. This method involves isolating the turbulent component of the Doppler velocity spectrum width, expressing turbulence intensity as an eddy dissipation rate, ϵ. By applying this method throughout large datasets of observations collected over the southern United Kingdom using the (0.28° beam‐width) Chilbolton Advanced Meteorological Radar (CAMRa), statistics of convective cloud turbulence are presented. Two contrasting case days are examined: a shallow “shower” case, and a “deep convection” case, exhibiting stronger and deeper updraughts. In our observations, ϵ generally ranges from 10−3 to 10−1 m2/s3, with the largest values found within, around and above convective updraughts. Vertical profiles of ϵ suggest that turbulence is much stronger in deep convection; 95th percentile values increase with height from 0.03 to 0.1 m2/s3, compared to approximately constant values of 0.02–0.03 m2/s3 throughout the depth of shower cloud. In updraught regions on both days, the 95th percentile of ϵ has significant (p < 10−3) positive correlations with the updraught velocity, and the horizontal shear in the updraught velocity, with weaker positive correlations with updraught dimensions. The ϵ‐retrieval method presented considers a very broad range of conditions, providing a reliable framework for turbulence retrieval using high‐resolution Doppler weather radar. In applying this method across many observations, the derived turbulence statistics will form the basis for evaluating the parametrization of turbulence in NWP models.