AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-4901-2016Proof of concept for turbulence measurements with the RPAS SUMO during the BLLAST campaignBåserudLineline.baserud@uib.noReuderJoachimhttps://orcid.org/0000-0002-0802-4838JonassenMarius O.https://orcid.org/0000-0002-4745-9009KralStephan T.https://orcid.org/0000-0002-7966-8585PaskyabiMostafa B.LothonMarieGeophysical Institute, University of Bergen, Allégaten 70, 5007 Bergen, NorwayUNIS – The University Centre in Svalbard, 9171 Longyearbyen, NorwayFinnish Meteorological Institute, Helsinki, FinlandLaboratoire d'Aérologie, University of Toulouse, CNRS, Toulouse, FranceLine Båserud (line.baserud@uib.no)6October2016910490149138January20161February201615September201615September2016This 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/9/4901/2016/amt-9-4901-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/4901/2016/amt-9-4901-2016.pdf
The micro-RPAS (remotely piloted aircraft system) SUMO (Small Unmanned Meteorological Observer) equipped with a
five-hole-probe (5HP) system for turbulent flow measurements was
operated in 49 flight missions during the BLLAST (Boundary-Layer Late
Afternoon and Sunset Turbulence) field campaign in 2011. Based on data sets
from these flights, we investigate the potential and limitations of airborne
velocity variance and TKE (turbulent kinetic energy) estimations by an RPAS
with a take-off weight below 1 kg.
The integration of the turbulence probe in the SUMO system was still in an
early prototype stage during this campaign, and therefore extensive
post-processing of the data was required. In order to be able to calculate
the three-dimensional wind vector, flow probe measurements were first
synchronized with the autopilot's attitude and velocity data. Clearly visible
oscillations were detected in the resulting vertical velocity, w, even
after correcting for the aircraft motion. The oscillations in w were
identified as the result of an internal time shift between the inertial measurement unit (IMU) and the
GPS sensors, leading to insufficient motion correction, especially for the
vertical wind component, causing large values of σw. Shifting the IMU
1–1.5 s forward in time with respect to the GPS yields a minimum for
σw and maximum covariance between the IMU pitch angle and the GPS
climb angle.
The SUMO data show a good agreement to sonic anemometer data from a
60 m tower for σu, but show slightly higher values for
σv and σw. Vertical TKE profiles, obtained from consecutive
flight legs at different altitudes, show reasonable results, both with
respect to the overall TKE level and the temporal variation. A
thorough discussion of the methods used and the identified uncertainties and
limitations of the system for turbulence measurements is included and should
help the developers and users of other systems with similar problems.
Introduction
The understanding of the complex interaction between the vertical structure
of the atmosphere and the characteristics of atmospheric turbulence is of
major importance for a wide range of practical applications and for basic
atmospheric research. The appropriate parameterization of turbulent exchange
processes in numerical weather prediction and climate models or the
estimation of structural loads in the field of engineering, e.g., for bridges
or wind turbines, are prominent examples.
Vertical profiles of turbulent kinetic energy (TKE) and the underlying
velocity variances of the three-dimensional wind vector are excellent indicators
for the state of ambient turbulence, as they provide information on both the
absolute turbulence level and its spatial characteristics, e.g., local
isotropy. They are also of major importance for the understanding of the TKE
budget by allowing the estimation of the magnitude of TKE production and
vertical transport, which are mechanisms of basic relevance for the
determination of turbulent exchange in atmospheric boundary layer (ABL)
research.
The measurement of velocity variances requires fast-response sensors. For
in situ observations these are typically mast- or tower-mounted sonic
anemometers or multiple-hole flow probes for airborne measurements. Mast- and
tower-based measurements can capture the local turbulence conditions in the
surface layer and in the case of higher masts and towers, also for the stable ABL
as a whole. However, under convective conditions only a fraction of the ABL's
vertical extent can be captured, so that important processes, in particular
in the entrainment zone, cannot be observed. A few attempts have been started
to extend the vertical measurement range by tethered platforms, as balloons,
kites or blimps
e.g.,.
Although showing some promising results, these observational platforms
require considerable infrastructure and have limitations with respect to wind
speed and/or strength of convective turbulence. Remote sensing of velocity
variances, e.g., by sodar e.g., or
lidar systems e.g.,, is able
to reach higher levels in the range of 1 km. Even though these remote
sensing methods are of high value for atmospheric research, they cannot fully
replace in situ observations as they have typically only limited vertical
resolution and sampling rate and as the volume averaging characteristics of
these methods require a number of assumptions to derive turbulence parameters
for the ABL e.g.,.
For these reasons, direct airborne measurements by manned aircraft, providing
a unique flexibility with respect to spatial sampling, have become a more
popular choice for ABL turbulence investigations during the last decades
e.g.,. Corresponding flow
probes are either mounted directly on an exposed and undisturbed position on
fixed wing aircraft or in an instrument rig towed by a helicopter, as in the
case of the Helipod . However, these operations
are, by nature, logistically demanding and expensive. The rapid development
of remotely piloted aircraft systems (RPASs) during the last decade has
provided new airborne sensor platforms for ABL research
, with several of them having proven their capability
for turbulence investigations
e.g.,.
The continuous miniaturization of electronic components and sensors, both for
measurement of meteorological parameters and the required attitude control of
the aircraft's autopilot, now also provides the required capability for a
micro-RPAS, with a take-off weight below 1 kg.
The main intention of this paper is the proof of concept for measurements of
velocity variance and TKE from the Small Unmanned Meteorological Observer
(SUMO), a micro-RPAS with a take-off weight distinctly below 1 kg. The
paper is structured as follows. Section briefly describes the
RPAS SUMO with a focus on the integrated five-hole-probe-based turbulence measurement system (5HP). The turbulence flights performed during the
BLLAST (Boundary-Layer Late Afternoon and Sunset Turbulence) campaign are
introduced in Sect. , while the required data processing
for the calculation of turbulence parameters is described in
Sect. . This includes the time synchronization of the
turbulence and attitude/position data, the transformation into a
meteorological coordinate system and the correction of a time shift between
the inertial measurement unit (IMU) and the GPS sensors to remove remaining oscillations in the vertical
wind component. In Sect. , vertical profiles of TKE and their
time evolution are presented and discussed for different days during the
BLLAST campaign. Section presents an analysis of the different
uncertainties, followed by a brief summary and outlook in the final section, Sect. .
The SUMO platform
SUMO is a micro-RPAS with a length and wingspan of 80 cm and a
take-off weight of around 650 g. The SUMO airframe
consists of a slightly modified version of the commercially available model
aircraft FunJet from Multiplex. The system has been continuously improved and
developed during the last years .
For navigation and flight control, the system uses the open-source autopilot
system Paparazzi, which is developed and maintained under guidance by the
École Nationale de l'Aviation Civile (ENAC) in Toulouse, France
. SUMO is equipped with an inertial measurement unit (IMU)
for attitude control and uses a GPS sensor for navigation and monitoring of
the aircraft's position. During the BLLAST campaign, the corresponding data
were acquired and stored at 10 Hz for the IMU and 4 Hz for
the GPS. The SUMO aircraft had no specific constant speed regulation in the
configuration during the BLLAST campaign, as this would require the data
input from a Pitot tube. This has been avoided on the basis of robustness
considerations of the system. The actual speed regulation is done by a
control loop based on input information of desired climb speed and GPS
velocity. This results (as shown in Fig. ) in fairly constant
values over one flight leg, but in a slight decrease over the whole flight
due to the charging level of the battery, which is at the moment only
compensated for in a simple linear way. A more detailed description of the
SUMO airframe and the sensors used during the BLLAST campaign is given in
.
The most recent development in instrumentation was the integration of a
five-hole flow probe (5HP) with a corresponding data computer hosting the
pressure transducers and data logger . The Aeroprobe data
computer provides airspeed, angles of attack and sideslip and altitude based
on differential pressure measurements at a temporal resolution of
100 Hz. After correcting for the aircraft's attitude and motion, this
enables the calculation of the three-dimensional flow vector at a sufficient
resolution for calculation of turbulence parameters such as turbulent kinetic
energy (TKE).
The 5HP has been calibrated for Mach numbers of 0.044 and 0.088 by the
manufacturer, which corresponds to 15 and 30 ms-1 respectively.
The probe is mounted in the nose of the airframe (see
Fig. ) and is connected to the differential pressure
sensors in the data computer by six silicon tubes 10 cm long. The tip
of the sensor is located approximately 10 cm in front of the
fuselage. Wind tunnel tests of the setup, performed at DLR (Deutsches Zentrum
für Luft- und Raumfahrt), Göttingen, Germany, in 2014, showed no
noticeable effects of flow distortion at this position. The angular response
of the probe was tested both stand-alone and mounted on a SUMO airframe, and
provided nearly identical results within the accuracy limits of the system.
More information on the 5HP system can be found in the manual provided by the
manufacturer and in .
The 5HP and the data computer from Aeroprobe as mounted in the SUMO
airframe.
During the BLLAST campaign, the 5HP data computer was not integrated into the
SUMO's data acquisition system. The 5HP flow data and the aircraft position
and attitude were therefore collected on different, unsynchronized data
loggers with different temporal resolution. This results in certain
challenges with respect to post-processing and will be further described and
discussed in Sects. and .
SUMO turbulence measurements during BLLAST
The BLLAST field campaign took place from 14 June to 8 July 2011 in
Lannemezan, France. The main goal of the campaign was an in-depth
investigation of the turbulence decay during the afternoon transition period.
A wide range of ABL instrumentation was deployed and operated in the area,
including energy balance stations, meteorological towers, radiosondes, manned
aircraft, RPASs, tethered balloons, and different types of remote-sensing
instruments. A comprehensive overview of the scientific goals and the
campaign setup is presented in .
The RPAS SUMO performed a total of 299 flights during the BLLAST campaign,
including 49 turbulence transect flights with the 5HP. For more information
on the missions the reader is referred to and
. All turbulence flights took place in the vicinity of the
two main instrumented locations in the campaign area, Site 1 and Site 2
. The pattern for all turbulence missions during the BLLAST
campaign was similar and consisted of straight legs of around 1000 m
length with circular turns at each end (see Figs.
and ). An overview of all turbulence flights, including the
vertical levels probed, is presented in Table . The battery
capacity of SUMO allowed for flight missions of 20 to 25 min,
corresponding to 8 to 10 straight segments. The most common flight strategies
were either four legs at two different altitudes, or two legs at four
different altitudes (see Fig. ).
Typical flight pattern for the turbulence measurements from SUMO
during the BLLAST campaign. Turbulence parameters are only evaluated for the
straight legs (red). This example is from flight no. 38 at 09:15 UTC on
27 June.
Flight path of the four SUMO flights, no. 27 (65 m a.g.l.),
29 (65 m a.g.l.), 30 (70 m a.g.l.) and 31
(70 m a.g.l.), in the vicinity of the 60 m meteorological
tower (blue diamond) situated at Site 1. The straight legs used for
calculation of turbulence parameters are marked in red. Each leg is
approximately 1 km long. Satellite picture from Google
Earth.
Sonic anemometer measurements (10 Hz) of vertical velocity,
w (blue), and 10 min running mean standard deviation of vertical
velocity, σw (black), from the 60 m meteorological tower for
19 June (top) and 20 June (bottom). The timing of the SUMO flight missions
(no. 27, 29, 30 and 31) is indicated by the red lines.
Two of the 49 flights had to be rejected due to problems with the data
loggers. Several other flights had to be excluded from further analysis due
to unsatisfactory time synchronization between the 5HP flow data and the
IMU/GPS. A description of the corresponding synchronization procedure and the
defined acceptance and rejection criteria is given in Sect. .
Additional flights were excluded due to large deviations from the desired
flight level during turbulence segments. Finally a total of 23 flights have
been used for the analysis of atmospheric turbulence presented in this study.
Four flights (no. 27, 29, 20 and 31), performed close to the 60 m
tower at Site 1 e.g., at altitudes between 65 and
70 m (Fig. ) have been used to compare the SUMO flow
measurements with data from a three-dimensional sonic anemometer (Campbell CSAT3) mounted
at 60 m (Fig. ). Ten flights from 15 June (all with three to four legs at two
altitudes) and nine flights from 27 June (all with two legs at four altitudes)
at Site 2, have been chosen to investigate the temporal evolution of
atmospheric turbulence by the means of TKE profiles (see
Sect. ).
All SUMO turbulence transects performed during the BLLAST campaign.
The flights used for further analysis are shown in bold. The abbreviation
“sshs” refers to the “small-scale heterogeneity site” located at Site 1.
The flight times are all in UTC, and represent the start of each
flight.
Example of the cross-correlation analysis between the GPS ground
speed and the 5HP airspeed for one of the SUMO flights, with correlation
coefficient and time shift in the left panel and time series of 5HP airspeed
and GPS ground speed after synchronization in the right panel. The data are
for flight no. 41 on 27 June at 12:30 UTC.
Data processing
In order to transform the measured flow vector from the SUMO's turbulence
system into a meteorological (earth-fixed) coordinate system with the
velocity components u (positive for wind from the west), v (positive for
wind from the south) and w (positive upward), the aircraft's attitude and
velocity need to be known with high accuracy. Since the flow and IMU/GPS data
relevant for this conversion were recorded on different data loggers, the
first step of the post-processing was to synchronize the flow and IMU/GPS
data sets in time. For this the time shift between the airspeed measured by
the 5HP and the GPS ground speed was identified by a cross-correlation
analysis, calculating the correlation coefficient, r, as a function of the
time shift. Both velocities are expected to be highly correlated, especially
during flight maneuvers, such as start, landing and turns.
The synchronization procedure was applied to all turbulence flights, and the
result for one example is presented in Fig. . It shows a clear
peak of above 0.99 in r for a time shift of 3.5 s. Twenty-two of
the flights had an r above 0.97. Flights with rmax<0.91 were removed
from further analysis. Some additional flights were ignored if a visual
inspection revealed several possible time shifts giving high correlation
coefficients (broad peak or prominent secondary peaks in the corresponding
plots in the left panel of Fig. ). The time shifts were
typically in the range of ±10 s, and are related to different and
varying start-up times of the 5HP data computer after switching the power on.
A delayed manual start of the ground control station software after
connecting the battery of the SUMO aircraft led to time shifts of up to
1 min on a few occasions.
Furthermore, the IMU and GPS data, which were recorded at a lower rate, were
upsampled to the 100 Hz rate of the 5HP by linear interpolation.
Potential implications of this procedure on the retrieval of turbulence
parameters are discussed in Sect. .
Thereafter, we identified straight flight legs for our turbulence analysis
based on the coordinates used to define the autopilot's flight track, which
are recorded during operation. This gave us an objective and automatic way to
pick out the straight legs of each flight. The turbulence legs during BLLAST
had a typical length of about 1000 m.
The wind speed with respect to the earth is found by performing a coordinate
transformation from a Lagrangian into a Eulerian system, based on the
velocity of air with reference to the aircraft and the velocity and
orientation of the aircraft with respect to the earth. The u, v and w
wind components in the earth coordinate system were calculated over straight
flight legs based on the well-established equations of .
The original full set of equations include terms involving the product of
angular velocities and the separation distance between the turbulence sensor
and the IMU/GPS. According to , the contribution of these
terms becomes insignificant if the distance is less than 10 m in the case
of a manned aircraft moving at a speed of the order of 100 ms-1.
For the SUMO system, typically moving with 20 ms-1, the
separation distance is about 60 cm. We have calculated the size of
these additional terms for SUMO, and found them to be of the order of 0.06 ms-1 for the vertical component, and even smaller for the
horizontal components, and thus too small to make any significant
contribution. Consequently we are neglecting these terms.
Example of the IMU time shift analysis for flight no. 27, 29, 30 and
31, showing the variation in σw for the range of time shifts between
0 and 2.5 s. The minimum value of σw for each leg can be seen by
the black asterisk. The average time shift for each flight is shown by the
dashed vertical line.
In Eqs. ()–(), the 5HP airspeed is given by
Ua, while the angle of attack and the angle of sideslip are given
by α and β, respectively. The attitude angles' pitch, roll and
yaw are given by θ, ϕ and ψ respectively, and the three
components of the aircraft's ground speed by ugs, vgs and
wgs. Due to the lack of a direct measurement for ψ, we used
the heading angle obtained from the GPS track for this conversion.
Example of the GPS climb angle and the IMU pitch angle after
applying a time shift of 1.2 s to the IMU, for one single leg (about
1 km length) of flight no. 29.
After the correction for the aircraft's movement by applying the coordinate
transformation (Eqs. –), the resulting w frequently
shows features of an oscillation, which seems to be highly correlated with
the aircraft's vertical motion. The mentioned oscillations lead to increased
values of the standard deviation for the vertical wind component, and thus
result in unrealistic estimates of TKE.
Mean standard deviations of u (upper left panel), v (upper right
panel) and w (lower left panel) components of the wind, and mean TKE (lower
right panel) from SUMO (y axis) against the sonic anemometer from the 60 m
meteorological tower (x axis), for flights no. 27 (triangle), no. 29
(square), no. 30 (circle) and no. 31 (diamond). The black bars show the variation between all the straight legs within each
flight for all symbols.
Inspection of the magnitude and phase of our contributing terms for
Eq. () reveals an insufficient cancellation and therefore aircraft motion still
remaining in w. Based on further investigations of this, we
have identified the oscillations in the vertical wind to be the result of an
internal time shift between the IMU and the GPS. Internal time lags between
sensors (of the order of 1 s) were also found by
. The lags arising from slightly different internal
data processing procedures (e.g., acceleration-to-speed/position compared to
position-to-speed or by the use of filters) for the individual sensors
systems.
A range of time shifts was applied to the IMU for each flight segment, and
the value giving the minimum difference between the IMU pitch and the GPS
climb angle (a pair of quantities expected to have high covariance) also
yielded the minimum in σw. Our initial range of time shifts was over
several of the periodic oscillations, in both directions, to ensure that the
time shift we found was the absolute, and not only a local, minimum. Shifting
the IMU pitch forward in time (1–1.5 s) gave a minimum σw
for all flight legs from BLLAST. With the IMU pitch and GPS climb angle in
phase (see Fig. ), the oscillations in the first and second
part of Eq. () cancel out. We have corrected for this time shift by
moving the IMU forward in time using the mean time shift obtained over all
legs for each flight. Figure presents the time shift analysis
for the four flights (no. 27, 29, 30 and 31) in the vicinity of the
60 m tower. One can note the well defined minima in σw for all
legs. Some spread between individual legs can be found, especially for legs
with less pronounced oscillating features, i.e., the legs that least resemble
the pitching calibration maneuver .
However, the difference in minimum σw for the individual legs
compared to the flight mean is very small.
A correction was applied to the sonic anemometer measurements to make the
data comparable to the SUMO measurements. The u and v components were
turned into the east/west and north/south directions respectively, by the
corrections u=-usin(β-γ)-vcos(β-γ) and
v=ucos(β-γ)-vsin(β-γ), where β=63.53∘ and
γ=112.25∘.
Results
Figure presents the comparison of σu,
σv, σw, and TKE from SUMO to the data from the sonic
anemometer for all four flights in the vicinity of the tower. For SUMO, the
σ and TKE are first calculated over each straight leg. The resulting
average value for one flight (i.e., each symbol in
Fig. ) is the mean over all individual legs
within that flight. For the sonic anemometer, the σ and TKE are calculated over
10 min time periods, corresponding to the timing of each individual
SUMO leg. The resulting average for one flight (i.e., each symbol in
Fig. ) is the mean over these values.
The data from SUMO fit quite well with the data from the sonic, especially
for σu. For σv, the SUMO data show slightly higher values, and
for σw we found SUMO around 0.25 ms-1 higher than for
the sonic anemometer. Values of TKE are consequently slightly higher for the SUMO. The
largest difference between SUMO and the sonic anemometer is found for flight no. 27.
Average spectra for the u (left panel), v (middle panel) and w
(right panel) components of the wind from SUMO (black) and the sonic anemometer from the
60 m tower (blue), for all legs of flight no. 29. The -5/3 line
(dashed gray) indicates the inertial subrange of the
spectra.
SUMO turbulence transects near the 60 m tower. Wind direction
(WD), wind speed (WS) and the crosswind (CW) component are based on
10 min average values from the sonic anemometer mounted at
60 m. The maximum error in ψ (ψmax(err)) is
estimated based on the airspeed from SUMO and the CW. The integral length
scales for the sonic at 60 m (LSs) are calculated using
horizontal wind speed over 10 min averaging periods.
Figure presents the spectra for flight no. 29 from SUMO in
comparison to the spectra from the sonic anemometer. The spectra are calculated using a
fast Fourier transform analysis of the detrended signals of each
individual leg for SUMO and over 10 min time periods, corresponding to
the timing of each leg for the sonic anemometer. Figure presents the
average frequency signals for both the SUMO and the sonic.
The SUMO and the sonic spectra for u and v are similar for a wide range
of frequencies, both with respect to energy levels and the shape of the
spectra. For the w component, the SUMO shows slightly higher energy levels.
The spectra follow, however, the -5/3 slope for the inertial subrange.
Three peaks are visible (at ∼ 1 Hz for u and v and at
∼ 2 Hz for w), which we relate to aircraft control mechanisms
in the horizontal and the vertical directions . This will
be investigated in more detail in the future, so that we can most
appropriately remove this contribution from the SUMO data.
The chosen 10 min averaging period for the sonic data is based on the
application of Taylor's hypothesis of “frozen” turbulence
, i.e., the time it takes the air mass, probed by SUMO on a
straight leg of around 1 km, to be advected past the stationary
tower. The wind speeds were generally weak during the whole campaign, with
daily average surface winds below 2 ms-1.
From Table , it is seen that the winds at 60 m
were also weak during the time of the four SUMO flights. A comparison of the
flight leg direction with the wind direction shows head- and tailwind for the
legs during flights no. 27 and 29 and a weak side wind for flights no. 30 and
31. See Table and Sect. for additional
information and discussion of potential uncertainties related to the
comparison of the SUMO and the sonic measurements.
Nine flights at Site 2 on 27 June were used to study the time evolution of
TKE profiles. Seven of these flights (no. 38, 40, 41, 42, 44, 46 and 47)
consist of two straight legs at four different altitudes of 60, 150, 300 and
500 m above ground level (a.g.l.). An example of this type of flight
pattern can be seen in Fig. . The remaining two flights
(no. 43 and 45) consist of eight and nine straight legs at one altitude of
340 m a.g.l. TKE was first calculated for each straight leg and then
averaged over all legs of the same flight at a given altitude. The resulting
evolution of the TKE profiles can be seen in Fig. .
Profiles of TKE from 27 June at Site 2. Consecutive flights are
separated by color. The average TKE value over two legs, for each altitude
(60, 150, 300 and 500 m a.g.l.), is shown by the circles. For the
two flights with straight legs at 340 m a.g.l., the diamonds
represent the average TKE values. The horizontal bars show the variation
between individual legs. The flight times given are all in UTC, and represent
the start of each flight.
27 June was a hot and cloud-free convective day, with surface temperatures
reaching 30 ∘C. The boundary layer (BL) height during this day did not behave in a
“textbook” manner. It grew fast in the morning and reached a maximum of
around 1200 m (observed with various measurement platforms like
ultrahigh-frequency (UHF) wind profilers, radiosondes and RPASs) for a period of less than 1 h (around
14:00 UTC), before decaying even faster in the afternoon .
The TKE profiles develop in parallel with this evolution of the boundary
layer height. The lowest TKE values are observed during morning and evening,
with very similar overall levels. The distinct maximum in the early afternoon
is limited to a period of less than 2 h. Only this profile exhibits the
shape of a typical TKE profile in a fully developed convective boundary layer (CBL), with increasing
values with altitude until a maximum is reached at around one-third of the BL
height, which is consistent with . The largest diurnal
variation is found at 150 and 300 m a.g.l., while the TKE values
vary less in the highest and lowest levels. In particular, the morning and
evening profiles show increased values at the lowest level of 60 m,
indicating the importance of shear production on TKE during these times. This
is supported by the increase in wind speed observed at the surface for the
morning and evening . The profiles around noon are
characterized by TKE values that are rather constant with height. Similar TKE
values have been found by for flights with the unmanned
aircraft M2AV from Site 1 on 2 July of the BLLAST campaign, with maximum
values between 1.2 and 1.5 m2s-2 (200–300 m a.g.l.) at
14:30 UTC, and minimum values below 0.3 m2s-2
(150–300 m a.g.l.) at 18:30 and 20:30 UTC.
The largest variation between individual legs is found for the flight at
12:30, 13:32 and 14:42 UTC. Especially at 13:32 UTC at
150 m a.g.l., our straight legs are likely to be too short and too
few to sufficiently sample the largest eddies .
Figure presents the time series of TKE from 10 SUMO
flights during the 15 June at Site 2, which is an example from a day with
cloudy weather conditions . The BL height grew fast
in the morning and reaching values of around 1000 m around noon and
remained nearly constant for a few hours in the afternoon. Each flight during
this day consisted of three to four straight legs at both 65 and
150 m a.g.l. During this day TKE at both levels shows a clear
maximum around 15:00 UTC before it rapidly decays throughout the afternoon.
This maximum is characterized by higher TKE values at the 150 m
level, again indicating the typical shape of a TKE profile in the developed
CBL. During this period we also see the largest spread between the individual
legs, again indicating insufficient sampling of the largest eddies
. For the rest of the day the TKE values from individual
legs within a flight agree more closely, and the values for both levels
are also rather similar.
Evolution of TKE from 15 June at Site 2. The colors indicate the
different altitudes of 65 (blue) and 150 (red) m a.g.l. Mean TKE
over all legs is shown by the circles. The vertical bars show the variation
between individual legs. The flight times are all in UTC, and represent the
start of each flight.
Uncertainty analysis
The SUMO system was still in a prototype stage during BLLAST when it comes to
turbulence measurements, requiring extensive data post-processing and
assumptions to be made in order to extract and validate the velocity variance
data in three dimensions, which are the basis for the TKE estimation. The
following section provides a discussion of the different sources of
uncertainty identified and of potential pathways and suggestions to improve
the situation in the future. Although some of the issues discussed here have
already been improved or solved in the further development of the SUMO
system, we expect that these methods and techniques can be valuable in a
general context, i.e., for the developers and users of other systems with
similar problems.
The unsynchronized data loggers of the autopilot and the turbulence probe can
cause some uncertainty. One cannot be more accurate in timing than the
slowest partner, i.e., GPS (at the moment 4 Hz), leaving us with a
potential maximum uncertainty of 0.25 s. The upsampling of this GPS
data and the 10 Hz attitude data can change the spectral behavior of
the resulting motion-corrected data sets. The latest version of SUMO uses one
common data logger for the 5HP and all IMU/GPS data. For newer systems we aim
to increase the IMU sampling rate to 100 Hz, and the GPS sampling rate
to 10 or 20 Hz, in order to remove these issues completely.
The yaw angle (ψ) has not been measured accurately, but is taken to be the
angle of the flight track (heading angle). This simplification might cause an
error in the resulting horizontal wind components, and hence, also affect the
TKE. However, it can be assumed that this does not lead to large errors as
long as the aircraft's ground speed is significantly higher than the side
wind component.
We have calculated the crosswind (CW) components for the four flights close
to the tower, based on a comparison of the SUMO flight track, and the wind
direction (WD) and wind speed (WS) measured by the sonic at 60 m.
Estimations of the maximum error in ψ, based on the airspeed of SUMO
(∼ 22 ms-1) and the CW, can be found in
Table . Following the error estimation in
, this will give a maximum error for SUMO below
0.01 ms-1 for the u component and below
±0.06 ms-1 for the v component, for the four flights close
to the 60 m tower. In addition, during the rest of the campaign, we do not
expect considerably higher uncertainties, as the observed wind speeds were
relatively low.
Following this argumentation we conclude that the assumptions made for ψ
do not lead to significant errors under conditions as experienced during the
BLLAST campaign, as winds were weak compared to the aircraft's ground speed
of around 20 ms-1. For measurements in situations with a strong
crosswind component, this has, however, to be taken into account as a
potential error source.
When transforming the wind vector from the aircraft to the earth-fixed
coordinate system, we have neglected terms involving the product of angular
velocities and the separation distance between the turbulence sensor and the
IMU/GPS. Tests have shown that the effects of these terms are insignificant
for SUMO (of the order of 0.06 ms-1).
Comparing the measurements of standard deviations and TKE from SUMO to the
corresponding measurements from the sonic anemometer mounted at the
60 m meteorological tower may require some additional considerations
regarding the comparability of the two methods. The two basic assumptions
that have to be fulfilled are Taylor's hypothesis and horizontal homogeneity.
As described by the area of interest was characterized by
different kinds of surfaces, partially causing significant differences in the
surface temperature , and consequently in the surface
forcing expressed by sensible and latent heat fluxes. These surface
heterogeneities are likely to influence the two measurement systems in
different ways. The footprint at the stationary tower is only dependent on
the meteorological conditions, i.e., stratification, wind speed and direction,
which can be assumed to be rather constant with time. In the case of the SUMO
platform, the footprint shows an additional dependency on the current
location of the airplane, thus being more affected by surface heterogeneity
(which makes the measurements so valuable, because they capture a more
realistic average of turbulent transport in the area). Additional differences
might arise from the horizontal distance between the flight track and the
location of the tower and the different averaging procedures that have to be
applied to calculate mean turbulent quantities, i.e temporal and spatial
averaging.
The averaging period of 10 min for the tower data does not exactly
correspond to the averaging distance of 1 km of the horizontal flight
legs of all flights. This choice is based on a compromise between having a
long enough period for good statistics and a short enough period to ensure
stationary conditions. Table presents the integral length
scales calculated from horizontal wind speed for the 10 min averaging
periods from the sonic (60 m tower). The values are between
60 and 70 m for flights no. 27 and 29, slightly above 20 m for
flight no. 30 and almost 90 m for flight no. 31.
The SUMO legs (around 1 km length) might be too short (and too few)
to capture the largest turbulent scales. This is evident from the spread
between individual legs, especially during the highly turbulent regimes
(Figs. , and
). Figure also indicates that SUMO has
trouble capturing the turbulent production scales.
Summary and outlook
We present turbulence measurements from the BLLAST field campaign, conducted
in summer 2011, obtained using the Aeroprobe 5HP system on board the
micro-RPAS SUMO. This system was still in an early prototype stage during the
BLLAST campaign, and extensive post-processing of the resulting data was
therefore needed in order to calculate the turbulence parameters. The 5HP and
the autopilot data loggers were not yet synchronized, for example. We solved
this problem through cross-correlating the airspeed measured by the 5HP and
the ground speed from the GPS, and correcting for the corresponding time
shift. Furthermore, an oscillation in the vertical wind component was
discovered. We identified this as the result of an internal time lag between
the IMU and the GPS sensor systems. Shifting the IMU 1–1.5 s forward
in time with respect the GPS yields a minimum for σw and a maximum
covariance between the IMU pitch angle and the GPS climb angle.
The resulting standard deviations of the three wind components, σu,
σv and σw, together with TKE from four SUMO flights, compare
fairly well with measurements from a sonic anemometer mounted at 60 m
on a meteorological tower, with the SUMO showing slightly higher values.
Vertical profiles of TKE, obtained from consecutive flight legs at different
altitudes, show low TKE values during morning and evening, and higher TKE
values during early afternoon, which would be expected given the time
development in surface forcing and corresponding ABL structure on the
investigated days.
Since the BLLAST campaign, the SUMO system has been improved in several
regards. The aircraft attitude and 5HP data are now synchronized on board, and
are logged using one single data logger. There are no longer problems with
suboptimal aircraft attitude (pitch) control tuning, which we believe was
the cause for the observed low-frequency oscillating aircraft motion observed
for the BLLAST campaign. Battery technology is in rapid development, and new
batteries have become available since BLLAST, allowing for flights lasting up
to 1 h. For turbulence measurements this enables us to perform flights
with either longer straight segments or an increased number of straight
segments per flight, both increasing the statistical relevance of our
measurements. This also gives us the additional flight time needed to perform
in-flight calibration routines before
measurement flights. It is evident that this is crucial to detect e.g.,
mounting errors and flow distortion effects for the sensors. This can also
help to identify time lags between individual sensor systems, as a
correlation analysis will be aided by having the quantities cover a
sufficient wide range of values. In addition, a fast-response temperature
sensor has been tested with the system, allowing for the
direct estimation of turbulent fluxes of sensible heat.
Still, some challenges with the system remain. Currently, the GPS heading
data are used for estimating the aircraft yaw angle. For cases with weak
crosswinds, such as those presented herein, this has minor influences on the
estimated turbulence parameters since the deviation from the true yaw angle
is minimal. However, for cases with strong cross-winds we have previously
observed larger deviations. To address this shortcoming in the future, we are
looking into possibilities of measuring the true yaw angle directly, e.g., by
magnetometers or the use of two differential GPS receivers. In addition, the
present SUMO airframe and the mounting of the 5HP exposed and unprotected in
the nose of the aircraft require an expert pilot for safe landings. In the
future, alternative airframes or an alternative mounting of the 5HP will be
considered for increased user-friendliness.
As described in the introduction, the potential of the turbulence measurement
capabilities of the presented SUMO system cover a wide range of applications
and extends beyond basic research on atmospheric turbulent characteristics.
Other example applications include the validation of numerical weather
prediction models, the characterization of wakes within wind farms and the
estimation of turbulent heat fluxes when the system are combined with a
fast-response temperature sensor.
Data availability
The data used in this study are freely available from the BLLAST database:
http://bllast.sedoo.fr/database.
Acknowledgements
The BLLAST field experiment was made possible thanks to the contribution of
several institutions and support: INSU-CNRS (Institut National des Sciences
de l'Univers, Centre national de la Recherche Scientifique, LEFE-IDAO
program), Météo-France, Observatoire Midi-Pyrénées
(University of Toulouse), EUFAR (EUropean Facility for Airborne Research) and
COST ES0802 (European Cooperation in Science and Technology). The field
experiment would not have occurred without the contribution of all
participating European and American research groups, which all have
contributed a significant amount.
The BLLAST field experiment was hosted by the instrumented site of Centre de
Recherches Atmosphériques, Lannemezan, France (Observatoire
Midi-Pyrénées, Laboratoire d'Aérologie). BLLAST data are managed
by SEDOO, from Observatoire Midi-Pyrénées.
The participation of the Meteorology Group of the Geophysical Institute,
University of Bergen, was facilitated by contributions of the Geophysical
Institute and the Faculty of Mathematics and Natural Sciences under the
“smådriftsmidler” scheme, a travel stipend by the Meltzer Foundation in
Bergen, and the Short Term Scientific Mission (STSM) scheme within the COST
Action ES0802 “Unmanned Aerial Vehicles in Atmospheric Research”.
The authors are grateful to Anak Bhandari for the technical assistance in the
preparation of the campaign, and to Christian Lindenberg, the SUMO chief
pilot during the campaign. Without his passion, determination and patience we
would never have achieved this large number of flights.
We are also grateful to Detlef Hübner from DLR (Deutsches Zentrum für
Luft- und Raumfahrt) in Göttingen, Germany, for facilities and technical
assistance during the wind tunnel experiments with SUMO in
2014. Edited by: E. Pardyjak
Reviewed by: two anonymous referees
ReferencesAeroprobe: On-The-Fly! Air Data System User's Manual Revision F, 1/2012,
available at: https://recuv-ops.colorado.edu/.../OTF_Manual.pdf, 2012.Balsley, B. B., Jensen, M. L., Frehlich, R. G., Eaton, F. D., Bishop, K. P.,
and Hugo, R. J.: In-situ turbulence measurement technique using
state-of-the-art kite/blimp platforms, in: Proc. SPIE 3706, Airborne Laser
Advanced Technology II, edited by: Steiner, T. D. and Merritt, P. H., 3706,
2–10, 10.1117/12.356947, 1999.Bange, J., Beyrich, F., and Engelbart, D. a. M.: Airborne measurements of
turbulent fluxes during LITFASS-98: Comparison with ground measurements and
remote sensing in a case study, Theor. Appl. Climatol., 73, 35–51,
10.1007/s00704-002-0692-6, 2002.Bange, J., Spieß, T., Herold, M., Beyrich, F., and Hennemuth, B.:
Turbulent fluxes from Helipod flights above quasi-homogeneous patches within
the LITFASS area, Bound.-Lay. Meteorol., 121, 127–151,
10.1007/s10546-006-9106-0, 2006.BLLAST: BLLAST dataset, available at: http:/bllast.sedoo.fr/database, last access: 7 January, 2016.Braam, M., Beyrich, F., Bange, J., Platis, A., Martin, S., Maronga, B., and
Moene, A. F.: On the Discrepancy in Simultaneous Observations of the
Structure Parameter of Temperature Using Scintillometers and Unmanned
Aircraft, Bound.-Lay. Meteorol., 158, 257–283,
10.1007/s10546-015-0086-9, 2016.Båserud, L., Flügge, M., Bhandari, A., and Reuder, J.:
Characterization of the SUMO Turbulence Measurement System for Wind Turbine
Wake Assessment, Energy Procedia, 53, 173–183,
10.1016/j.egypro.2014.07.226, 2014.Corsmeier, U.: Airborne turbulence measurements in the lower troposphere
onboard the research aircraft Dornier 128-6, D-IBUF, Meteorol. Z., 10,
315–329, 10.1127/0941-2948/2001/0010-0315, 2001.Darbieu, C., Lohou, F., Lothon, M., Vilà-Guerau de Arellano, J.,
Couvreux, F., Durand, P., Pino, D., Patton, E. G., Nilsson, E.,
Blay-Carreras, E., and Gioli, B.: Turbulence vertical structure of the
boundary layer during the afternoon transition, Atmos. Chem. Phys., 15,
10071–10086, 10.5194/acp-15-10071-2015, 2015.Drüe, C. and Heinemann, G.: A Review and Practical Guide to In-Flight
Calibration for Aircraft Turbulence Sensors, J. Atmos. Ocean. Tech., 30,
2820–2837, 10.1175/JTECH-D-12-00103.1, 2013.Elston, J., Argrow, B., Stachura, M., Weibel, D., Lawrence, D., and Pope, D.:
Overview of Small Fixed-Wing Unmanned Aircraft for Meteorological Sampling,
J. Atmos. Ocean. Tech., 32, 97–115, 10.1175/JTECH-D-13-00236.1, 2015.ENAC: Paparazzi User's Manual, available at:
http://wiki.paparazziuav.org/w/images/0/0a/Users_manual.pdf, 2008.Frehlich, R.: Doppler lidar measurements of winds and turbulence in the
boundary layer, IOP Conference Series: Earth and Environmental Science, 1,
012017, 10.1088/1755-1307/1/1/012017, 2008.Gaynor, J. E.: Accuracy of sodar wind variance measurements, Int. J. Remote
Sens., 15, 313–324, 10.1080/01431169408954075, 1994.Guest, P. S.: Measuring turbulent heat fluxes over leads using kites, J.
Geophys. Res., 112, C05021, 10.1029/2006JC003689, 2007.Lampert, A., Pätzold, F., Jiménez, M. A., Lobitz, L., Martin, S.,
Lohmann, G., Canut, G., Legain, D., Bange, J., Martínez-Villagrasa, D.,
and Cuxart, J.: A study of local turbulence and anisotropy during the
afternoon and evening transition with an unmanned aerial system and mesoscale
simulation, Atmos. Chem. Phys., 16, 8009–8021, 10.5194/acp-16-8009-2016,
2016.
Lenschow, D. H. (Ed.): Aircraft Measurements in the Boundary Layer,
in: Probing the Atmospheric Boundary Layer, Am. Meteorol. Soc., Boston, MA, US, 39–55,
1986.Lenschow, D. H. and Spyers-Duran, P.: Measurement Techniques: Air Motion
Sensing, National Center for Atmospheric Research, Bulletin No. 23,,
available at: https://www.eol.ucar.edu/raf/Bulletins/bulletin23.html,
1989.Lenschow, D. H. and Stankov, B. B.: Length Scales in the Convective Boundary
Layer, J. Atmos. Sci., 43, 1198–1209,
10.1175/1520-0469(1986)043<1198:LSITCB>2.0.CO;2, 1986.Lothon, M., Couvreux, F., Donier, S., Guichard, F., Lacarrère, P.,
Lenschow, D. H., Noilhan, J., Saïd, F., Lacarrere, P., Lenschow,
D. H., Noilhan, J., and Said, F.: Impact of coherent eddies on airborne
measurements of vertical turbulent fluxes, Bound.-Lay. Meteorol., 124,
425–447, 10.1007/s10546-007-9182-9, 2007.Lothon, M., Lohou, F., Pino, D., Couvreux, F., Pardyjak, E. R., Reuder, J.,
Vilà-Guerau de Arellano, J., Durand, P., Hartogensis, O., Legain, D.,
Augustin, P., Gioli, B., Lenschow, D. H., Faloona, I., Yagüe, C.,
Alexander, D. C., Angevine, W. M., Bargain, E., Barrié, J., Bazile, E.,
Bezombes, Y., Blay-Carreras, E., van de Boer, A., Boichard, J. L., Bourdon,
A., Butet, A., Campistron, B., de Coster, O., Cuxart, J., Dabas, A., Darbieu,
C., Deboudt, K., Delbarre, H., Derrien, S., Flament, P., Fourmentin, M.,
Garai, A., Gibert, F., Graf, A., Groebner, J., Guichard, F., Jiménez, M.
A., Jonassen, M., van den Kroonenberg, A., Magliulo, V., Martin, S.,
Martinez, D., Mastrorillo, L., Moene, A. F., Molinos, F., Moulin, E.,
Pietersen, H. P., Piguet, B., Pique, E., Román-Cascón, C.,
Rufin-Soler, C., Saïd, F., Sastre-Marugán, M., Seity, Y.,
Steeneveld, G. J., Toscano, P., Traullé, O., Tzanos, D., Wacker, S.,
Wildmann, N., and Zaldei, A.: The BLLAST field experiment: Boundary-Layer
Late Afternoon and Sunset Turbulence, Atmos. Chem. Phys., 14, 10931–10960,
10.5194/acp-14-10931-2014, 2014.Majumdar, A. K., Eaton, F. D., Jensen, M. L., Kyrazis, D. T., Schumm, B.,
Dierking, M. P., Shoemake, M. A., Dexheimer, D., and Ricklin, J. C.:
Atmospheric turbulence measurements over desert site using ground-based
instruments, kite/tethered-blimp platform, and aircraft relevant to optical
communications and imaging systems: preliminary results, in: Proc. SPIE
6304, Free-Space Laser Communications VI, 63040X, 10.1117/12.684010,
2006.Mansour, M., Kocer, G., Lenherr, C., Chokani, N., and Abhari, R. S.:
Seven-Sensor Fast-Response Probe for Full-Scale Wind Turbine Flowfield
Measurements, J. Eng. Gas Turb. Power, 133, 081601, 10.1115/1.4002781,
2011.Martin, S., Bange, J., and Beyrich, F.: Meteorological profiling of the lower
troposphere using the research UAV “M2AV Carolo”, Atmos. Meas. Tech., 4,
705–716, 10.5194/amt-4-705-2011, 2011.Muschinski, A., Frehich, R., Jensen, M., Hugo, R., Hoff, A., Eaton, F., and
Balsley, B.: Fine-Scale Measurements Of Turbulence In The Lower Troposphere:
An Intercomparison Between A Kite- And Balloon-Borne, And A Helicopter-Borne
Measurement System, Bound.-Lay. Meteorol., 98, 219–250,
10.1023/A:1026520618624, 2001.Pichugina, Y. L., Tucker, S. C., Banta, R. M., Brewer, W. A., Kelley, N. D.,
Jonkman, B. J., and Newsom, R. K.: Horizontal-Velocity and Variance
Measurements in the Stable Boundary Layer Using Doppler Lidar: Sensitivity to
Averaging Procedures, J. Atmos. Ocean. Tech., 25, 1307–1327,
10.1175/2008JTECHA988.1, 2008.Reineman, B. D., Lenain, L., Statom, N. M., and Melville, W. K.: Development
and Testing of Instrumentation for UAV-Based Flux Measurements within
Terrestrial and Marine Atmospheric Boundary Layers, J. Atmos. Ocean. Tech.,
30, 1295–1319, 10.1175/JTECH-D-12-00176.1, 2013.Reuder, J., Brisset, P., Jonassen, M., Müller, M., and Mayer, S.: The
Small Unmanned Meteorological Observer SUMO: A new tool for atmospheric
boundary layer research, Meteorol. Z., 18, 141–147,
10.1127/0941-2948/2009/0363, 2009.Reuder, J., Jonassen, M., and Ólafsson, H.: The Small Unmanned
Meteorological Observer SUMO: Recent developments and applications of a
micro-UAS for atmospheric boundary layer research, Acta Geophys., 60,
1454–1473, 10.2478/s11600-012-0042-8, 2012.Reuder, J., Båserud, L., Jonassen, M. O., Kral, S. T., and Müller,
M.: Exploring the potential of the RPA system SUMO for multipurpose
boundary-layer missions during the BLLAST campaign, Atmos. Meas. Tech., 9,
2675–2688, 10.5194/amt-9-2675-2016, 2016.Sathe, A. and Mann, J.: A review of turbulence measurements using
ground-based wind lidars, Atmos. Meas. Tech., 6, 3147–3167,
10.5194/amt-6-3147-2013, 2013.Sathe, A., Mann, J., Gottschall, J., and Courtney, M. S.: Can Wind Lidars
Measure Turbulence?, J. Atmos. Ocean. Technol., 28, 853–868,
10.1175/JTECH-D-10-05004.1, 2011.Seibert, P. and Langer, M.: Deriving characteristic parameters of the
convective boundary layer from sodar measurements of the vertical velocity
variance, Bound.-Lay. Meteorol., 81, 11–22, 10.1007/BF00119396, 1996.Sjöholm, M., Mikkelsen, T., Mann, J., Enevoldsen, K., and Courtney, M.:
Spatial averaging-effects on turbulence measured by a continuous-wave
coherent lidar, Meteorol. Z., 18, 281–287,
10.1127/0941-2948/2009/0379, 2009.Stevens, W. R., Squier, W., Mitchell, W., Gullett, B. K., and Pressley, C.:
Measurement of motion corrected wind velocity using an aerostat lofted sonic
anemometer, Atmos. Meas. Tech. Discuss., 6, 703–720,
10.5194/amtd-6-703-2013, 2013.
Stull, R.: An Introduction to Boundary Layer Meteorology, Springer, 670 pp.,
1988.Taylor, G. I.: The Spectrum of Turbulence, P. Roy. Soc. A-Math. Phy., 164,
476–490, 10.1098/rspa.1938.0032, 1938.Thomas, P. and Vogt, S.: Intercomparison of turbulence data measured by
SODAR and sonic anemometers, Bound.-Lay. Meteorol., 62, 353–359,
10.1007/BF00705564, 1993.Thomas, R. M., Lehmann, K., Nguyen, H., Jackson, D. L., Wolfe, D., and
Ramanathan, V.: Measurement of turbulent water vapor fluxes using a
lightweight unmanned aerial vehicle system, Atmos. Meas. Tech., 5, 243–257,
10.5194/amt-5-243-2012, 2012.van den Kroonenberg, A., Martin, T., Buschmann, M., Bange, J., and
Vörsmann, P.: Measuring the Wind Vector Using the Autonomous Mini
Aerial Vehicle M2AV, J. Atmos. Ocean. Tech., 25, 1969–1982,
10.1175/2008JTECHA1114.1, 2008.van den Kroonenberg, A. C., Martin, S., Beyrich, F., and Bange, J.:
Spatially-Averaged Temperature Structure Parameter Over a Heterogeneous
Surface Measured by an Unmanned Aerial Vehicle, Bound.-Lay. Meteorol., 142,
55–77, 10.1007/s10546-011-9662-9, 2012.Wildmann, N., Mauz, M., and Bange, J.: Two fast temperature sensors for
probing of the atmospheric boundary layer using small remotely piloted
aircraft (RPA), Atmos. Meas. Tech., 6, 2101–2113,
10.5194/amt-6-2101-2013, 2013.Wildmann, N., Ravi, S., and Bange, J.: Towards higher accuracy and better
frequency response with standard multi-hole probes in turbulence measurement
with remotely piloted aircraft (RPA), Atmos. Meas. Tech., 7, 1027–1041,
10.5194/amt-7-1027-2014, 2014.Wildmann, N., Rau, G. A., and Bange, J.: Observations of the Early Morning
Boundary-Layer Transition with Small Remotely-Piloted Aircraft, Bound.-Lay.
Meteorol., 157, 345–373, 10.1007/s10546-015-0059-z, 2015.