Partially due to global climate change, sand and dust storms (SDSs)
have occurred more and more frequently, yet a detailed measurement of SDS events
at different heights is still lacking. Here we provide a high-frequency
observation from the Qingtu Lake Observation Array (QLOA), China. The wind and
dust information were measured simultaneously at different wall-normal heights
during the SDS process. The datasets span the period from 17 March to 9 June
2016. The wind speed and direction are recorded by a sonic anemometer with a
sampling frequency of 50 Hz, while particulate matter with a diameter of 10 µm or less (PM10) is sampled simultaneously by a dust monitor with a sampling
frequency of 1 Hz. The wall-normal array had 11 sonic anemometers and monitors
spaced logarithmically from z=0.9 to 30 m, where the spacing is
about 2 m between the sonic anemometer and dust monitor at the same
height. Based on its nonstationary feature, an SDS event can be divided
into three stages, i.e., ascending, stabilizing and descending stages, in
which the dynamic mechanism of the wind and dust fields might be different.
This is preliminarily characterized by the classical Fourier power analysis.
Temporal evolution of the scaling exponent from Fourier power analysis
suggests a value slightly below the classical Kolmogorov value of -5/3 for the
three-dimensional homogeneous and isotropic turbulence. During the stabilizing
stage, the collected PM10 shows a very intermittent pattern, which
can be further linked with the burst events in the turbulent atmospheric
boundary layer. This dataset is valuable for a better understanding of
SDS dynamics and is publicly available in a Zenodo repository at 10.5281/zenodo.5034196.
Introduction
Sand and dust storms (SDSs) not only are common meteorological hazards
accompanying the occurrence of acid rain
and impacts on the marine biological system
but have also caused harm to human
health and led to serious air
pollution . These phenomena can
damage infrastructure, telecommunication and crops; affect transportation
through reduced visibility; and cause tremendous economic losses
. According to the World Meteorological Organization (WMO),
a dust storm is defined as the result of strong surface winds raising a large
quantity of dust into the air, which reduces visibility at eye level (e.g.,
1.8 m) to less than 1000 m, although
severe events may result in zero visibility. The duration of SDS events varies
from a few hours to several days with atmospheric PM10
(particulate matter with a diameter 10 µm or less) dust
concentrations beyond 15 mgm-3 in severe events . Partially due to global
climate change, SDS hazards have aroused more and more concerns in recent
years. Nevertheless, there is evidence to suggest that dust event frequency in
Asia has been declining since the late 1970s . There are also indications, with increasing vegetation cover, that the decrease in the number of
days with gale was the most important reason for the reduced number of sand and dust
storms. However, it is still a big challenge to explore the abundant dynamic
characteristics during SDSs due to a shortage of multi-height and high-frequency
wind and dust information.
An SDS lifts a large number of dust particles into the air and can transport them
hundreds or thousands of kilometers away . In northern
China, as a part of the Asian dust storm source, arid and semiarid areas
including sandy and gravel deserts annually released O(108t) of mineral dust , where 6×106–1.2×107 and 6.7×107t of mineral dust were
transported to the North Pacific Ocean and the China Sea, respectively
, covering a large proportion of
the global mineral dust transport . However, either
the dust field or wind field structures during the SDS transportation are
still unknown, since the turbulent atmospheric boundary layer (TABL) is
involved . The TABL is
inherently nonlinear and has a complex interaction with other types of motion,
e.g., large-scale coherent motions, gravity waves, solitary waves, low-level
jets, and other nonturbulent motion
.
For instance, it has been observed during experiments in turbulent boundary
layer flows that the large-scale coherent structures contains the lengths of
2δ-3δ (O(102) m; δ is the boundary layer
thickness) in the streamwise direction
. They carry a significant
portion of the turbulent kinetic energy and Reynolds stress and also play an
important role in the turbulent transport process. Recently, instances of very
large-scale motion (VLSM), also known as superstructures, whose streamwise
lengths were regarded to be approximately 20δ
(O(103) m), were observed not only in channel flow but
also in the TABL .
Since its discovery, the VLSM has been attracted more and more attention
.
For example, recent results show that occurrences containing considerable energy
structures also existed and increased with height
. Notably, found
that the space charge density and dust concentration were significantly
correlated over 10 min timescales. Therefore, the complex mechanic
system during SDSs is of great interest regarding the investigation of what kind of
structures will exist and how the structures will evolve.
However, free access to a high-frequency observation database on SDSs is still
lacking, which is partially due to the observation difficulty. In this work,
we present high-frequency observation data from the Qingtu Lake Observatory,
Minqin, China, where the wind speed, wind direction, air temperature and
PM10 values are simultaneously recorded at 11 heights during a strong SDS
for 24 h, i.e., from 04:00 on 28 March 2016 to 04:00 on 29 March
2016. A preliminary result on the spectral feature during an SDS is
presented. This dataset is valuable for a better understanding of SDS
dynamics. We hope that the community can be benefit from this dataset, which is publicly available in a Zenodo repository at 10.5281/zenodo.5034196.
The site and experimental setup description are given in
Sect. . Details of meteorological wind velocity, air
temperature, PM10 concentration and quality control can be seen in
Sect. . Then, the basic properties of the process of the
sand and dust storm are deployed in Sect. , including the vertical
and temporal spectral structure of the fluid and dust field. Finally,
Sect. presents a short conclusion of previous works based on
the current site, as well as a discussion on the nonstationarity of SDSs and
the log law or power law of wind speed against height.
Site description and measurements
A field experiment was carried out on Qingtu Lake in Minqin, China (39∘12′ N, 103∘40′ E) to measure the three-dimensional
velocity and temperature in streamwise, spanwise and wall-normal directions in
the atmospheric boundary layer. The Qingtu Lake Observation Array site is
between the two of the largest deserts in China: the Badain Jaran Desert and
the Tengger Desert, which lies within a dusty belt in the Hexi Corridor
. One reason to choose this location is to
avoid the influence of human activities; e.g., the nearest city is around
74km from this observation site; see the geolocation indicated
in Fig. b. Dust weather in the Hexi Corridor is often caused by
intense frontal systems. A combination of large-scale weather patterns and
topological effects led to the development of the intense frontal system
. Dust events occur most frequently in spring
(e.g., two-thirds of the dust events occur in March, April and May). To access
the vertical structure of the dust, dust monitor probes anemometers were
installed at 11 heights in Fig. d in a logarithmic manner for
z=0.9,1.71,2.5,3.49,5,7.15,8.5,10.24,14.65,20.96,30m, respectively. At each height, a sonic anemometer
(Campbell CSAT3B, sampling frequency of 50 Hz) and a dust monitor
(TSI DustTrak 8530, sampling frequency of 1 Hz) were installed on
both sides of the main tower (as shown in Fig. c). All the
sonic anemometers and dust monitors were installed on the towers to
synchronously measure the wind velocity, the air temperature and the
PM10 concentration, over the period from 17 March to 9 June 2016.
After the passage of a cold front, the outbreak of strong wind is often
accompanied by dust emissions in the spring in northern China. On 28 March
2016, a dust storm that has passed the observation site and been recorded
successfully by the aforementioned instruments; e.g., the wind and temperature
data were collected from the 11 sonic anemometers, and
PM10 data were obtained from 11 synchronous dust monitors for a
continuous 24 h period (from 04:00 on 28 March 2016 to 04:00 on 29 March
2016, UTC + 08:00) at different heights below 30 m. The sample
size is 4320000 data points for the wind velocity and temperature and
43200 for the dust.
The measured parameters during the dust storm from 04:00 on 28 March 2016 to
04:00 on 29 March 2016: (a, c) horizontal wind speed
(ms-1) and vertical wind speed (ms-1), (b) wind
direction (α=0 corresponding to the northwestern direction;
positive when the wind direction varied in the clockwise direction), and
(d, e) temperature (∘C) and
PM10 concentration (mgm-3). Note that data are from the height z=3.49m.
As shown in Fig. b, the measured wind direction had instances where it was noisy before becoming relatively stable but again became noisy. Figure a, c, d and e represent the horizontal (U), wall-normal (W) velocity, the air temperature (T) and PM10
concentration, respectively. The maximum wind speed is 13 ms-1 and is
recorded at z=5m (larger at a higher height), and the highest level of
PM10 concentration reached 3 mgm-3 (larger at a lower
height). Strong fluctuations for U and W are shown in the mature stage of
this SDS, which is accompanying violent PM10 transportation. The
dataset is publicly accessed in a Zenodo repository
(10.5281/zenodo.5034196) in a Hierarchical Data Format (HDF) file, which
is readable by popular software such as MATLAB or Python .
Data and methodologyMeteorological wind velocity
During installing, the coordinates of the sonic anemometer (Campbell CSAT3B),
i.e., x, y and z, were in accordance with the observation array, i.e., a
45∘ angle in the northerly direction; see
Fig. . To retrieve the meteorological wind component from the
measured wind velocity components ux, uy and uz, the following
equations can be applied,
u=(ux+uy)2/2,v=(uy-ux)2/2,W=uz,
where u, v and W are the easterly, northerly and vertical components.
The wind direction is then calculated as
α=180+180πatan2(v,u).
All parameters obtained are at a sampling frequency of 50 Hz. The
measurement resolution is 0.001ms-1 for ux and uy and
0.0005ms-1 for uz, respectively. The wind direction can
be defined from 0 to 360∘ with an accuracy of ±0.7∘
at a typical horizontal wind speed of 1ms-1.
Figure a–c show the observed meteorological wind speed
U, wind direction α and vertical wind velocity W at a height of
3.49 m.
Illustration of the observation coordinate: ux and uy are the wind
vector measured by the anemometer, which has a 45∘ angle against
the northerly direction. The corresponding meteorological wind vector and
direction can be retrieved via Eqs. () and ().
Air temperature
In addition, the sonic anemometer also provides the air temperature with the same
sampling frequency as the wind velocity and a measurement accurate to
0.025∘C. Figure d shows the collected
air temperature with visible outliers, which can be further excluded in
analysis using different quality control strategies.
The dust monitor (TSI DustTrak 8530) collected PM10 with a
sampling frequency of 1 Hz. The PM10 concentration is on the
range of 0.001 to 400mgm-3 with a typical accuracy of ±0.1% or ±0.001mgm-3. As aforementioned, the
measured PM10 concentration is on the range of 0 to
5mgm-3 with ascending and descending stages that each
last for a few hours; see Fig. e. Due to a technical
reason, the dust sensor at a height of 5 m failed to record the data.
Quality control
In most instances it is necessary to take many natural factors and events into
account, for instance, fierce buoyancy (heat flux) variance and sand and dust
movement, to list a few. Quality control methods are necessary to guarantee a
good data quality. Generally, the raw data collected from the
instrument were necessary to be carried out using pre-processing methods;
otherwise, they could be misunderstood as “trustworthy”. Typically, there
are generally three types (i.e., missing and unrecognizable characters,
extreme values, and values greater than 4 standard deviations) that needed to be
pre-processed from the raw data. For the current measurement, there are very
few “bad” data; see the temperature curve in Fig. d.
Therefore, we provide only the raw data with the quality control code.
In general, the following steps can be applied directly to the raw data to
remove the outliers. (i) For the “missing” and “unrecognizable characters”:
the situations for “data missing” are rare, but the “unrecognizable
characters” in the raw data emerge sometimes during a low-battery condition,
while the collector (Campbell CR3000) is under a full load or while the GPS is updating. If the irregular
point occurs alone, then it was replaced by the mean value on both
sides. Otherwise, for some continuous series (less than 1 s, 50 points
under the sampling frequency with 50 Hz), they were replaced by the
mean values based on the regular points over 1 h. Last, the irregular
points greater than 1 s should be considered carefully before using this
data series. (ii) Extreme values (for instance, velocity component
greater than 100 ms-1 and temperature with
100 ∘C) generally occur as a
single point, and it is rare during observation. It is replaced by the
mean value on both sides. (iii) For the values greater than 4 standard
deviations, the quality control for the database adopts “3σ limits” to produce items of the highest quality, where the data
are within 3 standard deviations from the mean to set the upper and lower
control limits in statistical quality control charts. Other methodologies are
also possible to detect outliers; for instance, instead of 4 standard deviations, one can apply the Hampel identifier to detect the outliers
. In this study, to reduce the bias from the
interpolation of the data, we replace the outliers by a not-a-number (NaN) variable.
Basic properties
In this section, we present the basic properties of the observed SDS. Without
further clarification, the collected data at a height of 3.49 m are
presented.
Nonstationarity
As mentioned above, the SDS event is a typical nonstationary
event. Figure a shows the hourly averaged curves for
both wind speed (U) and PM10, and
Fig. b shows the corresponding
root-mean-square values at a height of 3.49 m. Based on these
curves, the observed SDS event can be roughly divided into three stages. (i)
During the ascending stage between 09:00 (denoted by A) and 10:00 on 28 March
2016 (denoted by B), the wind speed increases quickly
while PM10 starts to rise. (ii) During the stabilizing stage between
10:00 and 17:00 (denoted by C), one sees the
strongest wind, which lasts for approximately 7 h. The corresponding
PM10 accumulates quickly and reaches its peak value (e.g.,
PM10≥0.35mgm-3) and maintains it for roughly
3.5 h. (iii) Finally comes the descending stage between 17:00 and 19:00
(denoted by D), where both the wind speed and PM10 are descending
sharply. The root-mean-square curves display a similar pattern. Note that a
nonlinear trend is evident; see Fig. a. When
calculating the root-mean-square value, a constant value is subtracted from
the raw dataset within a 1 h sliding window, and the trend can not be fully
excluded .
(a) Hourly averaged wind speed U‾ and PM‾10 at a height of 3.49 m. (b) The corresponding root-mean-square value. This SDS event is roughly divided into three stages, which are illustrated by A–D; see the main text for more detail.
(a) Phase diagram of the hourly averaged wind speed versus the root-mean-square speed at a height of 5 m. (b) The hourly averaged concentration PM10 versus its root-mean-square value. The circle is the bin average value with 10 bins for each decade in the logarithmic scale.
Another possible explanation of the above-observed pattern is the amplitude
modulation due to the existence of the aforementioned VLSM that is observed in
the TABL with typical spatial scales from O(102m) to
O(103m) or typical temporal
scales from O(1min) to O(10min). The small structures, thus the root-mean-square value,
are then modulated by the VLSM . This feature is
further illustrated as a diagram (hourly averaged value versus the
root-mean-square ones) in Fig. . The power-law trend is
observed with the scaling exponent of 0.63 and 0.86 for the wind speed and
PM10, respectively.
Vertical structure
The QLOA is in the logarithmic layer of the TABL, in which the wind speed
should vary logarithmically with height that often goes up to
O(102m) and even to O(103m). Figure a shows the hourly averaged
speed U‾ versus h in a semi-log view to emphasize the log law at
four time periods from A to D. It suggests that no matter the intensity of
the wind speed, the log law of the wall is preserved, which is deeply
associated with the TABL. However, the corresponding Urms shows a
complex h dependence. It first increases logarithmically with h and
reaches its peak, e.g., h=8 m at A, then decreases roughly logarithmically
with h. There is no clear h dependence when h≤20 m for the
collected PM‾10 in Fig. c, while
the corresponding PM10,rms clearly logarithmically
decreases against h when h<10 m. This is partially due to the
interaction between the suspended particles with the turbulent structures,
which for sure deserves further investigation.
(a) Hourly averaged wind speed U‾ versus height h at the same four periods as in Fig. . (b) The corresponding root-mean-square speed. (c) Hourly averaged concentration PM‾10 and (d) the corresponding root-mean-square one.
Temporal spectral structure
In this work, the Fourier power spectra E(f) are estimated via the
Wiener–Khinchin theorem without involving the classical Taylor frozen
hypothesis to convert the time measurement to a spatial one
. More precisely, the Fourier transform of the
autocorrelation ρ(τ) functions as follows:
E(f)=R∫-∞+∞ρ(τ)exp(-j2πfτ)dτ,
in which R means the real part; j=-1 is the complex unit; and
ρ(τ)=1M(τ)∑i=1M(τ)θ̃(ti+τ)θ̃(ti),
where
θ̃(xi)=θ(xi)-θ(xi) is
the centered θ(xi), θ is the value of either wind speed U or
PM10, M(τ) is the sample size at the time separation scale
τ, and ⋅ means average time. With this
approach, the estimated Fourier spectrum can recover the correct power-law
behavior for the time series with the missing data or irregular time steps
without the bias from the interpolation of the data .
Figure a shows the 1 h segment of the raw wind speed U
around the typical time periods from A to D at a height of 3.49 m.
Fourier power spectra E(f) are then estimated using the abovementioned
Fourier spectrum estimator; see Fig. b. Due to the existence
of atmospheric turbulence, the power-law behavior, i.e., E(f)∝f-β, is observed on the frequency range of 0.1≤f≤10 Hz with a
scaling exponent around 1.53, which is slightly below the classical
Kolmogorov value of 5/3 for three-dimensional homogeneous and isotropic
turbulence . This is partially due to the influence of
the ground that the scaling exponent βU demonstrates as height
dependence. It seems that, except for the intensity of the wind speed, this
small-scale scaling feature remains unchanged during the SDS; see
Fig. .
(a) Segment of 1 h of the raw wind speed around time A, B, C and D at a height of 3.49 m. (b) The corresponding Fourier power spectrum. (c, d) The PM10 and their Fourier spectra at the same height and time period. The dashed line is the power-law fit for reference.
Temporal evolution of the scaling exponent β, which is estimated on a 1 h sliding window with 80 % overlap. The horizontal dashed line indicates the Kolmogorov value of 5/3 for reference. The vertical dash line in Fig. indicates three different stages.
Concerning the concentration of PM10, the scaling feature is more
complex; see Fig. d and also Fig.
for the temporal evolution of the scaling exponents βPM10.
Note that the density ratio of the sand particle and air is around
2650/1.25≃2100. Therefore, the weak wind can not blow
the sand particles into the air. The scaling exponents are then approaching
zero before the ascending and after the descending periods. During the stabilizing
stage, the collected PM10 shows a very intermittent pattern, which
can be further linked with the burst events in the TABL; see
Fig. c. It is interesting to see that the power-law behavior is
still preserved and is even closer to the Kolmogorov value; see
Fig. for the measured scaling exponent β
versus time.
Discussions
The QLOA site is a one-of-a-kind field observation station that can monitor
three-dimensional TABL flows (streamwise, spanwise and wall-normal
directions) for both wind-blown sand movement and clean wind, as well as the
electric field near the ground surface. The facility's unique measuring array
allows us to investigate the three-dimensional shape of coherent structures,
as well as the temperature fluctuations that accompany them, which are also
covered in this paper. Precious works where the data were collected at the
current site can be categorized by three series. (i) The canonical atmospheric
turbulent boundary layer is the near-neutral atmospheric surface layer that can be
viewed as a truly high-Reynolds-number facility
(Reτ=δuτ/ν=O(106), where δ is the
boundary layer thickness, uτ is the skin-friction velocity and ν
is the kinematic viscosity). Characteristics of coherent structures were
investigated under near-neutral and stratified conditions
(e.g., ). Roughness
effects were explored to illustrate the features in the near-surface layer
(e.g., ). (ii) Sand-laden flows are
wind-blown sand movements that are topically two-phase flows in arid and
semiarid regions, where the interaction between flow features and
PM10 concentration were previously discussed
(e.g., ). (iii) The electric field during an SDS shows
granular materials that are regularly brought into contact or collision with one
other during dust events such as blowing sand, dust devils and dust storms,
generating huge quantities of electrical charge on their
surfaces. Measurements of electrical effects in dust events (especially dust
storms) have been made specifically and have been further analyzed
e.g.,. Notably,
explored the comparison of large-scale structures of
turbulent flows in the atmospheric surface layer with and without
sand. According to the research, the streamwise turbulent kinetic energy is
enhanced at all scales in the sand-laden flows, and the inclination angles of
large-scale structures are shown to increase with sand concentration, owing
to the decreased velocity gradient. However, the streamwise length scale of
large-scale structures and the size of the most energetic turbulent
structures are found to be unaltered compared to clean-air flows. The abundant
mechanisms during SDSs are still unknown, especially the difference between
the variables during SDS days and those during normal days, which is necessary
to be explored in detail in the future.
(a) Examination of the power-law relation, i.e., U‾(h)∝hξ, at four typical periods from A to D. The dashed line is a power-law fitting for reference. (b) Temporal evolution of the power-law scaling exponent ξ.
Dust aerosols, being one of the most important types of atmospheric-aerosol
constituents, have gotten a lot of attention because of their effects on the
environment, weather and climate via radiation and precipitation
. The
high-resolution data collected in the current work can benefit researchers who are
improving the current sand and dust storm model, as well as the interaction
between coherent structures in the flow field and dust field.
Nonstationarity of the SDS
In general, the TABL shows both daily and seasonal variations due to the
rotation and evolution of the earth. Therefore, not only the height of the
TABL but also its scaling feature, e.g., scaling of the wind speed and air
temperature, show daily and seasonal variations. Within a day, the
wind speed, air temperature, etc. often show an intraday trend; see
Fig. . As aforementioned, it is thus a typical
nonstationary event. There exist more rigorous approaches to characterize or
analyze the nonstationary time series. For example, a stationary parameter can
be defined as quantitatively characterizing nonstationarity
. One can also apply either
wavelet-based techniques (e.g., wavelet leader and synchrosqueezed wavelet
transforms;
) or the Hilbert–Huang
transform to extract
the local time-frequency information.
It is worth pointing out that the detrending methods should be applied with
caution, since the trend is difficult to define and is difficult to
separate too . For example, when calculating the
momentum transport (also known as the Reynolds stress) in a wavy aquatic
environment, different approaches may provide notable different values
. Therefore, the data-driven approaches, such as wavelet-based techniques or the
Hilbert–Huang transform, that can adaptively separate the variations into
different scales are recommended to deal with nonstationary SDS data.
Log law or power law of wind speed
As mentioned above, wind speed is often shown to be logarithmically
increasing against height h up to a few hundred meters above the ground; this has been verified at the current site . The main assumption of the log law is that
the turbulence intensity is only controlled by the amplitude of shear flow and
the bottom roughness, relying on the boundary condition. If we consider the
situation in the atmosphere, it could be much more complex. Indeed, the convection
induced by solar heating brings another source of turbulence that may
affect the velocity profile close to the surface, including the
influence of sand and dust storms. Meanwhile, the power law is used when surface
softness or stability information are not available, even though it has an
empirical relation and without a physical basis. There is a simple relationship
between the wind speed at one height and that at another; 10 m wind
speed is used as the reference level, which is the standard height prescribed by
the WMO.
The power-law relation, i.e., U‾(h)∝hξ, has been
proposed . The scaling
exponent ξ is expected to be constant for an infinite Reynolds number (i.e.,
Re=UL/ν, where U is the wind speed; L is the typical length scale,
e.g., the height of the sensor location; and ν is the kinetic viscosity of
air). It is interesting to note that the power-law relation is evident for
the current dataset, since the Reynolds number of the current data is up to
O(107) (Fig. ). It shows roughly a constant
value ξ=0.12±0.01 in the stabilizing stage, since the Reynolds
number and wind speed in this period are nearly constant. Note that with such
features, a comparison of logarithmic wind profiles and power-law wind profiles
is possible.
Code and data availability
The dataset was uploaded to a Zenodo repository at 10.5281/zenodo.5034196.
The datasets are published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Additional observation data may be requested from the corresponding author.
Conclusions
High-frequency observatory data for studying the features of the fluid and
dust field during sand and dust storms were presented. The complex physical
process during the evolution of sand and dust storms is still unknown due to the
lack of massive atmospheric observations, especially for high-resolution
and synchronous measurements near the surface. For a better understanding of
the interplay between turbulent flows and non-turbulent motions during an
SDS, a three-stage model was put forward to facilitate the easy
understanding of the different fluid and dust features based on the mean
velocity changes (also partially taking into consideration the wind direction and
PM10 concentration). For the duration of an SDS, structures gradually from ascending to stabilizing to descending effects in turbulent
energy and structures were obtained through Fourier spectra, corresponding to
the three-stage model. The features revealed in this work grant us a deeper and
wider understanding of the SDS fluid and dust field. It is anticipated that data collected in this work will be of utility not only specifically for the boundary
layer community in building a model for sand and dust storms but also
broadly for communities studying the exchange of the dust and fluid field and
energy transfer for the particle-laden two-phase flow.
Author contributions
XZ, GW and XL designed the experiment. XL performed the
field observations and data analyses and managed the manuscript with guidance from and editing by YH, GW and XZ. All authors discussed the results and commented on the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
The data are provided as is and with no warranties.Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank Haihua Gu, Tianli Bo, Ao Mei,
Guowen Han, Yirui Liang, Zhang Huan and Hongyou Liu for their hard work in the field observations. We acknowledge support from the National Natural Science Foundation of China (grant nos. 92052202, 11490553
and 11732010), and Xuebo Li is also supported by a CSC scholarship (file no. 201706180037). Yongxiang Huang is also partially supported by the State Key Laboratory of Ocean Engineering (Shanghai Jiao Tong
University) (grant no. 1910).
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 92052202, 11490553, and 11732010).
Review statement
This paper was edited by Qingxiang Li and reviewed by two anonymous referees.
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