Identification of non-turbulent motions for enhanced estimation of turbulent transport using the anisotropy of atmospheric Turbulence

Anisotropy, derived from the Reynolds stress tensor, is closely related to turbulent energy and transport, and can be quantified using two parameters: xB and yB. Scale-dependent properties of anisotropy from diverse observational experiments are investigated with the help of the Hilbert-Huang transform method. The mean yB-xB trajectories in the barycentric map are visualized as curve clusters under different stratification conditions. Specifically, xB increases from 0.4 to 0.9 with the increasing scale of motions, while yB initially rises from 0.5 to 0.7 before decreasing to 0. Trajectories deviating from this pattern in individual cases help distinguish non-turbulent motions and reconstruct turbulence data. This analysis reveals approximately 30% to 80% overestimation of turbulent fluxes. Therefore, anisotropy demonstrates potential for quantifying turbulent transport in the atmospheric boundary layer., To investigate the effectiveness of anisotropy in reflecting characteristics of atmospheric turbulence, the data are selected from multiple observational sites across different underlying surfaces and in different seasons.
The 255-m meteorological tower in Tianjin is located in the PBL Meteorological Observation Station, Tianjin Meteorological Administration, which is representative of the typical urban landscape with profound anthropogenic influences. The turbulent data from sonic anemometers (CSAT-3, Campbell Scientific, Inc., Logan, UT, USA) are acquired from the meteorological tower at 80m orienting towards East, which can provide the three-dimensional wind speed and the sonic temperature data. The fast-response observation of gases are conducted with the Open Path CO2/H2O Analyzer (LI-7500, LI-COR, Inc., Lincoln, NE, USA) installed at 80m, providing density time series of CO2 and water vapor. The sampling frequencies of the instruments above are all 10 Hz. There are also obser..., , # Identification of Non-turbulent Motions for Enhanced Estimation of Turbulent Transport using the Anisotropy of Atmospheric Turbulence

[https://doi.org/10.5061/dryad.c866t1gh6](https://doi.org/10.5061/dryad.c866t1gh6)

## Description of the data and file structure

To investigate the effectiveness of anisotropy in reflecting characteristics of atmospheric turbulence, the data are selected from multiple observational sites across different underlying surfaces and in different seasons.

### Files and variables

#### File: Statistics.zip

**Description:** the csv and mat files that can be loaded into statistical software, containing basic statistics of atmospheric turbulence, such as the mean wind speed ([m+1s-1]), turbulent momentum flux ([kg+1m-1s-2]), and the variance of variables.

Naming convention:

`[location of the observational site]_[height of the instrument].csv`

e.g., 'tianjin_80m.csv' records the statistics of the data from the Tianjin observational station at the height of...,

由雷诺应力张量（Reynolds stress tensor）推导得到的各向异性与湍流能量及湍流输送密切相关，可通过xB与yB两个参数进行量化。本研究借助希尔伯特-黄变换（Hilbert-Huang transform）方法，对多组观测实验中各向异性的尺度依赖特性展开研究。不同层结条件下，重心图中的平均yB-xB轨迹以曲线簇的形式可视化呈现。具体而言，随着运动尺度的增大，xB从0.4逐步升高至0.9；而yB则先从0.5上升至0.7，随后降至0。个别案例中偏离该规律的轨迹，可用于区分非湍流运动并重构湍流数据。本分析发现湍流通量存在约30%至80%的高估现象。由此可见，各向异性具备量化大气边界层内湍流输送的潜力。为探究各向异性在反映大气湍流特征方面的有效性，本数据集选取了不同下垫面、不同季节的多观测站点数据。

天津市255米气象塔位于天津市气象局行星边界层（PBL，Planetary Boundary Layer）气象观测站，该站点属于受显著人为影响的典型城市下垫面代表站点。朝向正东方向的80米高度处的超声风速仪（sonic anemometer，CSAT-3，Campbell Scientific公司，美国犹他州洛根市）采集得到湍流数据，可获取三维风速与超声温度资料。安装于80米高度的开路式CO2/H2O分析仪（Open Path CO2/H2O Analyzer，LI-7500，LI-COR公司，美国内布拉斯加州林肯市）开展气体快速响应观测，可获取CO2与水汽的密度时间序列。上述仪器的采样频率均为10赫兹。另有观测[原文此处截断]…… # 基于大气湍流各向异性识别非湍流运动以优化湍流输送估算
[https://doi.org/10.5061/dryad.c866t1gh6](https://doi.org/10.5061/dryad.c866t1gh6)

## 数据与文件结构说明
为探究各向异性在反映大气湍流特征方面的有效性，本数据集选取了不同下垫面、不同季节的多观测站点数据。

### 文件与变量
#### 文件：Statistics.zip
**文件说明：** 该压缩包包含可载入统计软件的CSV与MAT格式文件，内含大气湍流基础统计量，例如平均风速（单位：[m+1s-1]）、湍流动量通量（单位：[kg+1m-1s-2]）以及各变量的方差。
命名规则：`[观测站点位置]_[仪器安装高度].csv`
例如，‘tianjin_80m.csv’记录了天津观测站80米高度处的观测数据统计结果……