Lidars using vibrational and rotational Raman scattering to continuously
monitor both the water vapor and temperature profiles in the low and middle
troposphere offer enticing perspectives for applications in weather prediction
and studies of aerosol–cloud–water vapor interactions by simultaneously deriving
relative humidity and atmospheric optical properties. Several
heavy systems exist in European laboratories, but only recently have they been
downsized and ruggedized for deployment in the field. In this paper, we
describe in detail the technical choices made during the design and
calibration of the new Raman channels for the mobile Weather and Aerosol Lidar
(WALI), going over the important sources of bias and uncertainty on the water
vapor and temperature profiles stemming from the different optical elements
of the instrument. For the first time, the impacts of interference filters and
non-common-path differences between Raman channels, and their mitigation, in particular are
investigated, using horizontal shots in a homogeneous
atmosphere. For temperature, the magnitude of the highlighted biases can be
much larger than the targeted absolute accuracy of 1 ∘C
defined by the WMO (up to 6 ∘C bias below 300 m
range). Measurement errors are quantified using simulations and a number of
radiosoundings launched close to the laboratory. After de-biasing, the
remaining mean differences are below 0.1 gkg-1 on water vapor and
1 ∘C on temperature, and rms differences are consistent with
the expected error from lidar noise, calibration uncertainty, and horizontal
inhomogeneities of the atmosphere between the lidar and radiosondes.
Introduction
Atmospheric temperature and humidity in the low atmosphere are together
essential to comprehend weather phenomena and their evolution in a changing
climate. Through the effect of relative humidity on aerosol hygroscopicity and
cloud formation, they also influence the radiative balance of the Earth,
generating the largest uncertainties in climate projections (IPCC, 2013). For
both weather and climate prediction, observation means have evolved
tremendously, notably with satellite retrievals of moisture and temperature
routinely assimilated in numerical models. Yet remote sensing techniques from
spaceborne missions have difficulties probing the lower troposphere below
2–3 km in altitude, and have vertical resolutions that are too low,
greater than 1 km in the lower troposphere (e.g., Prunet et al., 1998;
Crevoisier et al., 2014). They are thus unable to resolve temperature
inversions and thin, dry or humid air masses (e.g., Chazette et al.,
2014a; Hammann et al., 2015;
Totems et al., 2019). Providing complementary profiles of the important
thermodynamic variables in the first kilometers of the atmosphere, where most
of the water vapor and temperature vertical variability is confined, is of
paramount importance for both weather forecasting and reducing aerosol-induced
uncertainty on climate models (Wulfmeyer et al., 2015).
Given their capacity for continuous, well-resolved, and precise temperature
measurements in the lower troposphere, vibrational Raman (VR) and rotational
Raman (RR) lidars have emerged as adequate tools in this endeavor. Water
vapor profilers are now well-established (from Whiteman et al., 1992, to
Dinoev et al., 2013), whereas temperature profilers have recently become more
widespread and powerful (from Cooney, 1972, and Vaughan et al., 1993, to
Weng et al., 2018, or Martucci et al., 2021). Without tackling turbulence-scale
resolution, which is the prerogative of heavier systems like the Raman lidars
of the University of Hohenheim (Behrendt et al., 2015), the University of
Basilicata (Di Girolamo et al., 2017), or ARTHUS (Atmospheric Raman
Temperature and Humidity Sounder, Lange et al., 2019), there is a need for
field-deployable instruments capable of fulfilling the breakthrough
requirements set by the World Meteorological Organization in terms of accuracy
on atmospheric temperature and humidity in the low troposphere (WMO, 2017).
Lidar profiles have proven beneficial for numerical weather prediction
(NWP) models (e.g., Adam et al., 2016; Fourrié et al., 2019), the study of
dynamic processes in the planetary boundary layer (PBL) (e.g., Behrendt et
al., 2015), or interactions between water vapor and aerosols (e.g.,
Navas-Guzmán et al., 2019). But to obtain the absolute accuracies demanded
here, especially that of 1 ∘C or less on temperature, the
required accuracy on the lidar channel ratios and their calibration is
extremely stringent, and the sources of systematic error are seldom discussed in
the literature (Behrendt and Reichardt, 2000; Simeonov et al., 1999; Whiteman
et al., 2012).
Within the European lidar landscape, WALI (Weather and Aerosol Lidar) is a
seasoned mobile Rayleigh–Mie–Raman system, “eyesafe” at 355 nm,
first deployed during the HyMeX international field campaign and subsequently
ChArMEx and PARCS, for aerosol and water vapor profiling (Hydrological
cycle in the Mediterranean eXperiment, Chemistry and Aerosol in the
Mediterranean Experiment, Pollution in the Arctic System; Chazette et al.,
2014b, 2018; Totems et al., 2019; Totems and Chazette, 2016). In its latest
evolution, the VR channels have been replaced by a Newton reflector and a
polychromator also including RR channels for temperature profiling. On this
occasion, we have established that biases due to various sources, in
particular from the dependency of spectral filtering on the angle of
incidence, detector non-uniformities, and other non-common-path differences
between Raman channels, may be several times greater than the requirements if
left unchecked. Correctible as they are by measuring the ratios of overlap
factors on the individual channels, these effects are not reported in the
literature of lidar temperature measurements. However, they were bound to
appear given the physical characteristics of the systems mentioned above.
The aims of this paper are (i) to compile, for the first time, the sources of
systematic error that must be considered and mitigated when using a Raman
lidar to profile atmospheric temperature and humidity and (ii) to validate WALI
as a dependable profiler deployable for field campaigns, satisfying the
requirements set by the WMO.
The theory of the Raman lidar retrieval of the atmospheric temperature and
WVMR, the error budget on these parameters, and the known causes of bias are
recalled in Sect. 2, as well as the principle and limitations of the
overlap measurement method. In Sect. 3, after summarizing the
characteristics of WALI, we propose a sequential review of the components of
the lidar chain, characterizing and mitigating the error sources. The
results of a calibration and qualification experiment using radiosondes
follow in Sect. 4. A conclusion and outlooks are presented in Sect. 5.
Theoretical considerationsRaman lidar retrieval of humidity and temperature
We will introduce notations by briefly recalling the theory of the retrieval
of water vapor content and temperature by the Raman lidar technique; the
complete theory has been extensively derived before, by Whiteman et al. (1992)
and Behrendt (2005) respectively, among others.
The vertical profiles of water vapor mixing ratio (WVMR) rH2O
and temperature T are calculated from the ratios of the H2OandN2-vibrational Raman (VR) channels and the RR2 (high-J number) and RR1
(low-J number) rotational Raman (RR) channels, respectively.
1R(z)=SH2O(z)SN2(z)2Q(z)=SRR2(z)SRR1(z)
Signals Sj(z) of Raman channels j have all been previously averaged
over the required altitude and time to improve the signal-to-noise ratio
(SNR) and have been corrected for (i) electronic baseline variations by subtracting a
baseline recorded every few profiles with detector (photomultiplier tube, PMT)
gain set to zero, (ii) the sky background mean value assessed on pre-trigger
or post-signal samples, (iii) PMT gain variations (allowed on the VR channels
to optimize daytime dynamic range; e.g., Chazette et al., 2014b), and (iv) known
leakage of the elastic return in the RR filters (Behrendt and Reichardt,
2000). Sj(z) values are thus expressed as
Sj(z)=1GjUjSj,raw(z)-L^j(z)-B^j-ε^jSelas(z),
where Gj is the channel gain controlled by PMT voltage Uj,
Sj,raw is the raw lidar signal, L^j is the estimated
baseline, B^j is the estimated sky background parasitic signal,
ε^j is the estimated residual transmittance of the
emitted laser wavelength through the interference filter (IF) of Raman channel
j compared to the elastic channel, and Selas is the elastic
signal. x^ denotes the estimate of x.
Both R and Q must then also be corrected for the difference of atmospheric
transmission between the two Raman channels and the ratio of overlap
factors:
4R′(z)=expΔτ(z)ORR^(z)R(z),5Q′(z)=1ORQ^(z)Q(z),
where Δτ(z) is the difference of optical thickness from the lidar
until range z observed between the wavelengths of the two VR channels, and
where ORR^(z) and ORQ^(z) are the estimated
ratios of the overlap factors of the two VR and RR channels, respectively
(expressed in Sect. 2.4). With an emitted wavelength at 355 nm,
Δτ(z) between 387 and 407 nm seldom produces deviations
above 5 % and can be efficiently estimated using an average
atmospheric density profile for molecular optical thickness and the
N2-Raman channel itself for aerosol optical thickness (e.g.,
Whiteman, 2003).
The WVMR is simply proportional to the VR scattering ratio between
H2O and N2, since nitrogen gas has a constant mixing ratio
in the troposphere and stratosphere. The temperature is retrieved from the
more complex dependency of the RR scattering cross sections between the two
channels RR1 and RR2. The respective estimates r^H2O and
T^ (to be distinguished from the true values without ^) are
obtained, after calibration, by
6r^H2O(z)=K^R′(z),7T^(z)=f^-1Q′(z),
where K^ is the estimate of the calibration coefficient for WVMR
combining all instrumental constants. Calibration function f^ is the
estimate of the temperature dependency of the ratio of RR cross sections. It
takes into account the instrumental constants of the two RR channels. We take
the model previously selected for operational purposes by Behrendt (2005):
Q′=f(T)=expa+bT+cT2,
with a, b, and c the coefficients of a polynomial regression of ln(Q′) as
a function of 1/T. K^ and f^ are obtained by confronting lidar
profiles of R′ and Q′ with collocated in situ measurements of
rH2O and T (e.g., from a radiosounding), aiming for a wide
range of values for a better constraint on the calibration.
Simple error budget
In this section, we will make a first assessment of the acceptable error on R and Q starting from the accuracy requirements for WVMR and temperature
profiles, which ensue from each scientific need, as compiled by Wulfmeyer
et al. (2015) for key applications. Monitoring, verification (e.g., model
qualification or calibration and validation of satellites), and data assimilation
purposes can be adequately addressed by a profiler capable of (i) <5% noise error and <2 %–5 % bias for water vapor and
(ii) <1∘C noise error and <0.2–0.5 ∘C
bias for temperature. In a simple error budget, we can use requirements of
(ΔrH2OrH2O)max=5% for WVMR and ΔTmax=1∘C for temperature, to give a first idea
of the different expectations for the performance of a VR–RR lidar.
Equations (4)–(8) allow us to derive constraints on the acceptable relative
error on the corrected lidar observables R′ and Q′, for either random
noise or bias, as
9ΔR′R′max=ΔrH2OrH2Omax,10ΔQ′Q′max=dQ′/dTQ′ΔTmax.
The relative error on R is equal to the constraint on WVMR,
i.e., 5 %. An assessment of the relative error on Q is performed
considering the RR filter parameters given in Table 2 (Sect. 3) to yield the
following numerical application: around T0=0∘C,
Q′(T0)=0.44, and dQ′/dT(T0)=+0.35/100∘C, so that (ΔQ′Q′)max=0.79%ΔTmax(∘C).
Summary of accuracy requirements from Wulfmeyer et al. (2015) and
corresponding constraints on ratios R′ and Q′. Resulting errors on relative
humidity RH at 0 ∘C and 50 % RH.
ParameterRandom errorSystematic error (bias)rH2O<5% relative<2 %–5 % relativeT<1∘C<0.2–0.5 ∘CR′<5%, i.e., SNR>20<2 %–5 %Q′<0.8% at 0 ∘C, i.e., SNR>125<0.12 %–0.4 % at 0 ∘CRH4.3 % RH at T=0∘C, RH=50%1.2–2.9 % RH at T=0∘C, RH=50%
The results, summarized in Table 1, have very important implications. In order
to fulfill WMO requirements for temperature and WVMR measurements, the Q′
ratio must be 6–10 times more accurate than R′. However, Raman
cross sections are larger for the RR channels than for the H2O VR
channel. Hence when dealing with a RR + VR lidar rather than a VR system, the
main difficulties are not only due to low signal-to-noise ratio, but they also
encompass strong constraints linked to instrumental biases. SNR as used in
Table 1 is defined on R and Q at the final resolution, and it is calculated
from the individual signal variances and means (including laser and
sky-background photon noise and detection noise) as
11SNRR=var(SN2)〈SN2〉2+var(SH2O)〈SH2O〉2-12,12SNRQ=var(SRR1)〈SRR1〉2+var(SRR2)〈SRR2〉2-12.SNRR, typically limited by the H2O channel, must be
above ∼20, and SNRQ must be above ∼125 to satisfy the
requirements given above. Such high values can be reached by increasing the
laser power and pulse repetition frequency (PRF) or enlarging the integration
over altitude and time, as SNR is usually magnified by the square roots of the
energy and number of averaged samples. However, limits on the latter are also
set by Wulfmeyer et al. (2015) for the same applications; integration range
Δz should be below 100 m in the PBL and 300 m in the
lower free troposphere, whereas an integration time Δt between 15
(assimilation and verification) and 60 min (monitoring) is required.
We derive the errors expected on RH given those on temperature and WVMR at the
bottom of Table 1. Here and in the following, % RH denotes absolute
percentage units on RH, whereas % denote relative errors. Relative humidity
is derived as a function of atmospheric pressure, temperature, and WVMR, using
standard empirical relationships for the water vapor saturation
pressure. Here, we use the Buck equation (Buck, 1981), which is accurate
within 0.2 % between -40 and +100 ∘C:
Pwv,sat=6.1121TT+257.14∘C13×exp18.678-TT+234.5∘C,14RH=PPwv,satrH2OrH2O+621.991gkg-1,
with P pressure and Pwv,sat the water vapor saturation pressure
in hectopascals and T temperature in degrees Celsius.
Sources of bias
Biases arising from inaccurate measurement of any of the estimated factors of
Eqs. (3)–(7), or from a variation after that measurement due to instabilities
in the instrument, must also be smaller than the aforementioned values of
2 %–5 % for WVMR and 0.12 %–0.4 % for temperature, the
latter being especially difficult to reach. Their impact must be mitigated
either by careful design or by precise estimation.
The expected (i.e., noiseless) values of R and Q can be detailed as
15R(z)‾=OH2O(z)ON2(z)KH2OKN2σH2OσN2rH2O(z),16Q(z)‾=ORR2(z)ORR1(z)K2σRR2(T(z))K1σRR1(T(z)),
with x‾ denoting the expected value of variable x and Kj, σj, and Oj(z) denoting the instrumental constant, Raman backscatter
cross section, and overlap factor of channel j, respectively. To simplify
our discussion, we choose to incorporate any deviation that affects the ratios
without a range dependence into the instrumental constant ratio and any
deviation with a range dependence into the overlap ratio.
(a) Definition of useful parameters for field angle θ and
filter angle of incidence θ′ calculations. f: receiver focal length;
Drec: receiver diameter; ϕ: full lidar field of view; f′:
collimation focal length; DIF: IF diameter. (b) Definition of metrics for
overlap calculations. e: emitter–receiver separation; H: hyperfocal distance;
ze: entry distance of laser into field of view. Green, red, and blue lines
represent rays from infinity, finite distance, and offset emitted beams,
respectively.
As previously explained, the impact of deviations on variables in Eq. (15)
remains tolerable below a few percent, but for the distinctly more constrained
temperature retrieval, the variables in Eq. (16) are affected by the following
effects that directly induce significant bias.
Laser wavelength drift or filter central wavelength (CWL) drift with
temperature both affect the ratios indiscriminately with range. By simulating
the variation in Q with the WALI filter parameters (Sect. 3), we find a large
impact of a wavelength drift Δλ (measured between the laser on
one side and both interference filters on the other side):
dQ/Q/dλ≈-0.26pm-1 and ΔT≈-0.34∘Cpm-1Δλ, meaning just
3 pm of drift in either filter or laser wavelengths can lead to biases
above 1 ∘C. That is one of the reasons why the laser must be
frequency-stabilized. Also, IFs subjected to fluctuations of local temperature
are known to experience CWL drifts; for WALI's filters manufactured by
Materion, this amounts to 1.28 pm∘C-1 (value given by the
manufacturer after their material dilation simulation). The temperature of
the polychromator must thus be kept stable within 1 ∘C for
this bias to become negligible.
Filter CWL variation with angle of incidence (AOI) on the IF generates a
channel transmittance variation which is range dependent and different for
each filter. Indeed, this variation ΔCWL is approached by
(e.g., Hayden Smith and Smith, 1990)ΔCWL(θ′)≈CWLθ′22neff2,where CWL is the filter central wavelength, θ′ is the angle
of incidence on the filter (assumed small), and neff is the
effective refractive index of the filter. For the RR1 filter (neff=1.62), we obtain as much as ΔCWL(θ′)≈43pmθ′ (∘)2. The problem stems from the fact
that because the filter is in the pupil plane, after collimation of the
received beam, each angle of incidence corresponds to a different point in the
focal plane of the receiver, which in turns corresponds to a field angle
θ of the lidar, as seen in Fig. 1a. Aperture number conservation across the receiving optical system
imposesθ′=ff′θ>DrecDIFθ,where f and f′ are the receiver and recollimation focal lengths, and
Drec and DIF are the receiver and IF diameters. For a
150 mm diameter receiver using a 1 in. diameter (22 mm clear
aperture) IF, we obtain at least θ′=0.39∘ for a
θ=1mrad field angle, producing ΔCWL(θ′)≈6.6pm and already ΔT≈2.2∘C. Note that the impact gets proportionately larger with
the diameter of the receiver. Because the optical path of each channel is
independently aligned, this always induces different overlap factors even when
sharing the same telescope. This large effect must be calibrated and
corrected, yet its impact was never discussed before in the RR lidar
literature, despite being 3 times as large in other systems with
450 mm receivers. This impact can be mitigated by attacking the
filters at normal incidence, where the derivative of CWL as a function of AOI
(see Eq. 17) is minimal.
Detector response non-uniformity up to ±12%, as a
function of both impact point on the active surface and angle of incidence, is
now specified on the cathodes of PMTs used at the 400 nm wavelength
(Hamamatsu, 2007, Sect. 4.3.3). The amplitude was found to be much larger by
Simeonov et al. (1999), with a significant impact. This effect has been bluntly
limited in all our lidars by putting the cathode plane as far as possible
before the focal plane, while still avoiding vignetting. It can still be
responsible for differences of overlap factors between channels.
Uncalibrated PMT gain or digitizer baseline variations will of course
induce bias in the channel system constants. We will see how to mitigate these
effects.
Slight variations in overlap or channel transmittance after calibration will
be directly responsible for bias. In the next sub-section, we discuss how
they can appear.
Overlap measurement with horizontal shots and limitations
Range-dependent biases influence the lower part of the lidar profiles exactly
like the overlap factors. They significantly impact the profiles up to a given
range from the emitter, depending on the characteristics of the receiving
optics as seen above, but also on the quality of the alignments, which is
seldom the same twice. Two methods are used in the literature to approximate
the actual overlap factors of a Raman lidar: (i) an iterative Klett inversion
of elastic and Raman channels sharing the same telescope is easy to achieve
(Wandinger and Ansmann, 2002) but inefficient when non-common-path errors are
involved, whereas (ii) the method of aiming the lidar horizontally (e.g.,
Sicard et al., 2002; Chazette and Totems, 2017) is sometimes impractical but
more direct and yields more accurate results in a horizontally homogeneous
atmosphere over a range of 1 to 2 km. In the context of RR
measurements, it is necessary to implement the latter and also to measure the
ratios of overlap factors, rather than the overlap factors themselves, thus
avoiding errors due to an imprecise estimation of atmospheric extinction.
Considering a horizontal line of sight in a supposedly homogeneous
atmosphere, the expected values of ratios R and Q can be expressed as
19R(z)‾=R(z∞)OH2O(z)ON2(z)exp-Δα⋅z,20Q(z)‾=Q(z∞)ORR2(z)ORR1(z),
where R(z∞) and Q(z∞) are the values observed when
all overlap factors have become constant at a sufficiently large range from
the lidar, noted as z∞, after which variations in the optical path
inside the reception channels become negligible. Δα=α(407nm)-α(387nm) is the difference of atmospheric
extinction between the two VR wavelengths.
To evaluate z∞, in Fig. 1b we introduce parameters that
characterize the overlap of a paraxial or coaxial lidar (e.g., Kuze et al.,
1998): (i) ze=2e/ϕ, at which the emitted laser beam located at
distance e from the receiver axis enters the field of view, whose full size
is ϕ, and ze is null for a coaxial system;
(ii) H=Drec/ϕ, the so-called hyperfocal distance, the
minimum range from which the beam originating from a point still fully enters
the field stop; (iii) HIF=2Drecf/f′θ′max, which we might call the filter hyperfocal distance, similarly
to the former, the minimum range from which the image of a point does not
exceed θmax′, the AOI on the IF that significantly changes
its transmittance. z∞ is above the maximum of those three, which is
usually HIF. If we use for θmax′ the AOI value
causing 1 ∘C bias on temperature in Eqs. (10) and (15), we
find z∞>HIF=780m. Note that z∞ can
reach several kilometers with misaligned filters.
If for instance the lidar can be mounted on a rotating platform capable of
aiming horizontally, the overlap ratios
ORR(z)=OH2O(z)/ON2(z) and
ORQ(z)=ORR2(z)/ORR1(z)
can be estimated with suitable precision (∼10-3) by
averaging the signals over time, smoothing them over range, and finally
correcting for the differential of extinction on the VR ratio:
21ORR^(z)=R(z)R(z∞)expΔα⋅z,22ORQ^(z)=Q(z)Q(z∞).
These estimates of the overlap ratios will then be used during signal
processing for vertical shots as in Eqs. (4) and (5). However, assumptions are
made for the former estimation, namely the following.
As explained above, the atmosphere is assumed to be homogeneous in WVMR and
temperature (down to <0.5∘C) up until z∞,
whereas the overlap ratios must be constant (down to <0.4%)
after z∞. Also, the maximum range (with sufficient SNR) of the
lidar must exceed z∞, implying nighttime measurements for the
Raman channels. Therefore, the effects generating overlap variation after a
few hundred meters must be prevented.
The lidar is assumed to retain the exact same overlap functions when aiming
horizontally and vertically. Considering a field of view around 1 mrad, the
stability of the emission and reception optical paths must be better than
∼10µrad between these two positions. This is feasible
for a small refractor but difficult for a Raman system such as WALI, with a
heavy laser and large reflector.
These difficulties make it extremely challenging to estimate the overlap
ratios with an accuracy better than a few percent. This is enough for the
WVMR, but we find that a correction must be applied by comparing with
in situ sounding for temperature measurements by Raman lidar.
Implementation and bias mitigation on the WALI system
In this section, we describe the WALI instrument from the emitter to the
reception channels, characterizing the critical elements in the framework of
WVMR and temperature measurements. The system has evolved from its previous
implementation described in Totems et al. (2019), by adding RR channels and
a fibered telescope receiver. A global diagram presenting the main lidar
sub-systems is shown in Fig. 2, and a summary of its characteristics is
given in Table 2.
Global diagram of the lidar system. The main sub-systems are the
emitter (center), the elastic receiver using a refractor (top), the Raman
receiver using a fibered parabolic reflector (bottom), and a separate,
thermally stabilized polychromator (upper right). See Fig. 5 for the
detail of the polychromator design.
WALI instrument characteristics summary (PRF: pulse repetition
frequency; FOV: field of view; CWL: central wavelength in vacuum; FWHM: full
width at half maximum; OOB: out-of-band blocking specification; OD: optical
density).
Its main features are a single rotatable platform (lightweight carbon fiber
breadboard by CarbonVision GmbH) carrying both its emission and reception
paths, a 150 mm refractor for the elastic channels (for aerosol
studies), and a 150 mm diameter parabolic fibered reflector for all
the Raman channels. The separation of the four Raman channels takes place in a
deported polychromator set in a thermally controlled enclosure, fed by the
optical fiber. Fiber optics are also known to partly scramble the input
illumination, which could help minimize the range dependance of filter
transmittance or detector sensitivity for the different Raman channels. The
output signals from the photomultiplier tubes (PMTs) in the polychromator
are digitized by a NI™ PXI system (not shown).
Emitter
The emitter is a commercial Lumibird/Quantel “Q-Smart 450” Nd:YAG pulsed
laser, stabilized by injecting the output of a single longitudinal mode fiber
laser emitting at 1064.175 nm into the main cavity (“SLM” option)
and frequency-tripled to emit at wavelength λlaser=354.725nm (in vacuum). The nominal pulse energy
for the Q-Smart 450 with SLM is 100 mJ at 355 nm, with a pulse
repetition frequency (PRF) of 20 Hz. These values set WALI near the
eye safety limit for pulsed energy, making the system eyesafe at the output of
a 2 m funnel, as limited by leaks at 532 nm through the
built-in filtering dichroic plates.
A critical issue to be cleared before using the Q-Smart 450 SLM in WALI was
the spectral purity and stability of the laser, in terms of line width and
wavelength drift. The laser seeder at 1064.175 nm is specified with a
50 MHz (0.062 pm at 355 nm) stability at fixed
temperature and 37 MHz∘C-1
(0.046 pm∘C-1 at 355 nm) temperature drift.
Nevertheless, the stability of the Q-Smart emission at 354.725 nm has
been verified with a dedicated optical setup, sending the output of a
Michelson interferometer with optical path differences (OPDs) between 0 and
100 mm on a UV-sensitive CCD camera. By extracting the contrast and
phase variations in the fringes at large OPDs from the videos, we were able to
ascertain
the laser line width, without seeder to be 24±2pm (versus 26.5 pm
data sheet value), and with seeder, to be small compared to 1 pm (versus 0.2 pm data sheet value);
the wavelength drift, without seeder to be below 8 pm over
10 min, and with seeder, to be below 0.2 pm rms (root-mean-square fluctuations) over 5 min. We consider the remaining
fluctuations to be mostly due to the ∼0.05pm∘C-1
temperature-linked drift of the seeder, which is not temperature-controlled
and was recently turned on.
Given the requirements derived in Sect. 2.3, this makes the seeded Q-Smart
laser theoretically suitable for RR measurements of temperature.
Raman receiver
In this sub-section we discuss the possible impact on the VR/RR ratios of
the fibered reflector (beam scrambling and fiber optics fluorescence), of
Raman filters characteristics, and of the polychromator design and
alignment. As far as we know, this type of comprehensive study does not exist
in the literature for Raman lidars.
Fibered reflector telescope and scrambling of the lidar field of view
The elastic and Raman receivers are both 150 mm in diameter. The focal
length of the refractor (elastic channels) is ∼300mm, which
with a 200×600µm field stop achieves full overlap at
∼150–200 m. However, the focal length of the reflector (Raman
channels) is 600 mm (parabolic mirror with aperture F/4); this implies
using a multimode fiber optics about 1 mm in diameter as the field
stop to allow similar results in terms of field of view and overlap. The
chosen fiber optics is an OH-rich UV fused-silica fiber, 2 m in length
and 1000 µm in core diameter, with a numerical aperture of 0.22
(Avantes FC-UV1000-2).
Coupling the reflector output into a multimode fiber (e.g., Chourdakis et al.,
2002) allows us (i) to minimize occultation of the primary mirror (here only
12 mm in diameter); (ii) to deport the Raman channel separation away
from the telescope, making it a separately tunable optical system, minimizing
the overall lidar size and making light or temperature confinement easier; and
(iii) in theory, to scramble the fiber output illumination versus the lidar
field angle, therefore minimizing the range dependence of AOIs on the IFs
discussed in Sect. 2.3 and flattening overlap ratios after the geometrical
full-overlap distance.
The scrambling of the lidar field of view, via the multiple internal
reflections in the fiber, has been experimentally tested by imaging the output
of the fiber, with a varying point-like input. The results are shown in
Fig. 3. Note that the radial coordinate of the output point relative to the
center of the fiber corresponds to a given AOI on a well-aligned IF in the
following polychromator, after a f′=50mm doublet lens.
Images of the output facet of the 1 mm diameter multimode fiber
optics for (a) centered and (b) decentered (at xin=0.38mm horizontal
offset from the center of the core) input point of a 20 mm beam focused on
the input facet of the fiber and energy density profiles along the x and y
axes.
It appears in Fig. 3b that the input energy is mostly redistributed
tangentially (i.e., along the angular polar coordinate, as opposed to radially)
by its passage through the fiber. The radial dispersion remains small, and the
mean output radius is approximately equal to the input radial
coordinate. Manually applying curvature to the fiber, as suggested by
so-called “mode scrambling” devices, did not make the energy distribution
more uniform so much as creating unwanted losses (effect not shown). Even for
a centered input, the energy radial distribution – i.e., the percentage of the
total output in a given radial bin, which will therefore impact a well-aligned
filter at the same AOI – is uniform. We conclude that even with the use of
fiber optics the angle of incidence on the interference filters depends on the
image positions in the focal plane of the telescope (i.e., mainly the distance
to the optical axis), in contrast to what could be expected. Range-dependent
biases will not be strongly mitigated.
Fiber optic fluorescence
It has been shown by Sherlock et al. (1999) and discussed by Whiteman
et al. (2012) that fiber optics fluorescence could be an obstacle to water
vapor measurements, because elastic scattering at 532 nm was inducing
fluorescence in an OH-poor fiber at a non-negligible level compared to the
atmospheric Raman scattering. It was solved by using an OH-rich fiber, but it
was predicted in the latter work that the effect could be larger at
355 nm.
We have characterized this effect in the WALI fiber optics, using a narrowband
CW laser excitation centered at 355 nm. The output of the fiber was
analyzed by a Fourier transform spectrometer (Thorlabs OSA201C spectrum
analyzer), behind a long-pass dichroic plate cutting the direct laser emission,
and the same collimating achromat as in the polychromator. The resulting
spectrum is shown in Fig. 4.
The 1000 µm diameter, 2 m long fiber fluorescence
measurement with 355 nm laser illumination.
We plot both the raw spectrum and the Fourier transform spectrometer noise
floor after 1000 profile integrations, to highlight the very weak features
observed at 780 to 910 nm and the high associated uncertainty. Due to
the noise level, and given the dichroic plate residual transmittance of the
laser wavelength, we can only ascertain that the fluorescence power spectral
density (PSD) around 400 nm is lower than 10-6 times the peak
laser PSD, although no feature can be detected in this spectral domain. Note
that fluorescence between 400 and 500 nm was indeed observed using a
broadband excitation from a fibered LED at 340 nm (not
shown). Nevertheless, the amount of rejection observed for a 355 nm
excitation is sufficient to exclude an adverse impact of the OH-rich fiber
optics for Raman lidar measurements.
Raman channelsPolychromator configuration
The RR + VR polychromator configuration used in WALI is presented in
Fig. 5. Dichroic and non-polarizing beam splitters are used to separate the
channels. In contrast to the design of Hammann et al. (2015) which optimizes
throughput and laser-line rejection on the RR channels, we chose to implement
a splitter-based configuration, favoring a compact system (25×25cm, easier to confine) and normal incidence on the filter, at the
expense of SNR. Indeed, designing the filters for a correct CWL at 5∘
incidence (as in the cited work) instead of 0∘ dramatically narrows
the filter angular acceptance, as can be deduced by deriving Eq. (15) as a
function of incidence θ′. In the WALI polychromator, the output from
the fiber is collimated by a near-UV achromat with 50 mm focal length,
resulting in a 22 mm diameter beam. Dichroic beam splitters with
adequate cut-on wavelengths are used to separate channels. On each separated
channel, an aspheric lense condenses light on the PMT surface, located
4 mm before the focal plane. A steel cage system assembly holds all
parts with great stability; however beam splitters are not always perfectly
aligned at 45∘ in the stock cage cubes. That is why all filter, lens,
and PMT sub-assemblies are mounted on tiltable mounts to allow precise
alignment at normal incidence.
Compact rotational and vibrational Raman separation configuration
used in WALI. IF: interference filter (with CWL–FWHM given in nanometers); BS:
beam splitter; LPD: long-pass dichroic beam splitter (with cut-off wavelength
given in nanometers); PMT: photo-multiplier tube. This polychromator is thermally
regulated in a dedicated light-tight enclosure.
Filter qualification
All interference filters were custom-made by Materion, including the RR
filters on specifications graciously shared by the team of A. Behrendt
(following Hamann et al., 2015). They were characterized on the Fourier
transform spectrometer (described in Sect. 3.2.2) prior to mounting, using
fibered LEDs peaking at 340, 385, and 405 nm as the light source; the
beam was collimated by the same near-UV achromat with 50 mm focal
length. We give the measurement results for the RR filters in Fig. 6 and
Table 3.
RR filter spectral transmittance measured with an optical spectrum
analyzer with illumination by a 340 nm LED: RR1 (low-J) and RR2 (high-J)
filter at 0∘ incidence.
Measured RR IF characteristics. All CWL values are given in vacuum.
RR1 filterRR2 filterUncertaintyCWL354.09 nm353.22 nm0.01 nmFWHM0.24 nm0.54 nm0.01 nmneff1.622.030.05Max transmittance69 %51 %5 %Laser line transmittance2.7×10-82.9×10-710 % relativeAngular acceptance (AOI for 10 % loss at CWL)1.5∘2.5∘0.2∘CWL shift at max field angle (i.e., edge of fiber,AOI = 0.59∘)-9.8 pm-2.5 pm0.3 pm
The effective index and angular acceptance of the filters (arbitrarily chosen
for a 10 % loss at the CWL) were assessed by tilting the filters of
a known angle. A critical parameter, the transmittance of both filters at the
laser line λlaser in operational conditions, was assessed on
the lidar itself, by measuring the energy of an echo on a hard target located
at 200 m and switching between an elastic IF of known transmittance
with a known strong optical density and the RR IF in question. The excellent
extinction in the RR1 filter guarantees a minimal effect of elastic signal
leak in temperature retrievals, but it was nevertheless subtracted as in
Eq. (3). Note that no significant echo was detected on the H2O-Raman
channel, indicating extinction better than a few 10-9, thanks to the two
dichroic plates.
Polychromator alignment and qualification
Due to the filter CWL shift evolving as the square of the AOI in Eq. (15), it
is essential to minimize range-dependent biases by aligning the filters at a
precisely normal incidence from the input beam. However off-the-shelf beam
splitter plate holders are found to be misaligned by up to 1∘ from an
ideal 45∘ incidence. All PMTs are mounted jointly with their own IF
and lens into a tiltable mount to correct for this (represented in Fig. 7).
Method for polychromator alignment validation. Light from LEDs is
input in the WALI fiber optics and passes through the polychromator and into a
multi-mode fiber (MM fiber, Ø 600 µm) analyzed by a Thorlabs
OSA201C Fourier transform spectrometer. Central channel wavelengths are
expected not deviate from those of the filter measured independently at
normal incidence, to validate alignment.
The alignment of these mounts is performed in the lab by conjugating an input
multimode fiber of 600 µm diameter replacing the lidar input,
into a target fiber 200 µm in diameter at the focus of the PMT
lens, through the polychromator. Fibered LEDs are used for illumination like
in Sect. 3.3.2. All the channels are sequentially addressed in this manner.
By obtaining a maximal energy and a radially uniform profile at the output of
the target fiber, one can ensure alignment with a precision of 0.1 to
0.3∘.
To verify the result, the spectral transmittance of the polychromator channels
themselves are characterized by the Fourier transform spectrometer, as shown
in Fig. 7. By illuminating the channel with a LED coupled in the actual lidar
fiber, we ensure that the polychromator is studied in operational
conditions. The CWL of each channel is expected not to deviate by more than
20 pm (twice the empirical accuracy) from the CWL measured on the
individual filter at normal incidence, to validate the alignment. The
polychromator aligned using the procedure proposed above passes this test.
Detectors
Hamamatsu 10721P-210 PMTs, with >0.13 A W-1 cathode sensitivity at
400 nm, and up to ∼2×106 controllable internal gain, are used to transform the optical flux
into an electric current, directly digitized at 200 MHz
(0.75 m sampling along the line of sight) by three NI PXI-5124
two-channel digitizers with 50 Ω load. The acquisition software,
custom-made with LabVIEW, conducts analog and photon-counting (thresholding at
∼3 standard deviations of the noise) accumulations in parallel during
1000 shots (50 s), every minute, which are then pre-processed and
recorded (∼10s downtime). Every ∼8min,
baselines are recorded with PMT gains set at zero. The next sub-sections
describe critical points of the detectors affecting the RR and VR channel
ratios.
PMT response variability
As explained in Sect. 2.3, the non-uniformity of the PMT response can affect
the ratios of Raman channels as a function of range. We tested the sensitivity
profiles of WALI's H2O-Raman PMT to continuous laser illumination at the
405 nm wavelength, first using a 1 mm diameter collimated
beam, as a function of both point and angle of incidence. A cumulated ∼6.0 neutral density filter was used to avoid saturation of the PMT.
As shown in Fig. 8a, a strong variation in sensitivity by a factor of almost 2
is found on the PMT surface, much larger than specified. The relative
sensitivity is lowest near the center of the PMT and highest on the sides, at
a diameter of 4 mm approximately equal to the spot size in the
lidar. Indeed the PMT surface is 4 mm before the focal plane of the
0.5 NA condensing aspheric lens. This is consistent with the results of
Simeonov et al. (1999) on an older generation of detectors, excluding a
suspected hole-burning phenomenon over the lifetime of our PMT. On the
vertical axis, we also note the effect of the gridded cathode. Note that
sensitivity does not vary by more than a few percent as a function of angle of
incidence (not shown).
Study of the non-uniformity of the PMT response: (a) as a function
of point of impact on the active area along the horizontal (blue) and
vertical (red), with a 1 mm collimated beam from a 405 nm laser, and (b) as a
function of angle of incidence on the lens and PMT assembly similar to the
ones used in the WALI polychromator, with a 22 mm collimated beam from the
same laser. Dashed lines represent uncertainty calculated over multiple
measurements. Sensitivity is given normalized by its value at the
approximate mechanical center of the PMT or at normal incidence as
determined using the reflection on the attached neutral density filter.
We then put the condensing lens used in the polychromator in front of the PMT
and studied its response as a function of AOI on the lens + PMT assembly,
which is shown in Fig. 8b. The input beam was the nominal size in the
polychromator, i.e. ∼22mm in diameter. We find that the curve
corresponds well to the measured sensitivity profile, smoothed by its
convolution with the spot on the PMT. The problem is that at normal incidence,
the derivative of sensitivity with incidence is 2 %–5 % per
degree. Using the calculations in Sect. 2, a θ=1mrad field
angle corresponds to 0.39∘ incidence on the PMT, inducing potentially
0.8 %–2 % bias on R and Q and thus a significant 1 to
2 ∘C bias on temperature. In the future, the condensing lenses
will be replaced with afocal beam reducers to reduce this dependency.
Baseline and EM parasite correction
The baseline induced by the detection chain is found to vary between channels
and in time. It is also subject to electro-magnetic (EM) interference, causing
parasitic signals of both high frequency, mostly due to the strong flashlamp peak current radiating over the system, and low frequency, probably due to
other neighboring electronics. For this reason, the channel baselines are
evaluated regularly (by averaging 1000 shots with PMT gain set to zero, every
8 min), smoothed and corrected (Lj in Eq. 2). However, for the
Raman channels (H2O and RR2 specifically), the weakness of the
signals requires a specific care of EM compatibility, as repeating parasitic
spikes were found to jam the channels (especially photon counting, which relies
on thresholding) starting at an altitude of 6–7 km.
Analog detection baseline measurements (red) over 1000 laser shots
with PMT gains set to zero, expressed in photon counts equivalent on the RR2
channel: (a) in an unfavorable case (no mitigation), showing both baseline
fluctuations over time (20km∼133µs) and strong
electro-magnetic parasites at large distance, and (b) on the WALI system, after
mitigation. The final estimated baseline (L^j(z) in
Eq. 3) obtained after smoothing, which is subtracted to all recorded
profiles, is in black.
Figure 9a shows an example of perturbed baseline. Trial and error established
that common methods to avoid ground loops were not all efficient: star
grounding of the various cables worsened the problem, whereas physically
separating coaxial signal cables from direct current power supply and control
voltage cables, and grounding all connectors and opto-mechanics again on the
breadboard side, mitigated it, reaching the baseline plotted in Fig. 9b. Note
that baseline variation is not significant between successive evaluations
without an external perturbation; the estimated baseline is automatically
subtracted from the profiles before recording during the next 8 min
(Eq. 3).
PMT gain adaptation
On each channel, PMT internal amplification gain G (using photoelectron
multiplication) is a definite function of its control voltage U. The
variation in G by ∼2 orders of magnitude allows for the optimization
of the dynamic range. This helps deal with the different Raman cross sections
in each filter, with variations in atmospheric transmittance, and especially
with sky background levels during daytime. The gain is pushed at its maximum
possible value still satisfying two conditions: (i) the signal voltage maximum
does not exceed the range of the digitizer, and (ii) the sky background signal
does not exceed the maximum output current of the PMT that guarantees
linearity (100 µA, i.e., 〈Sraw〉<5 mV). This is indispensable for day-round measurements of WVMR, otherwise
the channels would be saturated during daytime (Chazette et al., 2014b), or
suboptimal in SNR during nighttime.
Calibration of PMT gain G versus control voltage U: (a) log-gain
measurements and second-degree polynomial model for all Raman channels and
(b) relative gain ratio error between model and measurements for vibrational
and rotational Raman channel ratios.
However, PMT gain adaptation leads to biases on the Raman channel ratios if
the gain versus control voltage characteristics are not known with a better
precision than the requirements stated in Table 1 (2 % on VR
channels, 0.4 % on RR channels). In Fig. 10a, we show the
experimental calibration of G versus U as well as second-degree polynomial
fits for each channel. The relative error on the VR and RR channel gain ratios
approximated by these models is plotted in Fig. 10b, with the measurement
uncertainty. This uncertainty is mostly due to variations in atmospheric
parameters and laser energy during calibration. Since all relative errors are
well centered, we compute that the possible error for the gain ratio with
these models is ∼1.3%. This is compatible with WVMR
measurements but not with temperature measurements. Therefore, the PMT gain
should only be adapted on the VR channels, and the RR channels should be kept
at a fix value of gain.
Merging analog and photon-counting signals
Both analog and photon-counting raw signals are recorded. The analog signal
has lesser SNR at high altitude during nighttime, whereas the photon-counting
signal is saturated at low altitude and by daylight; by merging them
correctly, an optimal SNR can be obtained (Newsom et al., 2009). For signal
processing, the photon-counting raw signals are first desaturated (details in
Chazette et al., 2014b). Merging is performed during nighttime on the
pre-processed signals defined in Eq. (3). After calculating a photon-to-volt
conversion constant at an altitude where photon counting is not saturated, the
converted photon-counting profile replaces the analog profile after a
predefined altitude depending on signal strength (from 1 km for the
H2O VR channel up to 4 km for the elastic channel).
We wish to emphasize here that baselines Lj and background signals
Bj in Eq. (3) must be estimated separately for the analog and
photon-counting recorded profiles (which have no baseline and a smaller but
non-zero background value due to the suppression of electronic noise).
Otherwise, the merged signal will show discontinuities at the cut-off
altitude and biases at high altitude at dusk and dawn. Their impacts are
typically much larger than the requirements of Sect. 2.2.
Qualification on the atmosphere
In this section, we qualify the WALI system starting with the measurement of
its overlap factor ratios, followed by its calibration and comparisons with
radiosoundings. Remaining biases are highlighted and corrected, and
experimental measurement errors are evaluated.
Experimental set-up and strategy
We put the lidar into operation in our laboratory near Saclay
(48∘42′42′′ N, 2∘08′54′′ E) over a period of 2
weeks in May 2020. It was placed on a rotating platform below a trapdoor
equipped with silica windows for zenith shots and in front of a window at a
height of about 9 m above the ground level (a.g.l.) for horizontal
shots. During the latter, the lidar aimed north <5∘ above the
horizon (beam elevation <80m per kilometer of range). In that direction,
land use is fields up to 800 m in range, buildings and trees between 800
and 2 km in range, and fields again up to 5.5 km in range.
To calibrate and qualify the lidar measurements, we use radiosoundings
launched two to three times daily from the operational Météo-France
station located in Trappes (48∘46′27′′ N, 2∘00′35′′ E),
12.3 km WNW from the lidar near Saclay, approximately upstream in the
prevailing winds, although the wind was oriented mostly NE during the May 2020
period.
Measurement of overlap ratios with horizontal shots
The overlap factors and their ratios were estimated on signals averaged over
3 h after sunset on 19 December 2019, with a tepid
(14 ∘C), non-turbulent but hazy atmosphere (aerosol extinction
coefficient 0.32 km-1 at 355 nm with Ångström exponent
∼1.5, 11 ∘C ground temperature, and WVMR at ground level
around 6.5 gkg-1). With a planetary boundary layer (PBL) height
of ∼900 to 1000 m, and slow gradients of temperature (-1 to
-4 ∘Ckm-1) and WVMR (-0.8 to
-1.2 gkg-1km-1) in that PBL (as measured by radiosoundings
launched from Trappes at ∼12:00 and 00:00 UTC, presented in the
next subsection), conditions were excellent for a homogeneous atmosphere
within the first 5 km at least.
The estimated overlap factors of the different channels, with atmospheric
extinction fitted between 800 and 2000 m, are shown in Fig. 11a. Full
geometrical overlap is obtained as expected between 150 and 200 m, but
the curves differ by several percent between the Raman channels. Atmospheric
extinction drifts from the estimated value after 2 km.
(a) Overlap factors measured over 3 h of nighttime
measurements with a horizontal line of sight on 19 December 2019. Estimated
overlap ratios between VR (b) and RR (c) channels: native resolution (thin
blue line) and final estimate after smoothing (thick red line).
Results of calibration on 12 nighttime and 24 daytime
radiosoundings launched from Trappes between 20 May and 2 June 2020 for
WVMR (a–c) and temperature (d–f), in three steps: calibration on
measurements above 1500 m(a, d) with samples as crosses (one color per
radiosonde) and calibration curve in black; residual overlap ratio
estimation (b, e) with samples as crosses, mean ratio in blue, random error
on mean ratio as vertical bars, and model in red; and calibration on all results
(c, f). Daytime samples are limited to SNRs above 10 for R′ (WVMR) and 30 for
Q′ (temperature).
The estimated ratios of overlap factors ORR and
ORQ are plotted in Fig. 11b and c, at 7.5 m resolution
(thin line) and after smoothing (thick line, final correction used
hereafter). Peak divergence is 5 % to 7 %, at ∼150m. Convergence within 1 % happens at ∼400m, but oscillations of lower amplitude persist until ∼3km. We note that for ORQ, deviations do not exceed
the ±0.7% required to maintain bias below
1 ∘C. They are nevertheless corrected.
Comparison to radiosoundings and calibration, estimation of residual error
In total 12 nighttime and 24 daytime radiosoundings were launched from Trappes between
20 May and 2 June 2020. Lidar profiles are averaged from 0 to 40 min
after the radiosounding launch time. The range averaging is progressive and
defined to keep the nighttime temperature error below 1.5 ∘C:
range bins are 15 m long below 100 ma.g.l., growing to
360 m above 8 kma.g.l.
In order to de-bias WVMR and temperature measurements from residual errors on
ORR and ORQ, we perform a three-step calibration.
In the first step, we exclude the first 1500 ma.g.l. of the profiles
when fitting rH2O in situ vs. R′ and Q′ vs. T
in situ to estimate K and f, respectively. This initial calibration is
shown in Fig. 12a and d.
In the second step, using these first estimates, we then plot the ratios
between the lidar observables R′ and Q′ and the expected observables
deduced from the in situ measurements and these initial calibration
parameters. This provides an estimate of the remaining biases on
ORR and ORQ, which we find to be up to ∼4% and ∼1.8%, respectively. This represents a
small correction to the overlap ratios estimated while shooting horizontally
but remains larger than the requirements of precision specified in
Table 1. The modeled corrections of ORR and ORQ
are plotted in red in Fig. 12b and e. We fit a sum of three exponential falls
to the mean, of the form 1+a1exp(-z/z1)+a2exp(-z/z2)+a3exp(-z/z3), with ai coefficients and zi ranges to be
adjusted.
In the third step, we apply the previous estimates of ORR and
ORQ, and we perform a new calibration using all the data (down to
200 ma.g.l.), yielding more precise estimates of calibration
constants, as shown in Fig. 12c and f.
In the three steps, data with SNR lower than 10 for R′ and 30 for Q′ are
rejected so as to limit the impact of noise present at higher altitudes.
Residual deviations between lidar and Trappes radiosoundings in
terms of WVMR, temperature, and relative humidity, for nighttime (a–c) and
daytime (d–f), with mean deviation (thick lines) and RMSE (colored
rectangles). The error corresponding to noise levels on the lidar signal is
shown as darker rectangles. Cloudy profiles have been discarded. Daytime
measurements are limited to SNRs above 5 for R (WVMR) and 20 for Q
(temperature).
Statistics of observed differences for rH2O, T, and RH:
experimental mean differences (MD), root-mean-square differences (RMSDs),
averaged over two different range bins, in the low troposphere (1–2 km) and
the free troposphere (5–6 km). Comparison to the “natural” atmospheric
variability between the lidar and RS sites as modeled by the ECMWF/IFS ERA5
reanalyses (difference over the considered period between grid points
nearest to each of the two sites) and to the theoretical root-mean-square
error (RMSE) derived from the variance of the RR signals. The grid points
are located 8 km WNW of the lidar and 2 km S of the RS launching station 18.3 km apart, and almost all RS trajectories below 6 km
altitude are contained within the same “pixel” of the ERA5 fields as the
RS station.
The reliability of this calibration along time has been tested by comparing to
the same exercise performed 2 months later at the end of July 2020. After
calibration in the same conditions as in May, we found K decreased by
∼7.3%, and the temperature associated with a given value of
Q′ was ∼2.1∘C higher. However, ORR
and ORQ were still accurate within the reachable precision, i.e.,
∼0.2%. It was later proven that a malfunction of the laser
seeder was responsible for a slow drift of the emitted wavelength. Thus,
although a regular verification of the calibration is necessary, the
measurement of the overlap ratios is reliable.
In Fig. 13, we examine the residual deviations between the lidar and the same
series of radiosoundings used for the calibration. RH has been derived using
Eq. (14) from lidar-estimated WVMR and temperature, and the pressure profile
was given by radiosoundings. For each parameter rH2O, T, and RH,
we plot for daytime and nighttime profiles the mean and rms deviations
averaged over wide range bins as colored bars as well as the propagated
signal error as darker shaded areas. This allows us to compare the observed
random error to what could be expected from the level of noise on the lidar
measurements. Note that only profiles with good SNR unperturbed by clouds have
been selected for this comparison.
On WVMR, the results show little bias, and rms deviation is dominated by
spatial atmospheric variability at night and at low altitude (when SNR is
high) and by lidar noise in all other cases. For temperature, most of the rms
deviation is explained by noise; a significant +1 ∘C bias is
seen below 800 m during nighttime, opposed by a
-0.5 ∘C bias during daytime. This could be due to local
effects in the diurnal cycle between LSCE and Trappes, although no such effect
can be significantly highlighted in weather model reanalyses (described
below). In Fig. 13c and f the consequences of this bias on
relative humidity RH are plotted to be around 2 to 4 % RH, but the resulting
error to be expected is also plotted. We see that with the defined averaging random error is
around 2 % RH up to 5 kma.g.l. during nighttime and
1 kma.g.l. during daytime, growing fast above this level.
To support the above interpretation, in Table 4 we compare the experimental
mean difference and rms difference plotted in Fig. 13, averaged over two
altitude ranges (low troposphere (LT) at 1 to 2 km and free troposphere
(FT) at 5 to 6 km), to (i) the natural variability of the atmosphere
between the radiosondes at Trappes and the lidar at LSCE, as modeled by ERA5
reanalyses of the ECMWF/IFS weather model, and (ii) the expected random error
given the noise level on the RR signals. Nighttime and daytime values are
indicated in the LT, and only nighttime values are in the FT.
We see that the experimentally observed values of RMSD are rather consistent
with the quadratic sum of the rms variability of the atmospheric variables
between Trappes and LSCE and of the noise-induced RMSE. The excess
random difference is thus well explained by the distance.
There is still a discrepancy with the mean difference of temperature below
1000 m between daytime and nighttime, however, probably due to the
distance to the sounding station. Also, the model used to approximate a
regularized correction is still imperfect at such short ranges and introduces
small errors when the necessary correction is large and fast-varying. We aim
to improve this in the future by launching radiosondes directly from the lidar
site for calibration and by a better estimation the overlap ratios
horizontally, for instance using a large folding mirror instead of tilting the
lidar, which induces varying mechanical constraints on the optics.
Conclusions
During the qualification of the rotational Raman channels for the WALI lidar
of LSCE, with the aim of providing profiles of relative humidity, we
encountered important sources of bias that are seldom described in the now
abundant literature involving such systems. We highlighted the predominant
effects of the dependency of filter transmittance and detector sensitivity
upon angle of incidence and point of impact, respectively. Because the latter
parameters are directly proportional to field angle, they cause
range-dependent biases on the RR–VR signal ratios that are several times
greater than the required accuracy of lidars for temperature measurements
(only 0.79 % for 1 ∘C here), but less so for water
vapor measurements. We established that this effect cannot be suppressed by
using fiber optics between the receiver and polychromator, because scrambling
of the lidar field of view does not happen radially in the fiber. Mitigation
efforts impose the careful alignment of each filter at normal incidence to the
input beam and the verification of the spectral transmittance of each channel
on a spectrometer. The thermal stability of the polychromator is also of prime
importance. Other significant bias sources include electro-magnetic
perturbations of signal baselines and PMT gain variation, which must be
mitigated. The impacts of fiber optics fluorescence and the measured laser
line width or short-term wavelength drift were shown to be negligible in the
WALI system.
After a measurement of RR–VR channel ratios during horizontal shots, which
showed the significant impact of the above phenomena (up to 5 %
bias on ratios below 300 m, ∼1% higher), we
calibrated and de-biased the WALI measurements using radiosondes launched from
the nearby Trappes station of Météo-France. Between the de-clouded
lidar measurements and the radiosonde profiles, the remaining mean differences
are small (below 0.1 gkg-1 on water vapor, 1 ∘C
on temperature), and rms differences are consistent with the expected error
from lidar noise, calibration uncertainty, and horizontal inhomogeneities of
the fields between the lidar and radiosondes. For relative humidity we thus
reach a goal of ∼10 % RH random error and 5 % RH systematic
error up to 9 km by night and 1.5 km by day, with
40 min time integration and progressive vertical integration of 15 to
360 m at 10 km. The systematic error on RH is dominated by
bias on temperature, whereas the random error is dominated by noise on water
vapor measurements.
Thus exhaustively qualified, the WALI system may be applied in the near future
to exercises assimilating thermodynamic profiles in weather models, as is
expected within the WaLiNeAs (Water vapor Lidar Network Assimilation
experiment) project (Flamant et al., 2021). The long-term temporal evolution
of Raman channel calibration, expected from various effects like differential
PMT aging or laser seeder drift, induces biases variable in time over the
timescale of such a project (several months). This aspect is becoming a main
focus as the community works towards operational uses of weather Raman lidars
(e.g., Hicks-Jalali et al., 2020).
Code and data availability
The lidar data presented in this article, and information on code used for
data processing, are available upon request to Julien Totems at
julien.totems@lsce.ipsl.fr. Radiosoundings from the Teisserenc de Bort
station (Trappes) were obtained at
https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=97&id_rubrique=33 (Météo France, 2021), courtesy of Météo-France. ERA5 reanalyses of the ECMWF/IFS model were obtained at
https://doi.org/10.24381/cds.bd0915c6 (Hersbach et al., 2018) courtesy of the
Copernicus Climate Change Service (C3S).
Author contributions
JT designed, built, and operated the new version of the WALI system; led the
complementary measurements on lidar components; processed the data; and
wrote the article. PC conceptualized the WALI system, acquired funding, and
participated in structuring and proofreading of a previous version of the paper. AB participated in
the complementary measurements on lidar components and in lidar–model
comparisons, and proofread a previous version of the paper.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors thank Andreas Behrendt and Diego Lange for sharing
component specifications and for useful discussion.
Financial support
This work was funded by the Centre National d’Etudes Spatiales (CNES), the Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), and the Agence Nationale de la Recherche (ANR) WaLiNeAs Grant (grant no. ANR-20-CE04-0001).
Review statement
This paper was edited by Robert Sica and reviewed by two anonymous referees.
ReferencesAdam, S., Behrendt, A., Schwitalla, T., Hammann, E., and Wulfmeyer, V.: First assimilation of temperature lidar data into an NWP model: impact on the simulation of the temperature field, inversion strength and PBL depth, Q. J. Roy. Meteor. Soc., 142, 2882–2896, 10.1002/qj.2875, 2016.
Behrendt, A.: Temperature Measurements with Lidar, in: Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, vol. 102, edited by: Weitkamp, C., Springer-Verlag, New York, 273–306, 2005.Behrendt, A. and Reichardt, J.: Atmospheric temperature profiling in the presence of clouds with a pure rotational Raman lidar by use of an interference-filter-based polychromator, Appl. Optics, 39, 1372, 10.1364/AO.39.001372, 2000.Behrendt, A., Wulfmeyer, V., Hammann, E., Muppa, S. K., and Pal, S.: Profiles of second- to fourth-order moments of turbulent temperature fluctuations in the convective boundary layer: first measurements with rotational Raman lidar, Atmos. Chem. Phys., 15, 5485–5500, 10.5194/acp-15-5485-2015, 2015.Buck, A. L.: New Equations for Computing Vapor Pressure and Enhancement
Factor, J. Appl. Meteorol., 20, 1527–1532,
10.1175/1520-0450(1981)020<1527:NEFCVP>2.0.CO;2, 1981.Chazette, P. and Totems, J.: Mini N2-Raman Lidar onboard ultra-light aircraft for aerosol measurements: Demonstration and extrapolation, Remote Sens.-Basel, 9, 1226, 10.3390/rs9121226, 2017.Chazette, P., Marnas, F., Totems, J., and Shang, X.: Comparison of
IASI water vapor retrieval with H2O-Raman lidar in the framework of
the Mediterranean HyMeX and ChArMEx programs, Atmos. Chem. Phys., 14,
9583–9596, 10.5194/acp-14-9583-2014, 2014a.Chazette, P., Marnas, F., and Totems, J.: The mobile Water vapor Aerosol Raman LIdar and its implication in the framework of the HyMeX and ChArMEx programs: application to a dust transport process, Atmos. Meas. Tech., 7, 1629–1647, 10.5194/amt-7-1629-2014, 2014b.Chazette, P., Raut, J.-C., and Totems, J.: Springtime aerosol load as observed from ground-based and airborne lidars over northern Norway, Atmos. Chem. Phys., 18, 13075–13095, 10.5194/acp-18-13075-2018, 2018.Chourdakis, G., Papayannis, A., and Porteneuve, J.: Analysis of the receiver response for a noncoaxial lidar system with fiber-optic output, Appl. Optics, 41, 2715, 10.1364/AO.41.002715, 2002.Cooney, J.: Measurement of Atmospheric Temperature Profiles by Raman Backscatter, J. Appl. Meteorol., 11, 108–112, 10.1175/1520-0450(1972)011<0108:MOATPB>2.0.CO;2, 1972.Crevoisier, C., Clerbaux, C., Guidard, V., Phulpin, T., Armante, R., Barret, B., Camy-Peyret, C., Chaboureau, J.-P., Coheur, P.-F., Crépeau, L., Dufour, G., Labonnote, L., Lavanant, L., Hadji-Lazaro, J., Herbin, H., Jacquinet-Husson, N., Payan, S., Péquignot, E., Pierangelo, C., Sellitto, P., and Stubenrauch, C.: Towards IASI-New Generation (IASI-NG): impact of improved spectral resolution and radiometric noise on the retrieval of thermodynamic, chemistry and climate variables, Atmos. Meas. Tech., 7, 4367–4385, 10.5194/amt-7-4367-2014, 2014.Dinoev, T., Simeonov, V., Arshinov, Y., Bobrovnikov, S., Ristori, P., Calpini, B., Parlange, M., and van den Bergh, H.: Raman Lidar for Meteorological Observations, RALMO – Part 1: Instrument description, Atmos. Meas. Tech., 6, 1329–1346, 10.5194/amt-6-1329-2013, 2013.Flamant, C., Chazette, P., Caumont, O., Di Girolamo, P., Behrendt, A., Sicard, M., Totems, J., Lange, D., Fourrié, N., Brousseau, P., Augros, C., Baron, A., Cacciani, M., Comerón, A., De Rosa, B., Ducrocq, V., Genau, P., Labatut, L., Muñoz-Porcar, C., Rodríguez-Gómez, A., Summa, D., Thundathil, R., and Wulfmeyer, V.: A network of water vapor Raman lidars for improving heavy precipitation forecasting in southern France: introducing the WaLiNeAs initiative, Bulletin of Atmospheric Science and Technology, 2, 10, 10.1007/s42865-021-00037-6, 2021.Fourrié, N., Nuret, M., Brousseau, P., Caumont, O., Doerenbecher, A., Wattrelot, E., Moll, P., Bénichou, H., Puech, D., Bock, O., Bosser, P., Chazette, P., Flamant, C., Di Girolamo, P., Richard, E., and Saïd, F.: The AROME-WMED reanalyses of the first special observation period of the Hydrological cycle in the Mediterranean experiment (HyMeX), Geosci. Model Dev., 12, 2657–2678, 10.5194/gmd-12-2657-2019, 2019.Di Girolamo, P., Cacciani, M., Summa, D., Scoccione, A., De Rosa, B., Behrendt, A., and Wulfmeyer, V.: Characterisation of boundary layer turbulent processes by the Raman lidar BASIL in the frame of HD(CP)2 Observational Prototype Experiment, Atmos. Chem. Phys., 17, 745–767, 10.5194/acp-17-745-2017, 2017.Hamamatsu: Characteristics of photomultiplier tubes, in:
Photomultiplier tubes: basics and applications, 3rd edn., Hamamatsu
Photonics K.K. Electron Tube Division, available at: https://www.hamamatsu.com/resources/pdf/etd/PMT_handbook_v3aE-Chapter4.pdf
(last access: 29 April 2021), 2007.Hammann, E., Behrendt, A., Le Mounier, F., and Wulfmeyer, V.: Temperature profiling of the atmospheric boundary layer with rotational Raman lidar during the HD(CP)2 Observational Prototype Experiment, Atmos. Chem. Phys., 15, 2867–2881, 10.5194/acp-15-2867-2015, 2015.Hayden Smith, W. and Smith, K. M.: A polarimetric spectral imager using acousto-optic tunable filters, Exp. Astron., 1, 329–343, 10.1007/BF00454329, 1990.Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], 10.24381/cds.bd0915c6, 2018Hicks-Jalali, S., Sica, R. J., Martucci, G., Maillard Barras, E., Voirin, J., and Haefele, A.: A Raman lidar tropospheric water vapour climatology and height-resolved trend analysis over Payerne, Switzerland, Atmos. Chem. Phys., 20, 9619–9640, 10.5194/acp-20-9619-2020, 2020.
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tigno, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, 2013.Kuze, H., Kinjo, H., Sakurada, Y., and Takeuchi, N.: Field-of-view dependence of lidar signals by use of Newtonian and Cassegrainian telescopes, Appl. Optics, 37, 3128, 10.1364/AO.37.003128, 1998.Lange, D., Behrendt, A., and Wulfmeyer, V.: Compact Operational Tropospheric Water Vapor and Temperature Raman Lidar with Turbulence Resolution, Geophys. Res. Lett., 46, 14844–14853, 10.1029/2019GL085774, 2019.Martucci, G., Navas-Guzmán, F., Renaud, L., Romanens, G., Gamage, S. M., Hervo, M., Jeannet, P., and Haefele, A.: Validation of pure rotational Raman temperature data from the Raman Lidar for Meteorological Observations (RALMO) at Payerne, Atmos. Meas. Tech., 14, 1333–1353, 10.5194/amt-14-1333-2021, 2021.Météo-France: Observations d’altitude (Radio sondages), Météo-France [data set], available at: https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=97&id_rubrique=33, last access: 30 November 2021.Navas-Guzmán, F., Martucci, G., Collaud Coen, M., Granados-Muñoz, M. J., Hervo, M., Sicard, M., and Haefele, A.: Characterization of aerosol hygroscopicity using Raman lidar measurements at the EARLINET station of Payerne, Atmos. Chem. Phys., 19, 11651–11668, 10.5194/acp-19-11651-2019, 2019.Newsom, R. K., Turner, D. D., Mielke, B., Clayton, M., Ferrare, R., and Sivaraman, C.: Simultaneous analog and photon counting detection for Raman lidar, Appl. Optics, 48, 3903, 10.1364/AO.48.003903, 2009.Prunet, P., Thépaut, J.-N., and Cassé, V.: The information content of clear sky IASI radiances and their potential for numerical weather prediction, Q. J. Roy. Meteor. Soc., 124, 211–241, 10.1002/qj.49712454510, 1998.Sherlock, V., Garnier, A., Hauchecorne, A., and Keckhut, P.: Implementation and Validation of a Raman Lidar Measurement of Middle and Upper Tropospheric Water Vapor, Appl. Optics, 38, 5838, 10.1364/AO.38.005838, 1999.Sicard, M., Chazette, P., Pelon, J., Won, J. G., and Yoon, S.-C.: Variational method for the retrieval of the optical thickness and the backscatter coefficient from multiangle lidar profiles, Appl. Optics, 41, 493, 10.1364/AO.41.000493, 2002.Simeonov, V., Larcheveque, G., Quaglia, P., van den Bergh, H., and Calpini, B.: Influence of the photomultiplier tube spatial uniformity on lidar signals, Appl. Optics, 38, 5186, 10.1364/AO.38.005186, 1999.Totems, J. and Chazette, P.: Calibration of a water vapour Raman lidar with a kite-based humidity sensor, Atmos. Meas. Tech., 9, 1083–1094, 10.5194/amt-9-1083-2016, 2016.Totems, J., Chazette, P., and Raut, J.-C.: Accuracy of current Arctic springtime water vapour estimates, assessed by Raman lidar, Q. J. Roy. Meteor. Soc., 145, 1234–1249, 10.1002/qj.3492, 2019.Vaughan, G., Wareing, D. P., Pepler, S. J., Thomas, L., and Mitev, V.: Atmospheric temperature measurements made by rotational Raman scattering, Appl. Optics, 32, 2758, 10.1364/AO.32.002758, 1993.Wandinger, U. and Ansmann, A.: Experimental determination of the lidar overlap profile with Raman lidar, Appl. Optics, 41, 511, 10.1364/AO.41.000511, 2002.Weng, M., Yi, F., Liu, F., Zhang, Y., and Pan, X.: Single-line-extracted pure rotational Raman lidar to measure atmospheric temperature and aerosol profiles, Opt. Express, 26, 27555, 10.1364/OE.26.027555, 2018.Whiteman, D. N.: Examination of the traditional Raman lidar technique I Evaluating the temperature-dependent lidar equations, Appl. Optics, 42, 2571, 10.1364/AO.42.002571, 2003.Whiteman, D. N., Melfi, S., and Ferrare, R.: Raman lidar system for the measurement of water vapor and aerosols in the Earth's atmosphere, Appl. Optics, 31, 3068–3082, 10.1364/AO.31.003068, 1992.Whiteman, D. N., Cadirola, M., Venable, D., Calhoun, M., Miloshevich, L., Vermeesch, K., Twigg, L., Dirisu, A., Hurst, D., Hall, E., Jordan, A., and Vömel, H.: Correction technique for Raman water vapor lidar signal-dependent bias and suitability for water vapor trend monitoring in the upper troposphere, Atmos. Meas. Tech., 5, 2893–2916, 10.5194/amt-5-2893-2012, 2012.
WMO: WMO Oscar: List of all requirements, available
at: https://www.wmo-sat.info/oscar/requirements (last access: 28 April 2021), 2017.Wulfmeyer, V., Hardesty, M. R., Turner, D. D., Behrendt, A., Cadeddu, M. P.,
Di Girolamo, P., Schlüssel, P., Van Baelen, J., and Zus, F.: A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles, Rev. Geophys., 53, 819–895, 10.1002/2014RG000476, 2015.