Aeolus, launched on 22 August in 2018, is the
first ever satellite to directly observe wind information from the surface
up to 30 km on a global scale. An airborne prototype instrument called ALADIN airborne
demonstrator (A2D) was developed at the German Aerospace Center (DLR) for
validating the Aeolus measurement principle based on realistic atmospheric
signals. To obtain accurate wind retrievals, the A2D uses a measured
Rayleigh response calibration (MRRC) to calibrate its Rayleigh channel
signals. However, differences exist between the respective atmospheric
temperature profiles that are present during the conduction of the MRRC and
the actual wind measurements. These differences are an important source of
wind bias since the atmospheric temperature has a direct effect on the
instrument response calibration. Furthermore, some experimental limitations
and requirements need to be considered carefully to achieve a reliable MRRC.
The atmospheric and instrumental variability thus currently limit the
reliability and repeatability of a MRRC. In this paper, a procedure for a
simulated Rayleigh response calibration (SRRC) is developed and presented in
order to resolve these limitations of the A2D MRRC. At first the
transmission functions of the A2D Rayleigh channel double-edge Fabry–Pérot
interferometers (FPIs) in the internal reference path and the atmospheric
path are characterized and optimized based on measurements performed during
different airborne and ground-based campaigns. The optimized FPI
transmission functions are then combined with the laser reference spectrum
and the temperature-dependent molecular Rayleigh backscatter spectrum to
derive an accurate A2D SRRC which can finally be implemented into the wind
retrieval. Using dropsonde data as a reference, a statistical analysis based
on a dataset from a flight campaign in 2016 reveals a bias and a standard
deviation of line-of-sight (LOS) wind speeds derived from a SRRC of only
0.05 and 2.52 m s-1, respectively. Compared to the result
derived from a MRRC with a bias of 0.23 m s-1 and a standard deviation
of 2.20 m s-1, the accuracy improved and the precision is considered
to be at the same level. Furthermore, it is shown that the SRRC allows for the
simulation of receiver responses over the whole altitude range from the
aircraft down to sea level, thus overcoming limitations due to high ground
elevation during the acquisition of an airborne instrument response
calibration.
Introduction
Continuous global wind observations are of highest priority for improving
the accuracy of numerical weather prediction as well as for advancing our
knowledge of atmospheric dynamics (Stoffelen et al., 2005; Weissmann et al.,
2007; Žagar et al., 2008; Baker et al., 2014). Among various techniques
such as radiosonde, radar wind profiler, and geostationary satellite
imagery, a spaceborne Doppler wind lidar is considered the most promising
one to meet the need of near-real-time observations of global wind
information. Based on the principle of the Doppler effect, two different
wind lidar detection techniques, namely coherent and direct detection, have
been developed and studied over the last decades (Reitebuch, 2012a). The
coherent Doppler lidar (CDL), typically used in the particle-rich boundary
layer, can directly determine the Doppler frequency shift via the beat
signal between the emitted laser signal and the particulate backscattered
light, and the frequency shift introduced by an acoustic-optical modulator
enables the measurement of positive and negative winds. In contrast, for a
direct-detection wind lidar, the measured signal cannot directly be related
to the frequency shift. Thus, a so-called response calibration describing
the relationship between the measured instrument response and the actual
Doppler frequency shift constitutes a prerequisite for an accurate wind
retrieval. A direct-detection wind lidar can measure atmospheric wind by
means of either particulate or molecular backscatter signals, typically
offering much higher data coverage of the wind field from ground up to the
lower mesosphere. Different spectral discriminators such as Fabry–Pérot
interferometers (Chanin et al., 1989; Korb et al., 1992), Fizeau
interferometers (McKay, 1998, 2002), iodine vapor filters (Liu et
al., 2002; She et al., 2007; Baumgarten, 2010; Wang et al., 2010; Hildebrand
et al., 2012), Michelson interferometers (Thuillier and Hersé, 1991; Herbst and Vrancken, 2016) and Mach–Zehnder interferometers (Bruneau, 2001; Bruneau and
Pelon, 2003; Tucker et al., 2018) can be used for direct-detection wind
lidars.
Aeolus, launched on 22 August 2018, is the first ever satellite to
directly observe line-of-sight (LOS) wind profiles on a global scale. Its
unique payload, the Atmospheric LAser Doppler INstrument (ALADIN), is a
direct-detection wind lidar operating at 355 nm from a 320 km orbit
(Stoffelen et al., 2005; ESA, 2008; Reitebuch, 2012b). The particulate and molecular backscatter signals are received by two
different spectrometers, which are a Fizeau interferometer in the Mie
channel, measuring particulate backscatter, and a double-edge filter with
two Fabry–Pérot interferometers (FPIs) in the Rayleigh channel, measuring
molecular backscatter. The novel combination of these two techniques,
integrated for the first time into a single wind lidar, expands the
observable altitude range from ground to the lowermost 30 km of the
atmosphere. ALADIN provides one component of the wind vector along the
instrument LOS with a vertical resolution of 0.25 to 2 km and with a
requirement on the wind speed precision of 1 to 2.5 m s-1
for the horizontally projected LOS (HLOS), depending on altitude.
Furthermore, as the first high spectral resolution lidar in space (Ansmann
et al., 2007; Flamant et al., 2008), ALADIN has the potential to globally
monitor cloud and aerosol optical properties to contribute to climate
impact studies.
In the frame of the Aeolus program, a prototype instrument called ALADIN
airborne demonstrator (A2D) was developed at the German Aerospace Center
(DLR). Due to its representative design and operating principle, the A2D has
provided valuable information on the validation of the measurement principle
from real atmospheric signals before the satellite launch. In addition, the
A2D is expected to contribute to the optimization of the wind measurement
strategies for the satellite instrument as well as to the improvement of
wind retrieval and quality control algorithms during satellite operation
(Durand et al., 2006; Reitebuch et al., 2009; Paffrath et al., 2009). As the
first ever airborne direct-detection wind lidar, A2D has been deployed in
several ground and airborne campaigns over the last 12 years (Li et al.,
2010; Marksteiner, 2013; Weiler, 2017; Lux et al., 2018; Marksteiner et al.,
2018).
Different instrument response calibration approaches have been studied using
both measured and simulated response calibration to characterize and
calibrate the ALADIN Rayleigh channel (Tan et al., 2008; Dabas et al., 2008;
Rennie et al., 2017). Currently, only measured Rayleigh response
calibrations (MRRC) are used for the A2D (Marksteiner, 2013; Lux et al.,
2018; Marksteiner et al., 2018). However, the atmospheric temperature
affects the Rayleigh–Brillouin line shape and has a direct effect on the
instrument response calibration (Dabas et al., 2008). Differences exist
between the respective atmospheric temperature profiles that are present
during the conduction of the MRRC and the actual wind measurements. These
differences are an important source of wind bias, which grows with increasing
temperature differences. This is also the reason why it is mandatory to
consider the atmospheric temperature in the Aeolus level 2B procedure to
retrieve reliable winds (Dabas et al., 2008; Rennie et al., 2017).
Furthermore, some experimental limitations, which will be introduced
specifically in Sect. 2.1, need to be considered carefully to achieve a
reliable MRRC. Overall, the atmospheric and instrumental variability coming
along with MRRC limits the reliability and repeatability of the A2D instrument
response calibrations. Inspired by the calibration method used in the ALADIN
level 2B processor (Dabas and Huber, 2017), the simulated Rayleigh response
calibration (SRRC) was developed to resolve these limitations of A2D. It is
based on an accurate theoretical model of the FPI transmission function and
the molecular Rayleigh backscatter spectrum. In this paper, the SRRC is
introduced and its impact on the A2D wind retrieval is discussed and
compared to results obtained with a measured response calibration.
In Sect. 2, different calibration approaches of double-edge FPIs are
discussed firstly. Afterwards, the principle of an A2D SRRC is presented in
Sect. 3. Section 4 gives an overview of the campaign and the dataset
analyzed in this paper, whereas Sect. 5 introduces the A2D SRRC, which is
applied to the campaign measurements, and discusses the corresponding wind
results. Section 6 provides a statistical comparison of LOS wind velocities
from A2D Rayleigh channel measurements, using the MRRC and SRRC, and winds
from simultaneous CDL and dropsonde datasets. A comparison of A2D MRRCs and
SRRCs is also evaluated in Sect. 6. Section 7 provides a summary and
conclusion.
Comparison of different FPI-based direct-detection wind lidars.
LidarWavelength andCalibrationInstrument driftReferencessystemapproachvia correctionOHPa Rayleigh532 nm, doubleSimulation,quick windChanin et al. (1989);lidarFPIsFPI scanacquisition cycleGarnier and Chanin (1992);strategySouprayen et al. (1999a, b)NASAb355 nm, three FPIsSimulation,locking etalon andKorb et al. (1992, 1998);Rayleigh/MieFPI or laserservo-controlFlesia and Korb (1999);lidarfrequency scansystemFlesia et al. (2000);Gentry et al. (2000)USTCc355 nm, three FPIsmeasurementlocking etalon andXia et al. (2012);Rayleigh lidarand simulation,servo-controlDou et al. (2014)FPI scansystemESA355 nm, doublelevel 1B: scanninginternal referenceReitebuch et al. (2018);ALADINFPIs for Rayleighmeasurement, laserpathRennie et al. (2017)channellevel 2B: simulation,laser frequency scanDLR 355 nm, doubleMeasurement, internal referenceMarksteiner (2013);A2DFPIs for Rayleighlaser frequencypathLux et al. (2018);channelscanMarksteiner et al. (2018)
a Observatory of Haute Provence, France.
b National Aeronautics and Space Administration, USA.
c University of Science and Technology of China, China. This lidar is
mobile.
Calibration approaches for double-edge FPIs
Chanin et al. (1989) demonstrated for the first time that FPIs can be used
to measure wind in the middle atmosphere relying on molecular Rayleigh
scattering and a laser with a wavelength of 532 nm. The so-called response
can be defined as the contrast (Chanin et al., 1989) or the ratio (Korb et
al., 1992) of the signal intensities obtained after transmission through the
FPIs. A response calibration is a prerequisite for wind retrieval since it
represents the relationship between the measured quantity (e.g., intensity of
the backscattered light) and the frequency shift which is induced by the
Doppler effect. Generally, there are two approaches to determine the
relationship between response and Doppler frequency shift, i.e., to obtain a
response calibration function. Table 1 lists several FPI-based direct-detection wind lidar systems that are capable of measuring wind information
based on a measurement approach or a simulation approach.
For each direct-detection wind lidar system, the emitted laser frequency
should be known in order to allow for an accurate derivation of the Doppler
frequency shift. A zero Doppler shift reference determined by pointing to
the zenith direction has been used to correct the short-term frequency drift
in previous studies (Souprayen et al., 1999b; Korb et al., 1992; Dou et al.,
2014). But for the A2D, the internal reference path is particularly
dedicated to the derivation of information about the emitted laser
frequency. As shown in Lux et al. (2018, Fig. 1), a small portion of the
laser beam radiation is collected by an integrating sphere and coupled into
a multimode fiber, then it is injected into the receiver via the front optics.
This path is called the internal reference path. The atmospheric backscattered
signal is collected by a Cassegrain telescope and guided via free optical
path propagation to the front optics and receiver successively. This path is
called the atmospheric path. An electro-optical modulator is used to
temporally separate the atmospheric signal from the internal reference
signal, thereby avoiding disturbances to the internal reference signal by
atmospheric signal and saturation of the detectors at short ranges
(Reitebuch et al., 2009). Because of the different optical illumination of
the internal and atmospheric path resulting in different divergence and
incidence angles on the FPIs, the response calibration curves for these two
paths are different. It is noted that the internal reference path of ALADIN
is different from A2D's path, where ALADIN uses free path propagation rather than a
fiber coupling unit.
Approach using measured response calibrations
The first approach to obtain a response calibration function is based on
measurements during which the laser beam is pointed into zenith direction
while assuming that the vertical velocity of the probed atmospheric volume
is negligible, i.e., no Doppler frequency shift is induced. Then, either the
frequency of the laser transmitter is scanned with a constant FPI cavity
length (Reitebuch et al., 2018; Lux et al., 2018; Marksteiner et al., 2018)
or the cavity length of the FPIs is scanned while keeping the laser
frequency locked (Dou et al., 2014).
Since the shape of the actual molecular Rayleigh backscatter spectrum is
determined by the atmospheric temperature and pressure profiles (Tenti et
al., 1974; Pan et al., 2004), the measured response calibration function in
the atmospheric path is only valid for a specific combination of temperature
and pressure profiles. Regarding ground-based lidar systems, the calibration
procedure can be carried out frequently. Based on stable atmospheric
conditions (Dou et al., 2014; Liu et al., 2002) it is reasonable to assume
that only small temperature and pressure variations occur with a negligible
effect on the retrieved wind within a specific analysis period. However, for
spaceborne or airborne lidar systems like ALADIN or the A2D, the variability
in temperature and pressure can be one of the main sources of systematic
errors for the Rayleigh channel wind retrieval as it modifies the instrument
response calibration (Dabas et al., 2008; Marksteiner, 2013).
For ALADIN, the Rayleigh winds produced by the level 1B processor (Reitebuch
et al., 2018) are based on a MRRC, while the level 2B processor uses a SRRC.
A MRRC includes three response calibration curves, one each derived from the
internal reference, the atmospheric response, and the ground return. A so-called
instrument response calibration mode is usually performed once per week.
During about 16 min the frequency of the laser transmitter is scanned
over 1000 MHz in steps of 25 MHz, and the satellite is rolled by
35∘ in order to point nadir, thereby avoiding frequency shifts
induced by horizontal wind velocities. In order to increase the signal-to-noise ratio (SNR), the signals generally from the altitude range between 6
and 20 km are accumulated to derive a single response calibration curve
for the atmosphere (Reitebuch et al., 2018). Compared to ALADIN, the MRRC of
the A2D can be derived and used per range gate because of the larger SNR
prevailing for airborne measurements, which are performed closer to their
target. The instrument response calibration of the A2D can be carried out
several times during a flight by tuning the laser frequency in steps of 25 MHz over a frequency interval of 1.7 GHz.
Apart from the atmospheric temperature and pressure effects on the MRRC,
several specific experimental constraints are critical for achieving a
reliable instrument response calibration for both ALADIN and A2D. Firstly,
the particulate Mie scattering, which is not fully filtered out by the Fizeau
interferometer, will enter the FPIs and can be considered Mie
contamination of the Rayleigh signal. Because of the different spectral
widths of the particle and molecular backscatter signals, the sensitivities
of the FPIs on them are different. If not taken into account, the Mie
contamination on the Rayleigh channel is one of the sources of systematic
errors because it modifies the MRRC curve. In order to avoid such
modifications, the A2D tries to conduct instrument response calibrations in a preferably pure Rayleigh
atmosphere. Furthermore, the characteristics of the ground, such as high
albedo and preferably flat terrain, and low ground elevation, should
be considered to improve the SNR, to facilitate the deduction of a ground
return response curve and to maximize the vertical coverage of the
atmosphere (Marksteiner, 2013; Weiler, 2017; Lux et al., 2018; Marksteiner
et al., 2018). In some cases, A2D calibrations were performed over terrain
with high elevation (e.g., Greenland). Obviously, no response calibration
curve can be obtained from below the surface, which would, however, be
necessary for accurate wind retrieval at other geographical locations with
lower ground elevation. In addition, the LOS velocity needs to be zero
during the instrument response calibration. This is accomplished by flying
curves with a roll angle of 20∘, which corresponds to the
installation angle of the A2D telescope in the DLR Falcon 20 aircraft.
Regions showing gravity wave activity or strong convection are avoided as
they cross the assumption of negligible vertical wind velocity (Lux et al.,
2018; Marksteiner et al., 2018). Overall, the reliability and repeatability
of A2D MRRCs is a main limitation for accurate wind retrieval.
Approach using simulated response calibrations
The second approach is based on SRRC curves and the fact that the
transmitted signals through each FPI are proportional to the convolution of
the respective filter transmission function with the atmospheric backscatter
spectrum. Therefore, this approach relies on accurate models for both FPI
transmission functions and atmospheric backscatter spectrum. In practice,
the transmission function of FPIs can be obtained by scanning the laser
frequency and keeping the FPI's etalon length fixed (Rennie
et al., 2017) or by scanning the spacing between the plates of FPIs with a fixed
laser frequency (Souprayen et al., 1999b; Xia et al., 2012).
For ALADIN and A2D, the seed laser is frequency tuneable to cover a spectral
range of 11 GHz in the UV to calibrate the spectral characteristics of FPIs
for the internal reference path. This procedure is called instrument
spectral registration (Reitebuch et al., 2018). However, the transmission
functions of FPIs for the atmospheric path are different from the
transmission curves registered on the internal reference path during the
instrument spectral registration. This is because of the difference in the
illumination of the FPIs by the beams in the atmospheric and the internal
reference paths, i.e., due to different divergence and incidence angles
(Reitebuch et al., 2009). For ALADIN, this is taken into account by
correcting the FPI transmission curves of the atmospheric path (Dabas and
Huber, 2017). Regarding the A2D, a SRRC based on such a simulation approach
promises an improvement in terms of wind speed errors. A SRRC includes two
response calibration curves derived from internal reference path and
atmospheric path. The transmission function of the A2D FPIs in the internal
reference path can be obtained during an instrument spectral registration.
The determination of the transmission functions of the FPIs in the
atmospheric path of the A2D is the most sophisticated part needed to
accurately retrieve wind information by using a SRRC. Furthermore, FPI
transmission functions should be a function of incidence angles, field of
view, temperature, pressure, thickness, fitness, etc. Regardless of
measurement or simulation method, any angular alignment drift will change
the incidence angles on the FPIs, resulting in a different transmission
value. Referring to the Observatory of Haute Provence (OHP) Rayleigh lidar,
the bias induced by instrument drifts can be eliminated by a specific
wind acquisition cycle strategy using the differences between vertical and
titled position measurement (Souprayen et al., 1999a). For ALADIN or the
A2D, the instrument drift is compensated by regularly performing instrument
response calibrations and instrument spectral registrations on a weekly
basis.
The principle of A2D SRRC
The Doppler frequency shift in LOS direction is derived from the difference
between the frequency of the received atmospheric return fa and the
emitted laser frequency fi:
Δf=fa-fi.
The corresponding LOS velocity is derived from the Doppler shift equation
using a laser wavelength of λ0:
VLOS=λ02Δf.
In order to derive fi and fa from the A2D Rayleigh channel, the
transmitted intensities IA,B,INT(f) and IA,B,ATM(f) through the
FPI filters A and B are used for the internal reference path (INT) and the
atmospheric path (ATM), respectively:
3IA,B,INTfi=∫-∞+∞TA,B,INT(f)Si(fi-f)df,4IA,B,ATMfa=∫-∞+∞TA,B,ATM(f)Sa(fa-f)df,5Saf=SRBf+(ρ-1)SMie(f).
Taking the transmitted intensity through filter A for instance, IA,INT(fi) is the convolution of the filter A transmission function on the
internal reference path (TA,INT(f)) and the normalized laser reference
spectrum Si(f) with the transmitted laser frequency fi.
Accordingly, IA,ATM(fa) is the convolution of the filter A
transmission function on the atmospheric path (TA,ATM(f)) and the
normalized atmospheric backscatter signal spectrum Sa(f) with the
center frequency fa; Sa(f) consists of the broad molecular
Rayleigh backscatter spectrum SRB(f) (the subscript RB stands for
Rayleigh–Brillouin) and the narrow particulate Mie backscatter spectrum
SMie(f), as shown in Eq. (5). Here ρ=1+βaer/βmol is the scattering ratio, where βaer and βmol are the
particle and molecular backscatter coefficients, respectively.
As described by Garnier and Chanin (1992), the Rayleigh response is defined
as
Rxf=IA,xf-IB,xfIA,xf+IB,xf,x=INTorATM,
where x represents the case of the INT or ATM path.
In order to determine the Rx(f) by means of Eqs. (3)–(6), accurate
knowledge about TA,B,INT(f), TA,B,ATM(f), Si(f) and Sa(f) is needed. Generally, the transmission function T(f) of an ideal FPI
can be expressed by the Airy function. However, small defects on the FPI
mirror surfaces or imperfect illumination of the FPI could result in small
deviations that have to be considered (McGill and Spinhirne, 1998). It is shown
that all these defects can be represented by a Gaussian defect term that
modifies the model of the FPI transmission function T(f) to the following (Witschas et
al., 2012):
Tf=1FSR1+2∑k=1∞Rkcos2πkfFSRexp-2π2k2σg2FSR2,
where FSR is the free spectral range of the corresponding FPI (A or B) on
the respective measurement path (INT or ATM), R is the mean
reflectivity of the mirror surfaces, and σg is a defect parameter
taking mirror defects into consideration.
Modeled spectral distribution of the transmitted laser pulse
(pink line) and pure molecular backscatter (blue line) for T=270 K and
P=700 hPa (normalized to one). The Rayleigh channel transmission spectra of
two FPIs are shown by black (TA (f)) and red (TB (f)) lines.
The transmitted, integrated intensities through FPI A and B are marked with
light-blue and magenta filled areas, respectively.
The laser pulse line shape Si(f) , with its laser linewidth and
emitted laser frequency (Lux et al., 2018; Marksteiner et al., 2018), can be
approximated by a Gaussian function. The spectral distribution of SMie(f) is similar to Si(f) as particles can be considered to cause no
significant spectral broadening due to random motion. SRB(f) can be
computed by using the Tenti S6 line shape model (Tenti et al., 1974; Pan et
al., 2004), which has been widely applied in atmospheric applications. An
easily calculated analytical expression of the Tenti S6 line shape model for
atmospherically relevant temperatures and pressures is used herein
(Witschas, 2011a, b; Witschas et al., 2014).
The measurement principle of the A2D Rayleigh channel signal is shown in
Fig. 1 as an example for one frequency step during the instrument spectral
calibration with no Doppler shift on the LOS. It is assumed that there is no
Mie contamination on the Rayleigh channel in this case; that is, ρ=1 or Sa(f)=SRB(f); Si(f) is depicted using a
Gaussian function with a full width at half maximum (FWHM) of 50 MHz.
SRB(f) is calculated for T=270 K and P=700 hPa. The transmitted
integrated intensities of Sa(f) through FPIs A and B, i.e.,
IA,ATM and IB,ATM, are indicated by light-blue and light-magenta
filled areas, respectively.
Simulation of LOS wind velocity errors ΔVMC induced by
Mie contamination and a molecular line shape at T=223 K and P=301 hPa.
The x axis and y axis represent the response value RATM and scattering
ratio ρ, respectively. The dashed red line corresponds to the
response value with minimum ΔVMC at each scattering ratio.
The LOS wind velocity error ΔVMC induced by Mie contamination is
defined as the difference in the LOS wind velocities measured under purely
atmospheric molecular conditions and conditions with a scattering ratio of
ρ. Figure 2 shows a simulation of ΔVMC at T=223 K and
P=301 hPa, where the x axis and y axis represent different response values
and scattering ratios, respectively. Positive and negative ΔVMC
represent the overestimation and underestimation of the LOS velocity,
respectively. An overestimation of LOS velocities occurs at response values
less than 0.235 in this case. Larger scattering ratios result in larger
overestimation, and the difference can get up to 13 m s-1 in the case of
ρ=3. According to previous studies (Dabas et al., 2008), the Mie
contamination correction could improve the quality of Rayleigh winds in
the cases of intermediate ρ, e.g., below 1.5. In this region the Mie signal
is not high enough to guarantee an accurate Mie wind measurement but instead
becomes rather significant for the Rayleigh channel (Sun et al., 2014; Lux
et al., 2018). The value of ρ, which is needed for the Mie
contamination correction in the Rayleigh channel, is obtained by analyzing
the Mie channel signal. The detailed algorithm can be seen in Flamant et
al. (2017).
(a) Simulated Rayleigh response calibration (SRRC) for internal
reference (INT, blue line) and atmospheric return (ATM, black line); the
cross point frequency is marked by the dotted red line; (b) INT (blue dots) and
ATM (black dots) response functions and corresponding linear least squares
fits (blue line for INT, black line for ATM) over a frequency interval of
±850 MHz, where relative frequency is used instead of absolute
frequency; (c) simulated nonlinearities (dots) and fifth-order
polynomial fits for INT (blue line) and ATM (black line). (d) Response
function residuals from INT (blue line) and ATM (black line).
Following the procedure of the A2D instrument response calibration mode, the
intensities transmitted through the FPIs and corresponding response values
at each frequency step are calculated, eventually forming the SRRC of the
internal reference path (RINT(f), blue line) and the atmospheric path
(RATM(f), black line) shown in Fig. 3a. It is noted that the
procedure is done assuming no Mie contamination in this case. The cross
point frequency fc (red dotted line) in Fig. 3a is derived from
RINT(f), where IA,i(f)-IB,i(f) is closest to zero
(Marksteiner et al., 2018). The relative frequency f′ is defined as the
difference between absolute frequency f and fc. Figure 3b shows
the simulated response functions RINT(f′) and RATM(f′)
within a relative frequency interval of ±850 MHz, where the
interval corresponds to the area marked by the dashed red square in Fig. 3a. A linear least-squares fit, Rlinearfit_x(f′), is
applied to the SRRC of the internal reference and atmospheric path, shown by
the solid blue and black lines in Fig. 3b. The linear fitting parameters
sensitivity, βx, and intercept, αx, are defined as
8βx=∂Rlinearfit_x(f′)∂f′,x=INT or ATM;9αx=Rlinearfit_xf′=0=Rlinearfit_xf=fc.
The nonlinearity γx(f′) is defined as the difference between
Rx(f′) and linear least-squares fit Rlinearfit_x(f′); that is, γx(f′)=Rx(f′)-(βxf′+αx). The different γx(f′) functions of the
internal reference path and the atmospheric path are shown in Fig. 3c.
For a wavelength of λ0=354.89 nm, a LOS velocity of 1 m s-1
translates into a frequency shift of 5.63 MHz. Taking a sensitivity βATM=5×10-4 MHz-1, the atmospheric
nonlinearity at -200 MHz almost reaches -0.02, which is equivalent to about
-40 MHz, which in turn corresponds to -7.1 m s-1. Consequently, large
errors in the derived LOS velocity would occur if γx(f′) is
not taken into account. Therefore, a fifth-order polynomial fit
(Marksteiner, 2013; Lux et al., 2018; Marksteiner et al., 2018) is selected
to model γx(f′), as shown in Fig. 3c for RINT(f′)
(RATM(f′)) as solid blue (black) line. A fit of the SRRC for the
internal reference and atmospheric paths can be expressed as a sum of a
linear fit and a fifth-order polynomial fit:
Rfit,xf′=βxf′+αx+γ,xf′=βxf′+αx+∑i=05mi,xf′i=ax+m0,x+βx+m1,xf′+m2,xf′2+m3,xf′3+m4,xf′4+m5,xf′5.
The difference between Rx(f′) and Rfit,x(f′) is defined as
residual and shown in Fig. 3d for the internal reference path (blue line)
and the atmospheric path (black line), respectively. A periodic fluctuation
can be seen, but the maximum residual of the atmospheric path is less than
1.5×10-4, corresponding to 0.053 m s-1
for βATM=5×10-4 MHz-1. The
absolute difference between the two residuals (INT minus ATM) is even smaller.
Overview of analyzed datasets from A2D, 2 µm CDL, and dropsondes
in the frame of the North Atlantic Waveguide and Downstream Experiment (NAWDEX) campaign.
DateA2D measurement period (UTC) and modeData availability of CDLMatched dropsonde time (UTC)17.09.201610:30–11:35available11:09:15Wind measurement11:33:4711:42–12:24no data11:56:00Wind measurement12:05:2012:15:0212:24:2321.09.201615:34–15:57available15:40:49Wind measurement15:45:0715:48:3415:52:5123.09.201607:51–08:53available08:19:01Wind measurement08:27:0708:33:0608:39:0508:45:0508:51:1628.09.201612:53–13:17availableno dataCalibration18.10.201609:20–09:57not available09:22:48Wind measurement09:27:1509:31:5309:36:2909:52:30Campaign and dataset
As part of the North Atlantic Waveguide and Downstream Experiment (NAWDEX) campaign
carried out in 2016 in Iceland, four aircraft equipped with diverse payloads
were employed to investigate the influence of diabatic processes for
midlatitude weather (Schäfler et al., 2018). The DLR Falcon 20 was
deployed with the A2D and a well-established 2 µm CDL, offering an
ideal platform to demonstrate the capabilities of the A2D under complex
dynamic conditions. A total of 14 research flights were performed with the
Falcon aircraft during the NAWDEX campaign. The A2D was operated in wind
measurement mode in most of the flight periods, while the instrument
spectral registration mode was carried out on the ground and during airborne
measurements. Furthermore, two flights on 28 September and
15 October 2016 were carried out to obtain A2D instrument response
calibrations. Six MRRCs have been performed in these two calibration flight
periods. After comparison and evaluation given by Lux et al. (2018), the
third calibration, which was carried out over an Iceland glacier on
28 September 2016 at 12:53 UTC, is chosen as the baseline of the A2D
Rayleigh wind retrieval, as it shows low Rayleigh residual errors and was
not affected by clouds, instrument temperature drifts, or outliers (Lux et
al., 2018). The other three aircraft – i.e., the German High Altitude and
Long Range Research Aircraft (HALO) (Gulfstream G 550), the French Service des Avions
Français Instrumentés pour la Recherche en Environnement (SAFIRE)
(Falcon 20), and the British Facility for Airborne Atmospheric Measurements
(FAAM) (BAe-146) – were equipped with dropsonde dispensers to provide
temperature, pressure, wind, and humidity profiles (Schäfler et al.,
2018). Time-space matching datasets between dropsonde and A2D can be used as
both references to validate A2D wind measurements and to provide essential
atmospheric temperature and pressure profiles for SRRC in this study. Table 2 provides an overview of datasets that are available from the 2016 flight
campaign and are used for this study. It is noted that all matched
dropsondes listed in Table 2 were dispensed from the HALO aircraft.
The transmission functions of the FPIs are reproducible, and the
transmission characteristics are different for the internal reference and
atmospheric paths. The underlying difference in illumination includes both a
difference in the spatial distribution and in the angular distribution of the
light. In particular, the use of a multimode fiber in the internal reference
path gives rise to speckles, resulting in an intensity distribution which is
markedly different from that of the atmospheric path. As for the A2D instrument
spectral registration during the NAWDEX campaign, the sampled transmission
functions of the FPIs are obtained from the internal reference only since
the atmospheric return is convolved with a temperature-dependent Rayleigh backscatter spectrum,
and the hard target ground return would be too variable due to albedo
variation. The only sampled transmission functions of the FPIs from the A2D
atmospheric path are available from the BRillouin scattering Atmospheric
INvestigation on Schneefernerhaus (BRAINS) field campaign (Witschas, 2011c;
Witschas et al., 2012), which was performed during January–February 2009 to
demonstrate the effect of Brillouin scattering in real atmosphere. Unique to
BRAINS was a horizontal pointing of the outgoing laser beam in order to get
a hard target return of a mountain with constant albedo in about 10 km
distance. This allowed measurements of narrowband backscatter signal through
the atmospheric path. The transmission functions of the FPIs were sampled by
changing the laser frequency with steps of 50 MHz over a frequency range of
12 GHz with fixed FPIs. Here, different transmission curves of FPIs from the
BRAINS field campaign in 2009 and NAWDEX airborne campaign in 2016 will be
used as candidate FPI transmission curves for SRRC analysis.
Flowchart of LOS velocity retrieval and comparison between A2D
SRRC and MRRC.
Determination of the A2D response function and Rayleigh wind retrieval
A flowchart of the LOS wind velocity retrieval based on SRRC and MRRC is
presented in Fig. 4. Firstly, the atmospheric temperature and pressure
profiles are taken from dropsonde, radiosonde, or model data to derive the
atmospheric molecular backscattered spectrum using the analytical
representation of the Tenti S6 line shape model (Witschas, 2011a, b; Witschas et
al., 2014). Then the transmission functions of FPIs are obtained by fitting
the measured FPI transmission characteristics based on Eq. (7). Afterwards
the frequency scan of the laser transmitter during A2D instrument response
calibration is simulated to derive the SRRCs for the internal reference and
the atmospheric path. The measured response values, RATM and RINT,
obtained from the A2D wind velocity measurement mode are combined with the SRRC
Rfit,ATM(f′) and Rfit,INT(f′). The Doppler frequency shift,
ΔfSRRC, due to LOS velocity is then derived from the difference
of fa,SRRC′ and fi,SRRC′ (Reitebuch et al., 2018):
ΔfSRRC=fa,SRRC′-fi,SRRC′=RATM-αATM-γfit,ATMfa,SRRC′βATM-RINT-αINT-γfit,INT(fi,SRRC′)βINT.
The LOS velocity VLOS,SRRC is derived according to Eq. (2):
VLOS,SRRC=λ02ΔfSRRC.
It is noted that LOS velocity herein includes not only the horizontal and a
possible vertical wind component but also the contribution from the aircraft
flight velocity. The correction of the flight-induced velocity,
VLOS,aircraft, is calculated using the inertial navigation system and
GPS on board the aircraft within an attitude correction algorithm
(Marksteiner, 2013). Finally, the corrected LOS wind velocity, Vcor,SRRC, is obtained as follows:
Vcor,SRRC=VLOS,SRRC-VLOS,aircraft.
Transmission characteristics of FPIs from different campaigns
A least-squares nonlinear procedure is applied to each sampled transmission
function obtained from the BRAINS field campaign in 2009 and NAWDEX airborne
campaign in 2016. Figure 5 illustrates the fits of the transmission
functions where the intensities are normalized to the maximum of filter A.
The black curves are derived from ground-based atmospheric path (ATMG)
measurements during the BRAINS field campaign in 2009. The red and blue
curves represent the ground-based (INTG) and airborne internal reference
path (INTA) measurements obtained from the NAWDEX campaign in 2016,
respectively. The specific parameters of FPIs are listed in Table 3. The
difference between ATMG and INTG is due to the different illumination of the
FPI via the atmospheric and internal reference paths. Obviously the FWHM of
INTA is broader than that of INTG, which is most likely due to small
contamination by atmospheric signal not completely blocked within the A2D
optical receiver. Specifically, the atmospheric contamination of the
internal reference signal of INTA is caused by the limited suppression
efficiency of the electro-optical modulator incorporated in the A2D front
optics. This leads to a leakage of atmospheric backscatter being incident on
the Rayleigh accumulated charge coupled device (ACCD), during the
acquisition time of the internal reference signal. Please note that the
internal path signal is recorded with the same ACCD detector as the
atmospheric path signal using an integration time of 4.2 µs. For the
internal calibration INTG that was performed on the ground, the atmospheric path
was blocked manually in front of the receiver, which completely avoided
atmospheric contamination.
The transmission function of fitted FPIs from different campaigns
and detection channels. The black, red, and blue groups are obtained from ATM
path measurement during the BRAINS ground campaign (ATMG) in 2009, INT path
measurement during NAWDEX from ground (INTG) in 2016, and INT path
measurement during NAWDEX airborne measurement (INTA) in 2016, respectively.
Determination of FPI transmission functions for SRRC
The most critical part both for ALADIN and for the A2D Rayleigh response
calibration is the determination of transmission curves of the FPIs for the
internal reference and atmospheric paths, respectively. The modeling of
FPIs performance has been discussed in previous studies (McGill and Spinhirne,
1998; McKay and Rees, 2000a, 2000b). As for ALADIN, the FPI
transmission curve in the atmospheric path is modeled by a convolution of
an Airy function, which describes the transmission of a perfect FPI, and a
tilted top-hat function (Dabas and Huber, 2017). The core idea of this
approach using Airy and top-hat functions is based on the comparison of
predicted and the measured Rayleigh response calibration. The FPI
transmission characteristics cannot represent the actual sensitivity of the
Rayleigh receiver at the atmospheric path until the difference of predicted
and measured responses coincide within a threshold limit.
Specific parameters of FPIs during different ground and airborne
campaigns illustrated in Fig. 5.
ParametersATM ground INT ground INT airborne ATMG INTG INTA Filtersfilter Afilter Bfilter Afilter Bfilter Afilter BFSR (GHz)10.93410.99810.93410.85110.93410.934FWHM (GHz)1.6711.7331.7431.8471.8331.943R0.6700.6960.6680.6790.6220.610σg (MHz)266363303391210247
Combinations for internal reference and atmospheric response
simulation with εR_slope and εR_intercept based on Eqs. (18a)–(18b) (Dabas and
Huber, 2017).
Unlike ALADIN, where only the transmission curve in the internal
reference path can be measured during instrument spectral registration, the
A2D FPI transmission curves both in the internal reference path and in the
atmospheric path were measured in previous campaigns. As listed in Table 4,
five combinations of FPI transmission functions derived from different
campaigns are used to derive different SRRCs. Since there is no simultaneous
dropsonde measurement to provide atmospheric temperature and pressure
information for modeling the atmospheric molecular backscattered spectrum
during the third calibration, the radiosonde dataset at a distance of
about 229 km to the calibration region (available at http://weather.uwyo.edu/upperair/sounding.html, last access: 12 July 2019) is used. The sensitivity
βx and intercept αx from fitting SRRCs can give a
qualitative comparison with the A2D MRRC. According to Eq. (11), the partial
derivative of αx and βx can be obtained as follows:
14∂Δf∂αATM=-1βATM,15∂Δf∂αINT=1βINT,16∂Δf∂βATM=αATM-MβATM2,M≡RATM-γATM,17∂Δf∂βINT=N-αINTβINT2,N≡RINT-γINT.
Using the typical values in the previous studies (Lux et al., 2018) – i.e., βATM=5.8×10-4 MHz-1, βINT=4.5×10-4 MHz-1, αATM=-0.06, and αINT=-0.001 –
and assuming realistic values of ΔβATM=10-5 MHz-1, ΔβINT=10-5 MHz-1, ΔαATM=0.01, and ΔαINT=0.01, it can be seen that the change in intercept, ΔαATM or ΔαINT, results in frequency differences
of about -17 or 22 MHz, equivalent to velocity differences of -2.99 or 3.91 m s-1, respectively. The effect of sensitivity, ΔβATM or ΔβINT, on velocity is related to the value
of M or N. In the case of M=0 or N=0, the change in sensitivity, ΔβATM or ΔβINT, results in a frequency difference of
about -1.8 or 0.05 MHz, equivalent to velocity differences of -0.31 or 0.009 m s-1. Therefore, the retrieval of LOS wind velocity
is more susceptible to intercept than sensitivity. The measured responses
and simulated SRRCs, including fits of internal reference (red) and the
eighth atmospheric altitude bin (blue dashed line, the corresponding
height is around 5.7 km), are chosen as an example and shown in Fig. 6.
Comparing the intercepts of measured and simulated ATM response curves, the
first and third combinations shown in Fig. 6a and c are
underestimated (-0.068, -0.102, respectively), while the second and
fourth combinations shown in Fig. 6b and d are overestimated (-0.040,
-0.042, respectively). Only the fifth combination, shown in Fig. 6e
and where the FPI parameters obtained from INTA and ATMG are used for internal
reference and atmospheric response determination, shows similar intercept
values (-0.055).
The response functions of internal reference and
eighth atmospheric altitude bin from MRRC (red and blue dashed lines, respectively, same on every plot) and different SRRCs
using different combinations of FPI transmission parameters (red and blue
dotted lines, respectively) a listed in Table 4.
In order to further determine which combination matches best to the actual
MRRC, the procedure adopted from ALADIN
(Dabas and Huber, 2017) is used. Herein, εR is defined as
the difference between response from the respective SRRCs and the MRRC.
Then, the linear fit of εR as a function of f′ is made,
returning a slope εR_slope and intercept
εR_intercept based on Eqs. (18a)–(18b) in
(Dabas and Huber, 2017). Ideally, if the results from the SRRC and MRRC
match, εR should be randomly fluctuating about 0 with zero
εR_intercept and εR_slope. Table 4 also lists the fitting results using five
different combinations, and it is shown that the fifth combination has
second smallest absolute εR_slope and
εR_intercept, offering the overall
consistence with the measured case. Therefore, the fifth combination will
be used for initial SRRC determination.
Case study using dropsonde data on 08:27:07 UTC, 23 September 2016: comparison of (a) sensitivity βATM (MHz-1), (b)ΔαATM, and (c) LOS velocity between results from A2D Rayleigh channel
MRRC (red) and not optimized SRRC (blue). The LOS velocity data from dropsonde
(black) and CDL (green) are also presented in Fig. 7c.
Optimization of FPI transmission characterization
The comparison of sensitivity and intercept of response calibration, as well
as the LOS wind velocity derived from SRRC and A2D measurements, can
intuitively assess the feasibility of SRRC on A2D Rayleigh wind retrieval.
Figure 7a and b show the comparison of βATM and ΔαATM=αATM+m0,ATM between results from SRRC and A2D
Rayleigh channel measurement at 08:33:06 UTC on 23 September 2016,
respectively. The LOS wind velocity results from SRRC, MRRC, simultaneous
dropsonde measurements, and CDL measurements are presented in Fig. 7c. It
can be seen that βATM and ΔαATM derived from
SRRC have similar altitude dependence as the one derived from MRRC,
indicating that the atmospheric temperature and pressure effect on the
response calibration is described correctly using the SRRC. However, the
discrepancy of ΔαATM between results from SRRC and MRRC
shown in Fig. 7b is obvious, resulting in a large discrepancy on LOS wind
velocity between SRRC and A2D Rayleigh channel datasets shown in Fig. 7c.
Taking data from a dropsonde which was released from HALO aircraft at the
same location as reference, the LOS results from SRRC are underestimated at a
height of 1–8 km where it can be regarded as “clear” Rayleigh wind
without Mie contamination, assuming that no aerosols are present in this altitude range. Thus, a further optimization of FPI parameters needs to be
implemented as the stability of the optical alignment of the instrument can
remarkably influence the performance of the A2D (Reitebuch et al., 2009;
Lemmerz et al., 2017; Lux et al., 2018).
The effect of the center frequency offset, Δf0, of
filter A and B for atmospheric path on atmospheric response (a)βATM(b)αATM, and (c) corresponding cost function
F(Δf0).
Considering the optical path of the A2D Rayleigh channel, the FPI center
frequency is sensitive to the incidence angle of the light. It is a
reasonable way to optimize FPI transmission function by fine adjusting the
center frequency of filter A or B for the atmospheric path. The Rayleigh
spectrometer is composed of double-edge FPIs which are sequentially coupled. Thus,
the reflection of the directly illuminated first FPI is directed to the
second FPI. Any incidence angle change before the Rayleigh spectrometer will
act similarly on both FPIs. Considering that the initial condition was
perpendicular incidence, both FPIs are affected similarly regarding a shift
in the center frequency. Furthermore, as angular shifts of only a few
µrad are expected to occur, large angles do not have to be
considered. Therefore, it is justified to consider the same offset for both
center frequencies induced by small incidence angle changes. Assuming the
center frequencies of filter A and B have the same offset Δf0
compared to the values obtained from ATMG, i.e., Δf0=Δf0,A=Δf0,B, and the FPI parameters at the internal reference
path are regarded as ideal, Fig. 8a and b present the effect of
Δf0 on the sensitivity and intercept of fitting SRRC at each
altitude bin, respectively. A cost function F(Δf0) is defined to
determine the optimized center frequency as follows:
FΔf0=∑i=1N|VLOS,SRRCi-VLOS,referencei|,
where VLOS,SRRC(i) is the LOS wind velocity derived from SRRC with
center frequency offset of Δf0 at altitude bin i,
VLOS,reference(i) is the LOS wind velocity from simultaneous dropsonde
datasets interpolated to the height of A2D Rayleigh channel altitude bin
i. Herein, all available altitude bins of SRRC from i=1 to i=N (N=17) are used to calculate the cost function F(Δf0) for
different Δf0. It is noted that altitude bins affected by
aerosol or cloud layer are hard to be flagged, unless there are auxiliary
information such as CDL measurements. Therefore, these bins affected by Mie
contamination are also taken into consideration in the calculation of
F(Δf0) calculation.
Case study using dropsonde data on 08:27:07 UTC, 23 September 2016: comparison of (a) sensitivity βATM (MHz-1), (b)ΔαATM, and (c) retrieved LOS velocity between results from A2D Rayleigh
channel MRRC (red) and optimized SRRC (blue). The LOS velocity data from
dropsonde (black) and CDL (green) are also presented in Fig. 9c.
It can be seen from Fig. 8c that F(Δf0) has its minimum when
the center frequencies of both filter A and B for the atmospheric path
increase by 20 MHz, corresponding to the optimization case for LOS wind
velocity retrieval using SRRC. The profiles for βATM and ΔαATM derived from SRRC with FPIs optimization are shown in Fig. 9a and b, respectively. Compared to Fig. 8a and b, the
increase in center frequencies of filter A and B (Δf0>0) results
in a decrease in βATM and ΔαATM. As shown in Fig. 9c, the LOS wind velocity derived from SRRC with optimized FPI
parameters now fits better to the dropsonde results except for heights below
1 km and at around 9 km where Mie contamination may negatively influence the
results.
The derived frequency shift of 20 MHz can basically depend on the alignment
of the atmospheric optical path. From experience from the last 10 years,
it is known that this alignment is not randomly varying from flight to
flight but changes from campaign to campaign. As the telescope and optical
receiver are coupled via free optical path (and not via a fiber), the
mechanical integration of the A2D into the aircraft prior to each campaign
leads to small variation in position and incidence angle on the
spectrometers for each deployment. Thus, a valid response calibration can be
used for the entire campaign period. This is true for both measured or
rather simulated response calibrations. In order to monitor the atmospheric
path alignment, the position of the spots generated on the ACCD detector
behind each FPI is analyzed and serves as information on the alignment
during the flight itself and among the flights during the campaign period.
It should be noted that the applied frequency shift is only 20 MHz, which is
even less than the frequency separation of successive measurement points
during a response calibration (25 MHz) and which corresponds to
1.8×10-3 of the FSR of the FPIs.
LOS velocity (Vlos) comparison obtained from (a) dropsonde and A2D
Rayleigh channel measurement with MRRC (b) dropsonde and SRRC before FPI
optimization, and (c) dropsonde and SRRC after FPI optimization.
Statistical comparison between results from dropsonde, A2D Rayleigh
channel measurement, and SRRC before and after FPIs optimization during 2016
campaign.
Statistical parametersDropsondeDropsonde to A2D SRRCDropsonde to A2D SRRCto A2D MRRCbefore FPI optimizationafter FPI optimizationNumber of compared data pairs185190190Correlation coefficient, r0.950.930.94Slope0.990.860.86Intercept, m s-10.19-3.70-0.32Mean bias, m s-10.23-3.320.05Standard deviation, m s-12.202.612.52Statistical comparison and assessment
A statistical comparison of LOS wind velocities derived from SRRC with other
instrument measurements is required to assess the feasibility and robustness
of SRRC under various atmospheric conditions. Firstly, the quality control
based on an SNR mask derived from the A2D Mie channel is applied
(Marksteiner, 2013) to identify invalid winds retrieved from the Rayleigh
channel, which retains a significant amount of valid Rayleigh winds via a
cloud and ground mask (Lux et al., 2018). Then, based on the matched dates
listed in Table 2, the comparisons of LOS wind velocity from dropsonde
measurements, A2D Rayleigh channel measurements, and results derived from
SRRC with and without FPI optimization are illustrated in Fig. 10. A linear
fit to the data points is presented to provide the slope and intercept. The
correlation coefficient r, bias, and standard deviation are also calculated
and listed in Table 5. Figure 10a illustrates the comparison of LOS wind
velocity between dropsonde and A2D Rayleigh channel measurement, showing
that the fit parameters slightly deviate from the ideal case. The
correlation coefficient r, bias, and standard deviation of the A2D Rayleigh
winds are 0.95, 0.23, and 2.20 m s-1, respectively, which is
comparable to results in previous studies (Lux et al., 2018). The comparison
of LOS wind velocity between dropsonde measurements and the results derived
from SRRC without FPI optimization is illustrated in Fig. 10b. The
corresponding correlation coefficient r, bias, and standard deviation are
determined to be 0.93, -3.32, and 2.61 m s-1, respectively.
It can be seen that underestimation of the LOS wind velocity from SRRC
without the FPI optimization is significant, demonstrating the necessity of
the FPI optimization before wind retrieval when using the SRRC procedure.
Figure 10c shows the comparison of LOS wind velocity between dropsonde
measurements and results derived from SRRC with FPI optimization. The bias
is 0.05 m s-1, which is better than the results from A2D wind with
MRRC, and the correlation coefficient r and standard deviation are 0.94 and
2.52 m s-1, respectively. This is comparable to the results from A2D
Rayleigh channel measurements and implies the feasibility and robustness of
SRRC with FPI optimization on A2D Rayleigh wind retrieval. From now on,
only SRRC results with optimized FPI parameters will be discussed.
(a) Difference in temperature between dropsondes used in SRRC and
the one during A2D instrument response calibration, and the difference in
(b) sensitivity and (c) intercept derived from A2D SRRC and MRRC. The red square
and the blue bar represent the mean bias and standard deviation at each
height, respectively.
In order to evaluate the atmospheric temperature effect on response
calibration procedure and wind retrieval, Fig. 11a shows the
atmospheric temperature difference between SRRC and MRRC, where the
red square and blue bar represent the mean bias and standard deviation at
each height, respectively. The differences in sensitivity and intercept of response
calibration between SRRC and MRRC are illustrated in Fig. 11b and c. It
can be seen from Fig. 11a that larger discrepancies of atmospheric
temperature can be found at about 7 to 8 km with mean differences of less
than 5 K. But for the corresponding differences in sensitivity and intercept,
shown in Fig. 11b and c, larger discrepancies appear at lower heights,
especially at heights lower than 3 km. On the one hand, it is implied that
the atmospheric temperature effect is less significant in the statistical
analysis of the 2016 flight campaign. On the other hand, due to the ground
elevation during A2D instrument response calibration, the measured response
calibration below 2 km in this case cannot be obtained, and thus the measured
response calibration at a height of 2 km is used for LOS velocity retrieval
below 2 km, causing larger discrepancies shown in Fig. 11b and c.
Comparison of profiles for LOS velocity (a) between A2D SRRC and
MRRC, (b) SRRC and dropsonde, and (c) MRRC and dropsonde.
The height-dependent comparisons of LOS wind velocity from different
datasets after quality control are illustrated in Fig. 12. The mean
differences in LOS wind velocity between SRRC and A2D Rayleigh channel
measurements shown in Fig. 12a have opposite trends at lower and higher
heights, which is related to the intercept difference shown in Fig. 9b.
Similar LOS wind velocity difference tendencies can be seen in Fig. 12b and c for the case between SRRC and dropsonde and between A2D Rayleigh
channel measurement and dropsonde. The error bars of LOS
velocity derived from MRRC and SRRC can also be seen in Fig. 12b and c, respectively. Generally, larger discrepancies occur at heights of
lower than 2 km and higher than 8 km. The LOS wind velocities derived from
A2D Rayleigh channel measurements have more obvious discrepancies at heights
lower than 2 km compared to the results derived from SRRC. This is
consistent with the fact that inappropriate values of A2D calibration
parameters at lower height result in additional LOS velocity bias, and this
is one of the limitations of the A2D MRRC approach, which can be overcome
using the SRRC approach. In order to analyze the height-dependent deviations
more comprehensively, Fig. 13 shows the examples of LOS wind velocity from
A2D Rayleigh channel measurement, dropsonde measurements, SRRC, and CDL on 23 September 2016, where dropsonde and CDL are interpolated to the A2D height.
The CDL provides high performance with accuracy of < 0.3 m s-1 and
precision of < 1 m s-1 (Chouza et al., 2016), and thus
we prefer to plot no error bars to the CDL measurements. Larger
discrepancies can be obviously seen at heights greater than 8 km due to the
occurrence of a cloud layer in these cases.
LOS velocity from dropsonde (black), CDL (green), A2D MRRC (red),
and A2D SRRC (blue) on (a) 08:27:07, (b) 08:33:06, and (c) 08:39:05 UTC,
23 September 2016.
(a) Matched CDL measurement where valid (or invalid) signal is
represented as 1 (or 0). (b) Mie contamination fraction, FMie, of
selected datasets from Table 2 used for comparison.
All matched CDL observations listed in Table 2 are used to assess the
probability of Mie contamination on Rayleigh wind results. Figure 14a
shows the CDL measurement behavior where valid (or invalid) signal is
represented as 1 (or 0). The Mie contamination fraction FMie, shown in
Fig. 14b, is defined as the ratio of the number of valid signals to all
CDL observation number N (here N=12) at each height. Obviously, FMie at
heights of lower than 2 km and between 7 and 11 km has high values,
giving the important cause for the larger discrepancies observed in Figs. 12
and 13. It is also implied that even though quality control mentioned above
is used, the applied SNR threshold approach cannot guarantee the accurate
removal of Rayleigh wind affected by Mie contamination.
Summary and conclusion
As the first airborne direct-detection wind lidar, the A2D has been deployed
in several ground and airborne campaigns over the last 12 years for
validating the measurement principle of Aeolus and further improving the
algorithm and measurement strategy. The A2D instrument calibration is used
to obtain the response calibration function, indicating the relationship
between the measured signal intensities and the Doppler frequency shift,
which is proportional to the wind speed. However, the atmospheric and
instrumental variability currently limit the reliability and repeatability
of the A2D instrument response calibration. For instance, there are some
factors affecting the accuracy of response calibration directly during
instrument response calibration such as Mie contamination, nonzero vertical
velocity, and unavailable response functions for lower altitudes due to high
ground elevation. The SRRC is thus presented in this paper to overcome these
limitations of MRRC.
The most critical part of SRRC is the determination of the transmission
characteristics of FPIs for the internal reference and atmospheric paths. Unlike the method used for the determination of ALADIN
FPI transmission curve in the atmospheric path where a tilted top-hat
function is used, the A2D candidate SRRCs using different combinations of
FPI transmission characteristics obtained from different campaigns were
calculated and compared to the MRRC firstly. It is found that the
combination of FPI parameters obtained from airborne internal reference path
measurement and the ground-based atmospheric path measurement are the best
to be used for the internal reference and atmospheric response determination
by SRRC. Since the stability of the optical properties of the FPIs and the
optical alignment of the instrument can remarkably influence the performance
of the A2D, a fine-tuning of FPI center frequencies for the atmospheric path is
performed to optimize the SRRC parameters. It is concluded that when the
center frequencies of both filter A and B for the atmospheric path increase
by 20 MHz, the LOS wind velocity derived from SRRC provides the best
consistency with the simultaneous dropsonde measurements. The dropsonde
profile of the wind velocity was used as reference in this study to obtain
an optimized SRRC. However, it would also be possible to use other
references such as the ECMWF model or 2 µm CDL measurements.
What's more, dropsonde data were used as a reference for statistical
comparison of LOS wind velocity since it has the generally best
spatiotemporal matching and coverage with the results derived from SRRC.
Firstly, the biases of LOS wind velocity derived from SRRC without and with
FPI optimization are -3.32 and 0.05 m s-1, respectively,
showing the necessity of FPI optimization for SRRC wind retrieval. The LOS
wind velocity from SRRC with FPI optimization also provides a standard
deviation of 2.52 m s-1, showing better accuracy and comparable
precision with respect to the results obtained from a conventional
(measured) Rayleigh response calibration, which yields a bias of 0.23 m s-1 and standard deviation of 2.20 m s-1. This demonstrates the
feasibility and robustness of SRRC on A2D Rayleigh wind retrieval.
Furthermore, the height-dependent statistical comparison shows that the
biases caused by inappropriate calibration parameters below 2 km due to the
limiting ground elevation during A2D instrument response calibrations can be
overcome by using SRRC, where the response values over the whole altitude
range from the aircraft down to mean sea level can be simulated. The larger
biases at heights below 2 km and above 8 km are related to residual Mie
contamination on the Rayleigh channel. It is also shown that even though
quality control based on SNR is used, the accurate removal of points
affected by Mie contamination cannot be guaranteed. This shows the necessity
of the combination of Mie and Rayleigh channel wind analysis.
It should be noted that the A2D SRRC procedure mentioned in this paper is
not a pure “copy” from what is done for ALADIN. There are some significant
differences, especially in the generation and update of the transmission
characteristics of the FPIs of the Rayleigh receiver for the atmospheric
channel. Firstly, as opposed to ALADIN where only the transmission curve in
the internal reference path can be measured during instrument spectral
registration, the A2D FPI transmission curves both in the internal reference
path and in the atmospheric path were measured in previous campaigns,
demonstrating slight deviations between both transmission paths due to the
aforementioned reasons. Therefore, different combinations of FPI
transmission functions derived from different campaigns can be used to
derive different candidate SRRCs. After the comparison of candidate SRRCs
with simultaneous MRRC, the most satisfactory combination is used for
initial SRRC determination. Secondly, as for ALADIN, the core idea of the
updated spectral registration using the Airy and top-hat functions is based
on the comparison of the predicted one and a MRRC. The FPI transmission
characteristics cannot represent the actual sensitivity of the Rayleigh
receiver at the atmospheric path until the difference in predicted and the
measured responses coincides within a threshold limit. But for A2D, the
optical path characteristic of the A2D Rayleigh channel is considered
carefully. The optimization of FPI transmission characteristics was made by
fine-tuning the center frequency of filter A or B for the atmospheric path,
thus obtaining optimized SRRC.
Overall, the SRRC allows for correction of variability in atmospheric
temperature and pressure profiles, giving accurate wind retrieval especially
in the cases of large atmospheric temperature differences between the
acquisition time and location of the MRRC and the actual wind measurements.
It can also overcome the possible ground elevation limitations, improving
the accuracy of A2D wind measurements at lower altitudes. Therefore, it can
improve the reliability and repeatability caused by atmospheric and
instrumental variability during the A2D MRRC process. Further studies based on
A2D SRRC will be performed regarding the atmospheric temperature and pressure
effect, Mie contamination correction, and the particulate optical properties
retrieval.
Data availability
Data used in this paper can be provided upon request by email to Oliver
Reitebuch (oliver.reitebuch@dlr.de).
Author contributions
XZ prepared the article, developed the method, and performed
the analysis of the A2D data. OR supported the development of
the method and analysis of the data. BW provided the
2 µm DWL data and provided the A2D FPI parameters. FW
analyzed the dropsonde data and supported the A2D analysis. UM
and OL provided tools for the analysis of the A2D observations.
BW performed the 2 µm DWL observations. OL,
CL, and OR performed the A2D measurements. All
co-authors provided input to the article and its revision.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The development of the ALADIN airborne demonstrator and the airborne
campaigns were supported by the German Aerospace Center (Deutsches Zentrum
für Luft- und Raumfahrt e.V., DLR) and the European Space Agency (ESA),
providing funds related to the preparation of Aeolus (WindVal II, contract
no. 4000114053/15/NL/FF/gp). The first author was funded by the Chinese
Scholarship Council (CSC number: 201706330031).
Financial support
This research has been supported by the German Aerospace Center Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR), the European Space Agency (ESA) (grant no. WindVal II, contract no. 4000114053/15/NL/FF/gp), and the Chinese Scholarship Council (CSC) (grant no. 201706330031).The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Review statement
This paper was edited by Ulla Wandinger and reviewed by two anonymous referees.
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