DOI: 10.5061/dryad.c866t1gh6
title: Identification of non-turbulent motions for enhanced estimation of turbulent transport using the anisotropy of atmospheric Turbulence

description: 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.
description: 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 CO<sub>2</sub>/H<sub>2</sub>O
 Analyzer (LI-7500, LI-COR, Inc., Lincoln, NE, USA) installed at 80m,
 providing density time series of CO<sub>2</sub> and water
 vapor. The sampling frequencies of the instruments above are all 10 Hz.
 There are also observation of mean wind speed (Cup and vane anemometer,
 Changchun Meteorological Instrument Ltd., Changchun, China) and mean
 temperature and humidity (HMP45C, Campbell Scientific, Inc., Logan, UT,
 USA) at a total of 15 levels. The gradient data are calculated as the
 averaged finite difference of the mean variables from 5 levels from 40m to
 120m. The observational data were collected from July 1 to August 31,
 2017. For more information about the Tianjin Station, refer to Ye et al.
 (2015) and Wei et al. (2018). The Naiman station is
 located in Horqin Sandy Land in the Naiman County of Inner Mongolia. The
 underlying surface is nearly flat and homogeneous, consisting mainly of
 sandy land with low and open shrubs, which is typical of a semiarid
 continental monsoon climate, and there is few human activity. The sonic
 anemometers and Open Path
 CO<sub>2</sub>/H<sub>2</sub>O Analyzer are the
 same as Tianjin Station at 8m orienting towards West, with the same
 sampling frequencies of 10 Hz. The mean wind speed (010C, MetOne
 Instruments Inc., Grants Pass, OR, USA), air temperature and humidity
 (HMP45C, Campbell Scientific, Inc., Logan, UT, USA), and wind direction
 (020C, MetOne Instruments Inc., Grants Pass, OR, USA) are observed at four
 levels from 2 to 20 meters. The gradient data are obtained from the finite
 difference between the two levels of 4 meters and 16 meters. The
 observational data were collected from July 1 to July 31, 2022. More
 information concerning the Naiman station can be found in Li et al. (2015)
 and Wei et al. (2021). The Dezhou station is located at
 Pingyuan county meteorological bureau in Shandong Province. The underlying
 surface is typical flat farmland in North China Plain. The station is
 equipped with a set of instruments at the height of 2.8m, including a
 three-dimensional sonic anemometer (IRGASON, Campbell Scientific, Inc.,
 USA) orienting towards North, a continuous particle measuring instrument
 E-sampler (Met One, Inc.), and a high-frequency sampling extinction
 coefficient measuring instrument (CS120A, Campbell Scientific, Inc., USA).
 The The sampling frequency of wind speed is 10 Hz, and that of
 PM<sub>2.5</sub> density is 1 Hz. The gradient data is
 acquired from a near-surface observation of a GPS low-altitude electronic
 sonde (XHD-403, Institute of Atmospheric Physics of the Chinese Academy of
 Sciences). The sonde collects data every 10 meters, and the averaged
 finite difference within 100 meters from the land surface is calculated as
 the gradient of each meteorological parameter. Observations are repeated
 every 3 hours from December 27, 2018 to January 24, 2019, and the data are
 interpolated to match the sampling frequency of other instruments. There
 is a comprehensive introduction on the Dezhou station and the methodology
 of the measurement of PM<sub>2.5</sub> density in Ren et al.
 (2020). The raw turbulence observational data were
 pre-processed using the EddyPro software (Advanced 7.0.9, LI-COR
 Biosciences, Inc., USA), including error flags, despiking (Vickers and
 Mahrt, 1997), double coordinate rotation (Wilczak et al., 2001), and
 detrending. The turbulent fluxes are also corrected to eliminate the
 influence of tilt, time lag, and WPL terms within the software and the
 following reconstructions. The averaging time span of data processing is 1
 hr for the data from Tianjin and 30 min for the data from Naiman and
 Dezhou. This allows for a comprehensive comparison of the characteristics
 of atmospheric turbulence under different conditions, and the calculations
 in the following sections use the same time spans.
description: # 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 80m. For the csv files: | Column name | Description | Unit | | :------------------------ | :--------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------- | | filename | Name of the raw file (or the first of a set) from which the dataset for the current averaging interval was extracted | \[#] | | date | Date of the end of the averaging period | \[yyyy-mm-dd] | | time | Time of the end of the averaging period | \[HH:MM] | | DOY | Day of year | \[ddd.ddd] | | daytime | Whether the data is daytime | \[1=daytime] | | file\_records | Number of valid records found in the raw file (or set of raw files) | \[#] | | used\_records | Number of valid records used for current the averaging period | \[#] | | Tau | Corrected momentum flux | \[kg+1m-1s-2] | | qc\_Tau | Quality flag for momentum flux | \[#] | | H | Corrected sensible heat flux | \[W+1m-2] | | qc\_H | Quality flag for sensible heat flux | \[#] | | H\_strg | Estimate of storage sensible heat flux | \[W+1m-2] | | sonic\_temperature | Mean temperature of ambient air as measured by the anemometer | \[K] | | air\_temperature | Mean temperature of ambient air, either calculated from high frequency air temperature readings, or estimated from sonic temperature | \[K] | | air\_pressure | Mean pressure of ambient air, either calculated from high frequency air pressure readings, or estimated based on site altitude (barometric pressure) | \[Pa] | | air\_density | Density of ambient air | \[kg+1m-3] | | air\_heat\_capacity | Specific heat at constant pressure of ambient air | \[J+1kg-1K-1] | | air\_molar\_volume | Molar volume of ambient air | \[m+3mol-1] | | ET | Evapotranspiration flux | \[mm+1hour-1] | | water\_vapor\_density | Ambient mass density of water vapor | \[kg+1m-3] | | e | Ambient water vapor partial pressure | \[Pa] | | es | Ambient water vapor partial pressure at saturation | \[Pa] | | specific\_humidity | Ambient specific humidity on a mass basis | \[kg+1kg-1] | | RH | Ambient relative humidity | \[%] | | VPD | Ambient water vapor pressure deficit | \[Pa] | | Tdew | Ambient dew point temperature | \[K] | | u\_unrot | Wind component along the u anemometer axis | \[m+1s-1] | | v\_unrot | Wind component along the v anemometer axis | \[m+1s-1] | | w\_unrot | Wind component along the w anemometer axis | \[m+1s-1] | | u\_rot | Rotated u wind component (mean wind speed) | \[m+1s-1] | | v\_rot | Rotated v wind component (should be zero) | \[m+1s-1] | | w\_rot | Rotated w wind component (should be zero) | \[m+1s-1] | | wind\_speed | Mean wind speed | \[m+1s-1] | | max\_wind\_speed | Maximum instantaneous wind speed | \[m+1s-1] | | wind\_dir | Direction from which the wind blows, with respect to Geographic or Magnetic north | \[deg\_from\_north] | | yaw | First rotation angle | \[deg] | | pitch | Second rotation angle | \[deg] | | roll | Third rotation angle (not applied) | \[deg] | | u\* | Friction velocity | \[m+1s-1] | | TKE | Turbulent kinetic energy | \[m+2s-2] | | L | Monin-Obukhov length | \[m] | | (z-d)/L | Monin-Obukhov stability parameter | \[#] | | bowen\_ratio | Sensible heat flux to latent heat flux ratio | \[#] | | T\* | Scaling temperature | \[K] | | model | Model for footprint estimation | \[0=KJ/1=KM/2=HS] | | x\_peak | Along-wind distance providing the highest (peak) contribution to turbulent fluxes | \[m] | | x\_offset | Along-wind distance providing &lt;1% contribution to turbulent fluxes | \[m] | | x\_10% | Along-wind distance providing 10% (cumulative) contribution to turbulent fluxes | \[m] | | x\_30% | Along-wind distance providing 30% (cumulative) contribution to turbulent fluxes | \[m] | | x\_50% | Along-wind distance providing 50% (cumulative) contribution to turbulent fluxes | \[m] | | x\_70% | Along-wind distance providing 70% (cumulative) contribution to turbulent fluxes | \[m] | | x\_90% | Along-wind distance providing 90% (cumulative) contribution to turbulent fluxes | \[m] | | un\_Tau | Uncorrected momentum flux | \[kg+1m-1s-2] | | Tau\_scf | Spectral correction factor for momentum flux | \[#] | | un\_H | Uncorrected sensible heat flux | \[W+1m-2] | | H\_scf | Spectral correction factor for sensible heat flux | \[#] | | spikes\_hf | Hard flags for individual variables for spike test | 8u/v/w/ts/co2/h2o/ch4/none | | amplitude\_resolution\_hf | Hard flags for individual variables for amplitude resolution | 8u/v/w/ts/co2/h2o/ch4/none | | drop\_out\_hf | Hard flags for individual variables for drop-out test | 8u/v/w/ts/co2/h2o/ch4/none | | absolute\_limits\_hf | Hard flags for individual variables for absolute limits | 8u/v/w/ts/co2/h2o/ch4/none | | skewness\_kurtosis\_hf | Hard flags for individual variables for skewness and kurtosis | 8u/v/w/ts/co2/h2o/ch4/none | | skewness\_kurtosis\_sf | Soft flags for individual variables for skewness and kurtosis test | 8u/v/w/ts/co2/h2o/ch4/none | | discontinuities\_hf | Hard flags for individual variables for discontinuities test | 8u/v/w/ts/co2/h2o/ch4/none | | discontinuities\_sf | Soft flags for individual variables for discontinuities test | 8u/v/w/ts/co2/h2o/ch4/none | | timelag\_hf | Hard flags for gas concentration for time lag test | 8co2/h2o/ch4/none | | timelag\_sf | Soft flags for gas concentration for time lag test | 8co2/h2o/ch4/none | | attack\_angle\_hf | Hard flag for attack angle test | 8aa | | non\_steady\_wind\_hf | Hard flag for non-steady horizontal test | 8U | | u\_spikes | Number of spikes detected and eliminated for variable u | \[#] | | v\_spikes | Number of spikes detected and eliminated for variable v | \[#] | | w\_spikes | Number of spikes detected and eliminated for variable w | \[#] | | ts\_spikes | Number of spikes detected and eliminated for variable ts | \[#] | | u\_var | Variance variables for u | \[m+2s-2] | | v\_var | Variance variables for v | \[m+2s-2] | | w\_var | Variance variables for w | \[m+2s-2] | | ts\_var | Variance variables for ts | \[K+2] | | w/ts\_cov | Covariance between w and variable ts | \[m+1K+1s-1] | For the mat files: 1332 columns of the data of heights [m] and the corresponding PM2.5 density [g/m^3], 290 rows for the data of every 3 hours from 2018121823 to 2019012402. #### File: Time\_Series.zip **Description:** the txt file of observational time series from 2 individual cases that are mentioned in the article manuscript (figure 5). ## Access information The full length data are available from the corresponding author upon reasonable request ([hsdq@pku.edu.cn](mailto:hsdq@pku.edu.cn)). ### Change Log **13 Jun 2025**: Title changed to reflect new manuscript