AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-1215-2017Assessment of virtual towers performed with scanning wind lidars and Ka-band radars during the XPIA experimentDebnathMithuIungoGiacomo Valeriovalerio.iungo@utdallas.eduhttps://orcid.org/0000-0002-0990-8133BrewerW. AlanChoukulkarAdityahttps://orcid.org/0000-0003-1007-0267DelgadoRubenhttps://orcid.org/0000-0002-7133-2462GunterScottLundquistJulie K.https://orcid.org/0000-0001-5490-2702SchroederJohn L.WilczakJames M.WolfeDanielWind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, Richardson, TX, USANational Oceanic and Atmospheric Administration, Earth Sciences Research Laboratory, Boulder, CO, USAAtmospheric Physics Department, University of Maryland Baltimore County, Baltimore, MD, USADepartment of Earth and Space Sciences, Columbus State University, Columbus, GA, USANational Renewable Energy Laboratory, Golden, CO, USADepartment of Atmospheric and Oceanic Sciences, University of Colorado at Boulder, Boulder, CO, USADepartment of Geosciences, Texas Tech University, Lubbock, TX, USAPhysical Sciences Division, National Oceanic and Atmospheric Administration, Boulder, CO, USAGiacomo Valerio Iungo (valerio.iungo@utdallas.edu)29March2017103121512271October201614October20161March20177March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/1215/2017/amt-10-1215-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/1215/2017/amt-10-1215-2017.pdf
During the
eXperimental Planetary boundary layer Instrumentation Assessment (XPIA)
campaign, which was carried out at the Boulder Atmospheric Observatory (BAO)
in spring 2015, multiple-Doppler scanning strategies were carried out with
scanning wind lidars and Ka-band radars. Specifically, step–stare
measurements were collected simultaneously with three scanning Doppler
lidars, while two scanning Ka-band radars carried out simultaneous range
height indicator (RHI) scans. The XPIA experiment provided the unique
opportunity to compare directly virtual-tower measurements performed
simultaneously with Ka-band radars and Doppler wind lidars. Furthermore,
multiple-Doppler measurements were assessed against sonic anemometer data
acquired from the meteorological tower (met-tower) present at the BAO site and a lidar wind
profiler. This survey shows that – despite the different technologies,
measurement volumes and sampling periods used for the lidar and radar
measurements – a very good accuracy is achieved for both remote-sensing
techniques for probing horizontal wind speed and wind direction with the
virtual-tower scanning technique.
Introduction
The increasing need of monitoring the atmospheric boundary layer for a broad
range of technological and scientific pursuits – such as for meteorology
, renewable energy
and air
traffic management – has led to a rapid
development of remote-sensing measurement techniques, such as wind lidars
and radars
. Compared to classical
meteorological towers, remote-sensing instruments allow easier deployment,
enhanced capability of varying deployment locations and potentially lower
costs.
A Doppler-based remote-sensing instrument allows measurements of the wind
velocity component parallel to the direction of the emitted wave source,
e.g., a laser beam for a lidar or radio waves for a radar. The measured wind
velocity, which is referred to as radial or line-of-sight velocity, is
proportional to the Doppler shift on the backscattered signal generated by
the aerosol suspended in the atmosphere . Measurements
of multiple velocity components with a single lidar or radar have been
typically performed by sequentially sensing different locations of a
measurement volume, and assuming flow homogeneity within the measurement
volume. This constraint entails limitations on the size of the measurement
volume and applicability of these scanning strategies in the presence of
significant flow heterogeneity, such as for measurements over complex terrain
and wind turbine wakes .
To overcome limitations connected with multiple-component velocity
measurements performed with a single instrument, multiple-Doppler scanning
strategies have been explored, which require the simultaneous availability of
multiple instruments . Multiple-Doppler scans consist of probing the
wind velocity field at a specific location with various non-parallel
line-of-sight velocities in order to characterize the 3-D nature of the
atmospheric boundary layer wind field, such as in the presence of wind shear,
veer or wakes produced by upwind obstacles (e.g., wind turbines, buildings,
topography), or stratified wind turbulence . The number
of independent non-parallel line-of-sight velocities should be equal to or
larger than the number of required velocity components
. For a specific site, at each
measurement point it is possible to optimize azimuthal and elevation angles
of the various line-of-sight directions in order to minimize the error in the
retrieval of the three Cartesian velocity components . It
is noteworthy that the accuracy of the retrieved velocity components is a
function not only of the experimental setup but also of the accuracy of the
individual instruments. Accuracy in the retrieval of the three wind velocity
components is a function of the norm of a matrix including trigonometric
functions of elevation and azimuthal angles of the measured line-of-sight
velocities .
The virtual-tower measurements presented in this paper are part of the
eXperimental Planetary boundary layer Instrument Assessment (XPIA) field
study, which was funded by the U.S. Department of Energy within the
Atmosphere to Electrons (A2e) program to estimate accuracy and capabilities
of various remote-sensing techniques for the characterization of complex
atmospheric flows in and near wind farms. The XPIA experiment was carried out
at the National Oceanic and Atmospheric Administration (NOAA), Boulder
Atmospheric Observatory (BAO), near Erie, Colorado, for the period 2 March–31 May 2015. An overview of the field campaign is provided in
, while a detailed analysis of several multiple-Doppler
scanning strategies performed with scanning lidars was provided in
, and vertical profiles of the three wind velocity
components performed with triple RHI scans were presented in
.
The XPIA experiment provided the unique opportunity of having available two
Ka-band radars and three scanning wind lidars with the capability of
performing multiple-Doppler measurements. To the authors' knowledge, this is
the first time that virtual towers performed with Ka-band radars and scanning
lidars are analyzed through a direct intercomparison. Furthermore,
validation of the multiple-Doppler measurements was performed against wind
velocity data acquired from sonic anemometers, which were installed
throughout the height of the meteorological tower (met-tower) present at the BAO site, and a lidar
profiler as well.
The remainder of the paper is organized as follows: a description of the
instruments deployed for this experiment is provided in Sect. . The data retrieval and assessment of the horizontal wind
speed and wind direction from dual-Doppler RHI scans performed with two
Ka-band radars are described in Sect. , while a similar
survey is then performed for the triple-Doppler step–stare scans carried out
with three scanning lidars (Sect. ). Subsequently, an
intercomparison between lidar and radar virtual-tower measurements is
described in Sect. . Finally, concluding remarks are
reported in Sect. .
Experimental setup and measurement procedures
The instrumentation deployed for the XPIA experiment comprised sonic
anemometers installed over the BAO met-tower, profiling lidars, radiosonde
launches, microwave radiometers and two scanning Ka-band radars. Moreover,
five scanning Doppler wind lidars were deployed to explore novel scanning
strategies for the characterization of atmospheric boundary layer flows. The
multiple-Doppler measurements performed with three scanning wind lidars and
two scanning Ka-band radars, which is the focus of this paper, represent one
task of a broader test matrix. More details about the XPIA campaign can be
found in . Virtual-tower measurements over the lidar
supersite location (Fig. ) were performed from 19:00 UTC on
24 March 2015 until 23:00 UTC on 31 March 2015. Wind data from one lidar were not
available for the period 27–28 March 2015 due to a connectivity technical
issue. The data set presented in this paper is the result of a quality control
process, which is a function of aerosol condition and carrier-to-noise ratio
of the measured line-of-site velocities. The presented wind data are
particularly valuable for assessment purposes due to the broad variability
that occurred both in wind speed, from 0 up to 20 m s-1, and wind
direction, which varied through the full circle of the wind rose
Map of the setup for the virtual-tower measurements performed over
the lidar supersite location during the XPIA experiment.
The BAO met-tower was built in 1977 to investigate the planetary boundary
layer . This 300 m tall tower has three legs spaced 3 m
apart, and it is instrumented with temperature and relative humidity sensors
at 10, 100 and 300 m above ground level (a.g.l.), while 12 CSAT3 3-D sonic
anemometers by Campbell Scientific were installed at 50, 100, 150, 200, 250 and 300 m a.g.l. Six anemometers were installed on booms
pointing NW (334∘), which are denoted as NW sonic anemometers, while
six other anemometers were installed on SE booms (154∘), denoted as SE
sonic anemometers. Most of the booms were 4.3 m long, while at the 250 m
level the SE boom was 3.3 m long. The sonic anemometers collected data with a
sampling frequency of 20 Hz, which were then tilt-corrected following the
method proposed in . The sonic anemometers were calibrated
for the XPIA experiment by Campbell Scientific, with measurement resolution
(maximum offset error) of 0.1 cm s-1 (8 cm s-1) for the horizontal
wind speed and 0.05 cm s-1 (4 cm s-1) for the vertical velocity. It
is noteworthy that the sonic anemometers can experience wake effects produced
from the met-tower for specific wind directions, i.e., between 111
and 197∘ for the NW anemometers, and between 299 and
20∘ for the SE anemometers (see for a more
detailed discussion).
GPS locations of the three scanning Doppler wind lidars, wind lidar
profiler, Ka-band radars and BAO tower.
Vertical profiles of the three velocity components were performed with the
WLS-16 Leosphere Windcube Offshore 8.66 profiling lidar, which is denoted as
V2 lidar and has an absolute mean deviation smaller than 0.1 m s-1 in
wind speed and smaller than 2∘ in wind direction. Wind velocity
measurements were carried out with the Doppler beam-swinging (DBS) technique
with an elevation angle from vertical of
28∘, with a sampling frequency of 1 Hz and with the range gates centered at
11 vertical heights (40, 50, 60, 80, 100, 120, 140, 150, 160, 180, 200 m). The profiler lidar was deployed at the location referred to
as the lidar supersite (Fig. ), whose GPS coordinates are
reported in Table .
Two Texas Tech University Ka-band (8.6 mm wavelength) mobile Doppler radars
were deployed during XPIA. These
Ka-band radars were designed to operate in a variety of weather conditions,
including precipitation and clear air. As for most radars, data quality and
maximum range are typically greatest during periods of precipitation. In such
environments, the maximum range of data can often exceed 20 km (depending on
the employed scanning parameters for a given experiment). Data quality and
maximum range tend to be reduced in clear-air conditions, but the magnitude
of the reduction is highly dependent upon the concentration of clear-air
scatters (e.g., dust, insects). Typical ranges in clear air can vary between
3 and 10 km. Late spring, summer and early fall typically provide the best
clear-air environments, with biological scatterers being limited during the
remaining portions of the year. The accuracy of the dual-Doppler virtual
towers from radar data has been shown to be fairly consistent across
different atmospheric conditions above approximately 50 m a.g.l.
. Below this level, dual-Doppler wind speeds tended to be
slightly overestimated in heavy precipitation . During the
XPIA experiment, the Ka-band radars were on site for 30 days. Atmospheric
conditions allowed for quality dual-Doppler data collection on 17 days.
Data set lengths were largely dependent upon data quality and project
objectives. During this time, the following radar scanning parameters were
employed: pulse repetition frequency of 15 kHz, pulse width of 20 µm s
and range resolution of 15 m. Radar 1 (radar 2) was deployed 3.192 km (3.9 km)
northwest (north) of the BAO tower (Fig. ). Considering the
0.33∘ half-power beam width of the radars, these distances yielded an
azimuthal resolution of 18 m (22.5 m) for radar 1 (radar 2) at the BAO tower
location. Simultaneous RHI scans were performed by focusing both radars over
the lidar supersite location by setting the radar azimuthal angles reported
in Table , sampling rate equal to 5 Hz and sampling period
of 3.3 s. For the 25 March 2015 data set, virtual-tower data were collected at
the onset of precipitation and persisted for 113 min before switching
scanning strategies to accomplish additional objectives. After quality
control, analysis of the radar measurements, wind data from the two Ka-band
radars for the period 13:20 to 15:07 UTC on 25 March and for heights ranging
from 10 to 490 m height with 20 m interval were available for this
particular study.
Three Leosphere Windcube 200S scanning Doppler wind lidars (University of
Texas at Dallas (UTD), NOAA Dalek 1, NOAA Dalek 2) were deployed for this
experiment. Wind measurements were performed by means of eye-safe laser with
a pulse energy of 0.1 mJ, a wavelength of 1.54 µm and a pulse length of 200 ns. Measurements were acquired by using an accumulation time of 0.5 s and
gate length of 50 m. Locations of the three scanning Doppler wind lidars are
shown in Fig. , while their GPS positions, azimuthal angles
and distance with respect to the virtual-tower location are reported in Table . Accuracy in the radial velocity of each scanning lidar is
always better than 0.3 m s-1 for carrier-to-noise ratio higher than -25 dB for the line-of-sight velocity . Squareness,
precision and repeatability tests indicate an absolute pointing accuracy of
about 0.15∘. All the scanning lidars performed fixed-point
measurements at different heights over the lidar supersite location (Fig. ) during the time period 00:00–24:00 UTC on 25 March 2015.
Lidar measurements were performed at six different heights from 100 to 200 m with 20 m steps.
For the measurements performed on 25 March 2015, the maximum and minimum
range for scanning Doppler lidars varied from 300 up to 3000 m, while the
carrier-to-noise ratio was between -50 and 3 dB. The
collected lidar data were further post-processed only when the carrier-to-noise
ratio of the lidar signal was larger than -25 dB .
The post-processing from the three radial velocities (Ur) to Cartesian
wind velocity components (U, V, W) was carried out following the
standard triple-Doppler retrieval
by means of the following equations:
UVW=cos(ϕUTD)*cos(θUTD)cos(ϕUTD)*sin(θUTD)sin(ϕUTD)cos(ϕD1)*cos(θD1)cos(ϕD1)*sin(θD1)sin(ϕD1)cos(ϕD2)*cos(θD2)cos(ϕD2)*sin(θD2)sin(ϕD2)-1×UrUTDUrD1UrD2,
where ϕ and θ represent elevation and azimuthal angles,
respectively, of the various lidars indicated as a suffix. The accuracy in the
retrieval of the three velocity components was estimated for the different
heights through the L2 norm of the rows of the matrix in Eq. (), including trigonometric functions of ϕ and θ of
the various lidars. Error in the velocity retrieval increases as the
L2 norm of the rows of the matrix in Eq. () diverges from 1;
however, the values obtained with this criterion do not represent any error
quantification and can only be used for a comparative analysis and selection
of optimal lidar configurations . In Table , it is shown that for this setup the accuracy in
the retrieval of the horizontal wind speed components is roughly unchanged
for the different heights, while accuracy is improved with increasing heights
for the vertical velocity, which is consequence of the higher elevation
angles of the three lidars.
Error analysis on the retrieval of the wind velocity components from
triple-Doppler lidar measurements for different heights consequent to
azimuthal and elevation angles of the three lidars. Values are
dimensionless.
Histogram of the overlapping time of the step–stare measurements
among the three lidars.
For each height of the virtual tower and each lidar, the closest range gate
to the considered measurement point is selected for the data retrieval. The
maximum horizontal distance of a gate centroid from the respective tower
measurement point is 19 m, while the vertical one is always smaller than 10 m. The sampling period at each measurement point was 25 s, while the total
time required to perform one virtual tower was on average 151.6 s. The lidars
used for the XPIA field campaign are commercial lidars operated with a
graphical user interface (GUI) provided by the lidar manufacturer. This GUI
did not allow the control and synchronization of the lidars through a master
computer; thus, at each measurement location the overlapping time was
generally smaller than the prefixed period of 25 s. A histogram of the
overlapping time is reported in Fig. , which shows a mean
value of 16.3 s and a standard deviation of 3.6 s.
Bias errors in laser pointing and in the line-of-sight velocity, which were
evaluated through preliminary tests and reported in
Table , were considered for the data retrieval. An estimate
of the azimuthal bias from the north for each lidar was retrieved through
hard-target tests performed by hitting reference towers present on site with
the lidar laser beam, and using their GPS coordinates with respect to the
lidar location. A bias in the radial velocity of the UTD lidar was due to
improper calibration of the frequency chirp in the laser pulse, which was
stable and reproducible in several tests, and could simply be subtracted out
of the lidar measurements.
Bias errors used for the triple-Doppler data retrieval.
Wind velocity data acquired from sonic anemometers and lidar
profiler at 150 m height: (a) horizontal wind speed Uh
(m s-1); (b) wind direction (∘). The date of the
observation is 25 March 2015. Two vertical dashed lines represent the
availability period of radar data.
We note that the sonic anemometers can experience wake effects from the tower
for specific wind directions, i.e., 111∘≤θ≤ 197∘
for the NW anemometers and 299∘≤θ≤ 20∘ for the SE
anemometers . For this experiment, wind
direction varied between 360 and 0∘, which indicates that the
SE and NW anemometers might be affected by wake effects for certain period of
time.
Assessment of radar virtual-tower measurements
Assessment of the lidar and radar virtual towers is performed against the
wind velocity components acquired through the sonic anemometers deployed
throughout the height of the BAO met-tower and vertical profiles of the 3-D
wind velocity sampled with a lidar profiler deployed at the lidar supersite
location. These data acquired are shown in Fig.
for the height of 150 m. In this figure, ranges of the wind direction for
which the sonic anemometers may experience tower wake effects are reported
with shaded areas . For few time stamps,
some differences are observed for the wind data obtained from the two sonic
anemometers, which might be a consequence of the statistical steadiness of
the acquired wind signals and the duration of the measurement sampling
period. A generally good agreement is observed among the different instruments
for both horizontal wind speed, Uh, and wind direction for the entire
duration of the experiment.
In order to perform comparison and linear regression analysis between wind
data acquired from different instruments, data acquired from instruments with
a higher sampling frequency are averaged over the corresponding sampling
period of instruments with a lower sampling frequency. For instance, sonic
anemometer data acquired with a sampling frequency of 20 Hz are averaged over
periods with a duration of 1 s for comparison with V2 lidar data acquired with
a sampling frequency of 1 Hz.
Linear regression analysis among the V2 lidar, SE sonic
anemometer and NW sonic anemometer data for 24 h data reported in
Fig. .
HeightUhR2Wind dir. R2(m)(slope)(slope)SE sonic vs. NW sonic 1000.97 (1.00)0.97 (1.06)1500.97 (1.00)0.97 (1.07)2000.98 (1.00)0.96 (1.08)All heights together0.97 (1.00)0.97 (1.07)V2 lidar vs. NW sonic 1000.92 (0.94)0.88 (0.96)1500.91 (0.93)0.92 (1.00)2000.86 (0.80)0.94 (0.96)All heights together0.90 (0.90)0.91 (0.97)V2 lidar vs. SE sonic 1000.92 (0.93)0.89 (0.90)1500.90 (0.93)0.93 (0.96)2000.76 (0.77)0.96 (0.97)All heights together0.90 (0.91)0.91 (0.92)
Linear regression analysis of Ka-band radars against sonic
anemometer and V2 lidar data.
HeightUhR2Wind dir. R2(m)(slope)(slope)Dual-Doppler radar vs. V2 lidar 1000.89 (1.02)0.90 (0.90)1200.93 (1.04)0.92 (0.90)1400.93 (1.06)0.91 (0.86)1500.92 (1.06)0.92 (0.89)1600.93 (1.08)0.92 (0.86)1800.93 (1.12)0.91 (0.84)2000.93 (1.12)0.90 (0.82)All heights together0.92 (1.08)0.91 (0.87)Dual-Doppler radar vs. NW sonic 100– (–)– (–)1500.91 (1.03)0.93 (0.83)2000.95 (0.92)0.96 (0.87)All heights together0.93 (0.97)0.94 (0.84)Dual-Doppler radar vs. SE sonic 1000.87 (1.10)0.95 (0.89)1500.93 (1.07)0.93 (0.86)200– (–)– (–)All heights together0.91 (1.10)0.95 (0.87)
Linear regression analysis performed between sonic anemometer and the V2
lidar data generally shows a good correlation among the different instruments
for the different heights. In Table , the slope and R2
values resulting from the linear regression analysis are reported for the
different heights and as overall ensemble statistics. It is noteworthy that
Tables and include sonic anemometer data
acquired under wake distortion produced by the met-tower
. Given the good agreement between the sonic
anemometers and the profiling lidars, we felt confident that the data sets
from these two types of instruments can be used to evaluate the accuracy of
virtual-tower measurements with scanning radars and lidars.
Dual-Doppler radar measurements at 150 m height compared with sonic
anemometer and V2 lidar data: (a) horizontal wind speed
Uh (m s-1); (b) wind direction (∘). The
date of the observation is 25 March 2015.
Linear regression analysis of the dual-Doppler radar retrieval
against sonic anemometer and V2 lidar data for all the tested heights:
(a–c) horizontal wind speed Uh (m s-1);
(d–f) wind direction (∘).
In this section, we present the assessment of the dual-Doppler measurements
performed with the two Ka-band radars against sonic anemometer and lidar
profiler wind velocity data. For the retrieval of the horizontal wind speed
and wind direction through the dual-Doppler technique, the vertical velocity
is assumed to be negligible, which allows dropping the last row in Eq. (). In Fig. , horizontal wind speed and wind
direction at 150 m height retrieved from the above-mentioned instruments are
compared. The considered wind data were acquired by the various instruments
at the same height of 150 m; thus no data interpolation was needed for this
analysis. A generally good qualitative agreement can already be perceived.
Difference of dual-Doppler radar retrieval with the reference
instruments, i.e., sonic anemometers and V2 lidar, for all the tested
heights: (a–c) horizontal wind speed Uh (m s-1);
(d–f) wind direction (∘).
In order to achieve a more quantitative characterization of the accuracy in
the dual-Doppler retrieval performed on the radar data, a linear regression
analysis was then performed for both horizontal wind speed and wind
direction. In order to compare radar data with sonic and V2 lidar data over
different heights, a 1-D linear interpolation was performed for each time stamp
in order to estimate the radar wind value for the heights probed by the sonic
anemometers and the V2 lidar. The correlation between the radar data and the
other reference instruments is generally very high, as shown in Fig. , with a correlation always larger than 91 %. Slope
and R2 values resulting from the linear regression analysis among
dual-Doppler radar data, sonic anemometer and V2 lidar data are then reported
in Table for the various heights and as ensemble statistics.
Again, a good agreement between radar and reference instrument data is
generally achieved throughout the height of the virtual tower and without any
noticeable trend in the vertical direction.
Finally, histograms of the difference between the horizontal wind speed
and wind direction measured through the dual-Doppler radar measurements and the
reference instruments are reported in Fig. . For the
horizontal wind speed the mean difference is -0.47, -0.11 and
-0.63 m s-1 compared with the V2 lidar, NW sonic and SE sonic,
respectively, with standard deviations of 0.68, 0.78 and
0.86 m s-1. A similar analysis for the wind direction leads to a mean
difference of 2.6, -4.75 and 2.60∘ compared with the V2
lidar, NW sonic and SE sonic, respectively, with standard deviations of
6.98, 6.28, 6.98∘.
Triple-Doppler lidar measurements at 100 m height and assessment
against sonic anemometer and lidar profiler data: (a) line-of-sight
velocities from the three scanning lidars; (b) horizontal wind speed
Uh (m s-1); (c) wind direction (∘);
(d) vertical velocity W (m s-1).
Retrieval and assessment of triple-Doppler lidar measurements
In this section, we present an assessment study of the
triple-Doppler lidar measurements which were performed with three scanning
Doppler lidars to retrieve the three velocity components. As for the previous
section, assessment of triple-Doppler data is carried out against sonic
anemometer and lidar profiler data. In Fig. a, the
line-of-sight velocities are reported for the measurements carried out at
100 m height during the entire period of the experiments. The wind data
considered for the triple-Doppler retrieval are first quality-controlled as a
function of the carrier-to-noise ratio (minimum value of -25 dB) and then
averaged over the actual sampling period, which is defined as the time for
which the three lidars measured simultaneously over the location of interest.
Statistics of the actual sampling period, i.e., of the overlapping time among
the three scanning lidars, have been already presented in Fig. .
The retrieved vertical velocity was assessed only against the V2 lidar data,
because the horizontal distance of 134 m between the BAO tower and the lidar
supersite location (see Table ) as well as the different
averaging volume of each instrument leads to poorer agreement between sonic
anemometer and triple-Doppler lidar data, as reported in Table . The linear regression in the vertical velocity with the
V2 lidar data, in contrast, shows a good agreement for the height of 200 m
with a slope of 0.94 and a correlation of R2=0.79. As predicted from the
error analysis presented in Table , the reduced
elevation angles of the lidar laser beams for smaller heights lead to a rapid
decay in the accuracy for the retrieval of the vertical velocity through the
triple-Doppler lidar measurements.
Linear regression analysis of triple-Doppler lidar data against the
reference instruments, namely sonic anemometers and V2 lidar.
HeightUhR2Wind dir. R2WR2(m)(slope)(slope)(slope)Triple-Doppler lidar vs. V2 lidar 1000.94 (0.99)0.92 (0.97)0.01 (0.13)1200.97 (0.99)0.93 (0.95)0.27 (0.32)1400.97 (0.97)0.85 (0.97)0.57 (0.52)1600.94 (0.97)0.88 (0.93)0.62 (0.63)1800.95 (1.00)0.95 (0.92)0.77 (0.68)2000.93 (1.07)0.99 (1.00)0.79 (0.94)All heights together0.96 (0.95)0.90 (0.97)0.49 (0.42)Triple-Doppler lidar vs. NW sonic 1000.92 (0.89)0.85 (0.90)0.008 (0.04)2000.90 (1.12)0.90 (0.91)0.13 (0.12)All heights together0.90 (1.02)0.87 (0.90)0.09 (0.1)Triple-Doppler lidar vs. SE sonic 1000.89 (1.12)0.84 (0.91)0.005 (0.012)2000.9 (0.94)0.93 (1.00)0.092 (0.11))All heights together0.89 (1.01)0.87 (0.95)0.03 (0.08)
Linear regression of triple-Doppler lidar data against reference
instruments for all the tested heights: (a–c) horizontal wind speed
Uh (m s-1); (d–f) wind direction (∘).
The horizontal wind speed and direction retrieved through the triple-Doppler
lidar measurements are reported in Fig. b and c,
respectively. In these figures, the respective velocity data directly
measured at 100 m height highlight that – just as for more traditional
instruments, such as sonic anemometers and the lidar profiler – the
triple-Doppler measurement technique allows characterization of a significant
daily variability in wind velocity from quiescent conditions up to about 20 m s-1. Good performance is also observed for the characterization
of the wind direction. Indeed, during the experiment, wind direction varied
all around the full angle of the wind rose, and the triple-Doppler
measurements were able to detect the different angles of the wind direction
and follow its variability as a function of time.
Accuracy in the triple-Doppler retrieval of horizontal wind speed and
direction is then quantitatively characterized through a linear regression
analysis, which was performed for all the heights under examination against
sonic anemometer and lidar profiler data (Fig. ).
Starting with a comparison with the V2 lidar profiler data located over the
lidar supersite location, a very good agreement is estimated between these
measurement techniques. For the horizontal wind speed, the slope is 0.96 with
a correlation of R2=0.95, while for the wind direction the slope is 0.97
and there is a correlation of R2=0.9.
Moving to the linear regression of the triple-Doppler lidar against sonic
anemometer data (Fig. ), the horizontal distance of
134 m between the BAO tower and the lidar supersite location, where all the
scanning lidars are focused, does not significantly affect the agreement
between measurements obtained from the various instruments. Indeed, the slope
for the wind velocity varies between 0.9 and 1.02, with correlation always
larger than R2=0.89. For the wind direction, the slope is 0.9 and 0.95 for
the linear regression against NW and SE sonic anemometers, respectively,
while the correlation is R2=0.87.
Difference of triple-Doppler lidar retrieval with reference
instruments for all the tested heights: (a–c) horizontal wind speed
Uh (m s-1); (d–f) wind direction (∘).
Results of the linear regression analysis for the measurements carried out at
different heights are reported in Table . Considering the
data against the V2 lidar, the slope for the horizontal wind speed is always
very close to 1, with a minimum value of 0.97 and a maximum value of 1.07,
while the correlation is always larger than R2=0.93. For the wind
direction, a reduced level of accuracy is estimated with a correlation larger
than R2=0.88 but with the slope still very close to 1. As for the error
analysis due to to the setup
of the three lidars (see Table ), accuracy in the measurements for both horizontal
wind speed and wind direction is not noticeably changed for the locations at
different heights.
Finally, histograms of the difference between the horizontal wind speed and
direction measured through the triple-Doppler lidar measurements and the
reference instruments are reported in Fig. . For the
horizontal wind speed, the mean differences are -0.38, -0.06 and -0.09 m s-1s, and the standard deviations are 0.83,
1.43 and 1.60 m s-1 with respect to the V2 lidar, NW sonic and
SE sonic, respectively. A similar analysis for the wind direction leads to a
mean difference of 3.36, 7.47 and 11.14∘ with standard
deviations of 25.68, 26.09 and 27.15∘ compared with the
V2 lidar, NW sonic and SE sonic, respectively.
Comparison between lidar and radar virtual-tower measurements
After discussing the assessment of the virtual-tower measurements against the
reference instruments, namely sonic anemometers installed over the BAO
met-tower and a lidar profiler, a direct intercomparison between Ka-band
radar and wind lidar data is now presented.
Intercomparison between radar and lidar virtual-tower measurements:
(a) horizontal wind speed Uh (m s-1) retrieved
with dual-Doppler radar; (b) horizontal wind speed Uh
(m s-1) retrieved with triple-Doppler lidar; (c) difference in
horizontal wind speed Uh (m s-1) between lidar and radar
data; (d) wind direction retrieved from dual-Doppler radar;
(e) wind direction retrieved from triple-Doppler lidar;
(f) difference in wind direction between lidar and radar data.
According to the linear regression analysis presented in Sects. and , a very good level of agreement for both
radar and triple-Doppler lidar data was observed with reference instruments,
as detailed in Tables and . Generally, the
slope obtained for the correlation analysis was very close to 1 for both
measurement techniques in the estimate of wind velocity (between 0.97 and
1.08 for radar data and between 0.95 and 1.02 for lidar data) and wind
direction (between 0.84 and 0.87 for radar data and between 0.9 and 0.97 for
lidar data). Correlation between the virtual-tower measurements and data
obtained from sonic anemometers and the V2 lidar is always larger than
R2>0.91 for the radar measurements and R2>0.87 for the triple-Doppler
lidar data. No systematic bias errors have been observed for both radar and
triple-Doppler lidar measurements for the retrieval of the horizontal wind
speed and wind direction (see Figs. and
).
Statistics of the absolute value of the difference between radar and
lidar wind data averaged over all the available heights:
(a) horizontal wind speed Uh (m s-1) difference;
(b) wind direction difference. Circles represent mean value, while
error bars represent standard deviation.
In Fig. , a qualitative comparison between the wind
data retrieved through the dual-Doppler radar measurements and the
triple-Doppler lidar data is presented. Radar virtual-tower measurements were
performed continuously over the lidar supersite location with an average
sampling period for each virtual tower of 3.3 s. Triple-Doppler lidar
measurements, in contrast, were performed every 10 min due to a test
schedule including other scans than these presented in this paper. Generally
good agreement is observed when virtual towers have been performed simultaneously
with the two Ka-band radars and the three scanning wind lidars. A similar
variability in time and over the different heights was observed through the
two different measurement techniques. Differences between the radar and the
lidar measurements, for both horizontal wind speed and wind direction, were
generally very small compared to the variability observed as functions of
time and height.
In Fig. , statistics of the difference between
the radar and lidar measurements are reported for the different virtual
towers performed. For the wind velocity, the difference averaged over the
height is always smaller than 0.5 m s-1 with a maximum standard
deviation of 0.29 m s-1. For the wind direction, the maximum difference
averaged over height is always smaller than 10∘, and the maximum
standard deviation is 4.79∘.
Concluding remarks
During the XPIA experiment, colocated virtual-tower measurements were
performed with two Ka-band radars and three scanning Doppler wind lidars.
Therefore, these tests provided the unique opportunity to perform a direct
intercomparison between dual-Doppler radar and triple-Doppler lidar
measurements. Furthermore, wind data obtained from the virtual-tower
measurements were also assessed against sonic anemometer data acquired from a
met-tower located at a distance of 134 m from the virtual-tower location and
a lidar profiler that, in contrast, was colocated with the virtual towers.
Results of this assessment study show that – besides the use of different
technologies, measurement volumes and sampling periods – multiple-Doppler
radar and lidar measurements are both characterized by a good level of
agreement with measurements performed with other reference instruments,
namely sonic anemometers and a lidar profiler. Through a linear regression
analysis between virtual-tower measurements, lidar profiler and sonic
anemometer data, it was found that the slope is always within 0.84–1.02,
while the correlation is always larger than R2=0.87. No systematic bias
errors have been detected for either radar or lidar measurements of the wind
horizontal wind speed and direction. Regarding the vertical velocity
retrieved through the triple-Doppler lidar measurements, accuracy
deteriorates rapidly with reducing height along the virtual tower, which is
mainly a consequence of to the lidar setup and the reduced elevation angles.
This assessment study has shown that multiple-Doppler scans performed with
either scanning lidars or radars allow achieving high accuracy in the
retrieval of the wind speed and wind direction. The Ka-band radars generally
provide continuous radial velocity measurements out to the maximum range when
distributed meteorological targets (water droplets, ice crystals etc.) are
present. Overall, The Ka-band radar system is characterized by a higher
carrier-to-noise ratio under clear-air conditions (low aerosol concentration)
and during light precipitations. A limitation of Doppler radars compared to lidars
is the effect of beam spread at large ranges. Indeed, for the radars a
divergence angle of 0.498∘ results in a beam spread of 17.1 m at 2 km
range and 85.5 m at 10 km range. The scanning lidars, in contrast, have poor
signal quality during precipitations, and the carrier-to-noise ratio strongly
depends on the concentration of aerosol suspended in the atmosphere. However,
lidars might have greater data availability under non-precipitation
conditions and typical aerosol concentrations. The divergence angle of the
lidars is practically negligible, leading to a constant spatial resolution
throughout the measurement range. Regarding the scanning capabilities, the
Ka-band radars have a maximum angular velocity in the scanning of 30∘ s-1, while for the lidars it is only 8∘ s-1.
Given the challenges associated with the collection of dual-Doppler radar
data in non-precipitating environments, future experiments could incorporate
both disdrometers and particulate monitors to better characterize clear air
and precipitating environments most conducive to radar data collection. Data
availability for all systems might also improve later in the calendar year
when a greater concentration of scatterers is naturally present in the
atmosphere.
The data from all the instruments deployed during the XPIA field campaign are now
available at DOE's Data Access Portal (DAP) located at https://a2e.pnnl.gov/data. Access to the general public has been open since 1 April 2016. In order to access
the data, users need to create an account on the website given above. For further inquiries please contact
either Julie Lundquist (julie.lundquist@colorado.edu) James Wilczak (james.m.wilczak@noaa.gov).
The authors declare that they have no conflict of interest.
Acknowledgements
This paper was developed based upon funding from the Alliance for Sustainable
Energy, LLC, Managing and Operating Contractor for the National Renewable
Energy Laboratory for the U.S. Department of Energy.
Edited by: W. Shaw
Reviewed by: two anonymous referees
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