To correctly understand and interpret lidar-acquired
signals and to provide high-quality data, the characterization of the lidar
transmitter–receiver geometry is required. For example, being fundamental to
correctly align lidar systems, this characterization is useful to improve
the efficiency of the alignment procedure. In addition, some applications
(e.g. air quality monitoring) need to quantitatively interpret the
observations even in the range where the overlap between the telescope field
of view and the laser beam is incomplete. This is generally accomplished by
correcting for the overlap function. Within the frame of lidar-based
networks (e.g. ACTRIS/EARLINET, the Aerosol, Clouds and Trace Gases Research Infrastructure/European Aerosol Research Lidar
Network), there is a need to define standardized
approaches to deal with lidar geometry issues. The multi-wavelength
multi-telescope Rayleigh–Mie–Raman “9-eyes” system in Rome Tor Vergata,
part of ACTRIS/EARLINET, has the capability, through
computer-controlled servomotors, to change the orientation of the laser beams and the
3D position of the diaphragm of the receiving optical system around the
focal point of the telescopes. Taking advantage of these instrumental design
characteristics an original approach to characterize the dependency of the
acquired signal from the system relative transmitter–receiver geometry (the mapping procedure) was developed. The procedure consists in a set of programs
controlling both the signal acquisition as well as the motor movements. The
approach includes solutions to account for atmospheric and laser power
variability likely to occur during the mapping sessions. The paper describes
in detail the developed procedure and applications such as the optimization
of the telescope/beam alignment and the estimation of the overlap function.
The results of the mapping applied to a single combination of
telescope-laser beam are shown and discussed. The effectiveness of the
mapping-based alignment was successfully verified by comparing the whole
signal profile and the outcome of the telecover test, adopted in EARLINET, for a
manual and a mapping-based alignment. A significant signal increase and
lowering of the full overlap height (from 1500 m to less than 1000 m) was
found. The overlap function was estimated down to 200 m and compared against
the one obtained from a geometric model. The developed procedure
also allowed estimating the absolute and relative tilt of the laser beam. The
mapping approach, even in simplified versions, can be adapted to other
lidars to characterize and align systems with non-motorized receiving
geometry.
Introduction
Lidar (light detection and ranging) techniques are an efficient tool to
provide quantitative information about vertical properties in the atmosphere (Measures, 1984; Weitkamp, 2005). Thanks to
the technological advancement of the last 20 years, the employment of
lidar systems in sensing the Earth atmosphere has rapidly grown. As an
example, aerosol properties are studied by spaceborne lidar observations
(e.g. Winker et al., 2003; McGill et al., 2015; AEOLUS), by
ground-based lidar networks (e.g. the European Aerosol Research Lidar
Network, EARLINET, Pappalardo
et al., 2014), and, recently, by single-channel automated lidar ceilometers
(e.g. Wiegner et al., 2014; Dionisi
et al., 2018). In particular, the advanced multi-wavelength elastic and Raman
lidars, which are part of EARLINET, provide unsurpassed information for the
characterization of aerosol optical properties. This network is now a key
component of ACTRIS (the Aerosol, Clouds and Trace Gases Research Infrastructure,
https://www.actris.eu/, last access: 28 February 2022), a research infrastructure that will coordinate the
atmospheric composition observations in Europe. Within this frame, to
provide quality-assured data sets by non-standardized lidar systems, like
most of those that are part of EARLINET, one of the major efforts of this
community was to establish quality assurance (QA) methodologies (Pappalardo
et al., 2014; Wandinger et al., 2016; Freudenthaler et al., 2018). The expected
outcome of this effort is to characterize lidar performances and check,
homogenize, and attest to the quality of the acquired data. After passing the QA
tests, lidar raw data can then be processed by the Single Calculus Chain
(SCC) that allows the “automatization and fully traceability of
quality-assured aerosol optical products” (D'Amico et al., 2015). Within
this frame, the characterization of lidar transmitter–receiver geometry
(e.g. Halldórsson
and Langerholc, 1978; Measures, 1984; Kokkalis, 2017) is essential to
provide high-quality data.
As the main objective of EARLINET is the study of the aerosol in the
troposphere and boundary layer (PBL), it is important to correctly interpret
the received lidar signal in the lowermost range. However, bi-axial lidar
systems present an incomplete response in the near-range observational field
due to the partial overlap of the receiver field of view (FOV) and the
transmitted beam. Therefore, to use data from heights below the full overlap
height, lidar signal profiles must be corrected for this near-field loss of
signal that is the overlap function O(R) (Wandinger and
Ansmann, 2002), which depends on the lidar system (e.g. Wandinger et al., 2016).
Within EARLINET, the telecover test, presented by Freudenthaler et al. (2018), is a useful and easily
implementable tool for the evaluation of the correct alignment of the lidar
system. This method allows identifying the lower height at which the lidar
signal can be used to retrieve aerosol optical properties (i.e. the lower
height of full overlap for ideal lidar system); however, it cannot provide
an estimation of the overlap function. In the literature various methods were
developed to compute this function both analytically and experimentally.
Analytical methods (e.g. Halldórsson
and Langerholc, 1978; Jenness et al., 1997; Chourdakis et al., 2002;
Stelmaszczyk et al., 2005; Comeron et al., 2011) require knowledge of light
distribution in the laser beam cross section, receiver characteristics and
relative inclination of the laser beam with respect to the receiver axis.
Experimental methods on the other hand make specific assumptions or have
special requirements based on the method: clear air and homogeneous aerosol
distribution (Sasano et al., 1979), statistically
homogeneous distribution (Tomine et al., 1989),
extrapolation via polynomial regression (Dho et al.,
1997), a second profile with lower overlap (e.g. ceilometer, Guerrero-Rascado et al., 2010;
Sicard et al., 2020), or a Raman channel with the assumption of similar
receiver geometrical configuration (Wandinger and Ansmann, 2002).
The multi-wavelength multi-telescope Rayleigh–Mie–Raman (RMR) “9-eyes”
system in Rome Tor Vergata (Congeduti et al., 1999) is an
old-style powerful lidar developed in the mid-90s with the objective of
monitoring the mid and upper atmosphere (D'Aulerio
et al., 2005; Campanelli et al., 2012; Dionisi et al., 2013a, b). To meet
the EARLINET requirements, which the system has been part of since July 2016, in addition to the standard EARLINET procedure, specific tests, based
on the characteristics of the system, were developed to characterize the RMR
performance in the near range.
The RMR system was designed with the capability to control, through
computer-controlled servomotors, the orientation of the laser beams and the
3D position of the diaphragm of the receiving optical system around the
focal point of the telescopes. These instrumental characteristics were
exploited to develop the mapping procedure: a set of semi-automated tools to
characterize the dependency of the acquired signal from the relative
transmitter–receiver geometry.
With respect to the existing approaches, the obtained results do not need
any assumptions or external information and include all artefacts due to the
system that may be difficult to account for in an analytical or numerical
representation.
With the objective of optimizing the RMR observational performances in the
troposphere and in the PBL, the developed procedure and two examples of
applications are presented in this study:
alignment optimization based on mapping information;
experimental estimation of the overlap function O(R).
In Sect. 2 the relevant instrumental characteristics of the RMR lidar
system with a specific focus on the emitter–receiver geometry of the system
are presented. Section 3 describes the developed methodology and reports
examples of telescope and laser mapping. Section 4 presents the results
obtained for two applications limited to a single wavelength telescope
combination. The mapping-based alignment is verified through the comparison
with telecover test results. The overlap estimation is compared to the full
overlap height estimated with the telecover test and with the predicted
values using a simple geometric model based on the nominal characteristic of
the system as presented in Sect. 2.
Finally, Sect. 5 contains the summary of the developed approach, the
achieved main results, and short-term perspectives in terms of
potential development. The applicability of the proposed approach to other
systems is also discussed.
System description
The design of the multi-channel multi-telescope RMR 9-eyes lidar was
first presented by Congeduti et al. (1999). Since 2002 the system has been operating in the Tor Vergata experimental
field in a semi-urban area southeast of Rome (41.8422∘ N,
12.6474∘ E; 107 m a.s.l.). Its current configuration is
described in detail by Dionisi et al. (2010).
Here the relevant characteristics of the lidar system with an emphasis on the
geometry of the emitting/receiving components are presented.
The lidar transmitter is based on a Nd:YAG laser (Continuum Powerlite 8010)
with second (532 nm: green) and third (355 nm: UV) harmonic generators. The
energy output is optimized for the exploitation of the UV Raman scattering.
Backscattered radiation is collected and analysed at four wavelengths of
interest: 532 and 355 nm for the elastic backscattering and 386.7 and
407.5 nm for Raman scattering of N2 and H2O molecules,
respectively. The characteristics of the transmitted beam are reported in
Table 1. In particular, it is noteworthy that the 355 and 532 nm beams are
collimated by means of 5× beam expanders, and, then, they are vertically
projected into the atmosphere through two 45∘ mirrors that can be
azimuth- and zenith-oriented through computer-controlled servomotors (Fig. 1).
Transmitter characteristics of the RMR lidar system.
Laser typeNd:YAGWavelength [nm]355–532Nominal energy per pulse [mJ]∼ 400 (355 nm), ∼ 200 (532 nm)Pulse duration [ns]7Pulse repetition rate [Hz]10Beam diameter dL [cm]5.0 (after the 5× beam expander)Beam divergence ΨL [mrad]0.1 (nominal, full angle afterbeam expander)Pointing stability [mrad]∼ 0.05
(a) Top view of the two 45∘ mirrors with azimuth and
zenith servomotors (components circled in red). (b) Schematic of the
mirror/beam movements.
The receiver is based on a multiple-telescope configuration allowing the
sounding of a wide altitude atmospheric interval:
one single 15 cm aperture telescope for the lower layers,
one single 30 cm telescope for the middle layers,
an array of 9×50 cm telescopes for the upper layers (∼ 1.7 m2 total collecting area, see Fig. 2).
The characteristics of the telescopes are reported in Table 2. For each of
the 11 telescopes, behind the field stop diaphragm (0.2, 0.4, 0.6, 0.8 mm
diameter for the 30 cm telescope, fixed 0.8 mm for the others) that is in
the focal position, there is a dichroic beam-splitting optical system that
separates the signals at λ<440 nm from the ones at
λ>440 nm and directs them in two different large-core
optical fibres, with 0.94 mm core diameter and 0.22 numerical aperture. In
this optical system (see Fig. 3 for a detailed description), a one-to-one
coupling of the field diaphragm on the optical fibre is obtained employing a
set of lenses (f=20 mm): first to collimate the radiation on the dichroic
mirror and then to focus the resulting different wavelength signals on the
input face of the respective fibre. Each receiving block has been aligned on
an optical bench before being mounted on the lidar system.
Telescope characteristics.
Collector 1Collector 2Collector 3Nine NewtoniansSingleSingleType of telescopearrayNewtonianNewtonianDiameter [cm]50 (each)3015Focal length f [cm]1509045f numberf/3f/3f/3FOV (full angle) ΨT [mrad]0.50.91.8
(a) Top view schematic of the relative position of the laser beams
(355 and 532) and the 11 telescopes. (b) Details of the emission and low-range telescopes (15 and 30 cm).
Schematic of the receiving block: (a) field stop diaphragm (0.8 mm
diameter), (b) collimation lenses (f=20 mm), (c) dichroic beam splitter
(λ=440 nm), (d) 45∘ mirror, and (e) optical fibre
connector. Dichroic beam splitter and UV collimation lens are currently not
present in the receiving block of the 15 cm telescope.
Field diaphragm, dichroic beam-splitting optical system, and SubMiniature version A (SMA) connectors
for the two optical fibre input faces are assembled in a small box supplied
with adjustments for lens focusing and dichroic mirror alignment. A system
of three orthogonal linear stages allows moving each box along the x, y, and
z axes by means of computer-controlled servomotors, to find optimal alignment
and focusing positions autonomously for each telescope.
A total of 37 servomotors (three for each telescope and two for each emitting
wavelength) are present. Two models of EOTECH Testine Micrometriche
Servocontrollate (TMS) are used: the TMS-25 for the movements in the
z-axis direction of the receiving system in the telescopes and the TMS-16 for
all other movements. Table 3 reports the nominal characteristics of the
employed servomotors. Each motor is controlled by a dedicated board. The
boards can be connected in a serial way to control more boards with a single RS-232
serial port. A set of three racks containing up to 14 boards is used
to control the motors through three serial ports. Motors belonging to a given
telescope or emitting mirror are grouped in a single rack: for this reason,
it is possible to control only one motor at once.
Two large carbon-fibre planes are utilized to support, respectively, the
telescopes (the lower ones) and the spiders holding the x–y–z motor-moved
stages with the dichroic boxes; fibreglass columns stick the two
planes together. With this architecture of the telescope-supporting frame, effects on
the alignment of thermal deformations are minimized. The receiving optical
system with the servomotors is depicted in Fig. 4.
In the current setting, for the smallest telescope (15 cm), only the optical
fibre carrying the signal return at λ>440 nm exits the
dichroic system, as this telescope is used only for the elastic
backscattering at 532 nm. Then, the optical fibres bring the light from the
telescopes to the photomultipliers (PMTs) after passing collimating lens and
interference filters that select the wavelengths of interests.
(a) Receiving optical system with the three axis servomotors (red
circled components). (b) Schematic of the receiving block movements.
Currently eight acquisition channels both in photon-counting mode as well as
analogue mode are implemented; Table 4 provides an overview of the RMR
channels with their associated telescopes and receiving wavelengths.
For standard measurement sessions the acquisition system is set to acquire
the photon-counting mode signals for 2000 bins with a 0.5 µs
integration per bin. Samples in the analogue channels are acquired at a
fastest rate, with 0.05 µs sampling rate, but they are averaged in
groups of 10 to have identical vertical resolution as in the counting
channels and, simultaneously, to improve the accuracy of the recorded data.
Thus, in the usual operation, the vertical resolution is 75 m (corresponding
to 0.5 µs bins) and the signals are generally integrated over 60 s (600 laser pulses) before recording.
Wavelengths and telescopes used for each currently
implemented channel.
The relative emitter–receiver geometry can be modelled knowing the
characteristics of emitters and receivers (Tables 1 and 2) and the distance
between the centres of each combination of emitter and receiver.
Summarizing, given
dcc, the distance between the centres of the laser beam and the telescope,
ΨL and ΨT, the divergence (full opening angle) of the
laser beam and the telescope, respectively,
dL and dT, the beam and telescope diameter, respectively,
and assuming
parallel vertical axes (beam and telescope FOV),
aperture in the focal plane (focus at infinity),
it is possible to calculate the following geometrical characteristics
relevant for the description of the overlap function O(R) (Stelmaszczyk et al., 2005):
R0=2dcc-dT-dLΨT+ΨL,R1=2dcc+dT+dLΨT-ΨL,
where R0 is the lowermost height at which the laser beam enters the
telescope field of view and R1 is the full overlap height (i.e. the
lowermost height with O(R)=1). As an example, Fig. 5 shows the case of the
15 cm telescope and 532 nm beam. These equations are equivalent to the ones
calculated from the diaphragm point of view (Halldórsson and Langerholc,
1978; Measures, 1984) taking into account the previously stated assumptions.
Table 5 completes the description of the geometry by reporting, for each
telescope, the telescope diameter (dT), the field of view (ΨT), and the distance from each emitting source (dcc532,
dcc355). Based on the nominal characteristics of the RMR system and
the analytical model described above (Eqs. and ), the values of
R0 and R1 have been computed for all implemented combinations of
emission laser wavelengths (355 and 532 nm) and telescopes. Results are
reported in Table 5. It has to be noted that the full overlap height can be
optimized by tilting properly the laser beam with respect to the telescope
axis (Kokkalis, 2017).
Characteristics, initial (R0) and full (R1) overlap heights
of the 11 telescopes for each emitted wavelength. Laser beam radius
dL=5.0 cm and divergence ΨL=0.1 mrad were used for the
calculations. The specifications of interest for this study (channel 1: 532 nm, 15 cm telescope) are highlighted in bold. NA = not available.
Schematic of the overlap between the telescope FOV (red) and the
laser beam (green). Full overlap is reached inside the cone delimited by the
dash–dotted lines. R0 and R1 heights are highlighted by the
horizontal lines.
A more realistic theoretical estimation of the whole overlap function is
possible. However, it requires accurate knowledge of the real
characteristics and positions of the optical parts of the system (e.g. beam
shape, relative inclination between the laser beam and telescope axis). The
estimation of these parameters needs a characterization of the lidar
emitting–receiving components that is often difficult to perform. The
proposed approach to characterize the geometry of the signal (Sect. 3)
allows an estimation of the overlap function (Sect. 4.2).
The following sections will focus on the characterization of channel 1 (532 nm, 15 cm telescope). This is the channel dedicated to the PBL sensing, for
which the knowledge of the overlap function is fundamental. The procedure
described is however applicable to all the remaining laser/telescope
combinations for quality control and signal optimization.
The mapping procedure
The mapping procedure takes advantage of the possibility of investigating the
dependency of the acquired signal S(R) from the relative transmitter–receiver
geometry by controlling the orientation of the laser beam and the 3D
position of the diaphragm of the receiving optical system around the focal
point of the telescopes. The procedure is based on a set of programs
controlling both the signal acquisition as well as the motor movements, and
it is fully defined by setting the following variables:
telescope/laser beam of interest
reference/starting position (x0, y0, z0 for the telescopes,
Az0, Zen0 for the laser beams);
range and regular step in each direction independently (i.e. number of
acquisitions);
channels to be acquired;
acquisition characteristics (e.g. duration, bin size).
Defining these parameters is a trade-off between having detailed and low-noise information and minimizing the signal variability introduced by
changes in the atmosphere and in the lidar system (e.g. laser power). To
minimize the atmospheric variability, the mapping procedure should be
preferably performed in stable meteorological conditions (e.g. end of the
night). However, strategies to monitor/account for these variabilities have
been implemented and will be discussed for each example of mapping reported.
The single telescope and laser mapping are described in detail in the
following subsections.
Telescope mapping
The telescope mapping procedure controls the position of the optical system
in all three axes. This procedure is implemented by performing, for a given
set of z positions, a series of acquisitions in the horizontal plane (x and
y directions). Each x–y plane is scanned starting from a reference position
(x0, y0) along a spiral path, in order to minimize the necessary
motor movements (see example in Fig. 6).
Example of telescope mapping geometry in the x–y plane (used for the first measurement session described in this work); x and y relative position of the servomotors in the respective axis.
(a) Expected radii for maximum (rd-ri) and partial
counts (rd+ri) in the signal map for an image of radius
ri and a diaphragm (solid line) of radius rd for ri<rd. (b) Schematic longitudinal view of radiation intensity near the
focal plane, diaphragm in a focused (1) and in an out-of-focus (2) position.
Given the relative dimension of the diaphragm and the optical fibre core
and assuming a well-aligned receiving box, the procedure presented in this
study takes into account only the characterizable effect of the field stop
diaphragm displacement.
(a) Simulated signal map for a diaphragm of radius rd=0.4 mm and an image of radius ri=0.2 mm. The signal is normalized by the
maximum value. (b) Result of a single-plane mapping performed with
misaligned optics; here the signal (normalized by the maximum value) is
asymmetrically clipped by the optical system between the telescope and the
photomultiplier.
Moving the diaphragm in the x–y plane on a fixed z for an image of radius
ri<rd, where rd is the diaphragm radius, approximately
constant counts are expected in a circle of radius rd-ri and a
decrease to zero counts within a radius rd+ri (Fig. 7a). This of
course under the assumption that all the signal passing through the
diaphragm is captured by the PMT. When the signal is clipped in the path
between the aperture and the sensor, the obtained mapping could be
asymmetric and could diverge from the expected shape. The resulting image
could also be affected by inhomogeneities in the PMT sensitivity (Freudenthaler, 2004); the use of optical fibres effectively
acts as a light scrambler minimizing the impact of this problem (Sherlock et al., 1999). Small imperfections in the
beam cross section, when the image is small and well-focused, should not
cause asymmetries in the resulting mapping.
For a fixed range R in lidar-acquired profiles, when changing the z
coordinate of the field stop/diaphragm, the image is expected to grow from
the minimum in the focused position following the enlargement of the circle
of confusion. If the image size is bigger than the receiving optical
component (e.g. diaphragm, optical fibre, lens), part of the signal will be
lost but the mapping will still be symmetric (Fig. 7b).
From the qualitative analyses of acquired signals from a single telescope
mapping, knowing the ideal behaviour, it is possible to diagnose deviations
from the nominal positions for all the optical components in the system not
taken into account by the simplified model. As an example, Fig. 8 depicts a
simulated single-plane mapping in case of good alignment and a real mapping
showing problems with the optical alignment (i.e. signal clipping in the
optical system between the telescope and the photomultiplier).
The information given by this type of mapping can be used to accurately
position the receiving optical system as shown in Sect. 4. Another
potential use of the information derived from the mapping is to estimate
unknown characteristics of the system.
As an example, the relative tilt between the field of view axis and the
laser beam can be computed once the centre of the image in the focal
plane (xc, yc) is found. For high ranges, this position corresponds to a
configuration with parallel beam and field of view axes. If the measurements
are done at different positions of the receiving system in the horizontal
plane, the relative tilt angle θtilt can be calculated for any
given position (x, y) with the following formula:
θtilt=D(xc,yc,x,y)f,
where f is the telescope focal length and D is the geometric distance between
the position x, y and the reference position xc, yc.
As previously mentioned, a trade-off between obtaining a reasonable signal-to-noise ratio (SNR) in
the range of interest and minimizing possible changes in the signals due to
atmospheric and system variability is needed.
In order to account for atmospheric/system variability, two approaches have
been tested:
If there is a channel acquiring information at the same wavelength of
the channel being mapped but through another telescope, a normalized signal
is obtained from the ratio of the profiles from the two channels. For
example, the normalized signal
S′(R)=SCH01(R)/SCH02(R) used for the mapping of channel 1 described in
the following section (Sect. 4.1) is defined as the ratio of the
simultaneously acquired measurements from channel 1 and 2 (Table 4).
In the absence of a suitable signal from a second channel, the signal profile
from the same channel, acquired periodically in a reference position (e.g. the position chosen with a previous alignment) during the mapping, can be
used to normalize the measurements within the interval of time between
acquisitions in the reference position. This approach is used in the laser
mapping (Sects. 3.2 and 4.2);
in this case the normalized signal was
calculated as S′(R)=SCH01(R)/SCH01ref(R).
Laser mapping
In the case of lidar systems with the capability of electronically
controlled azimuth and zenith orientation of the laser beam, an analogous
procedure can be implemented, leading to similar insights into the geometry of
the system.
For a given telescope-laser relative geometry the overlap function can be
estimated through a mapping performed varying the laser beam zenith and
azimuth angle. The lowermost range for which the overlap function can be
estimated depends on the characteristics of the system being required that
the laser beam can be tilted to have some position with full overlap.
Moreover, in order to define an absolute maximum with O(R)=1, the size of
the image has to be smaller than the diaphragm (i.e. the image is
sufficiently focused).
The scan is performed progressively, minimizing the necessary motor
movements (Fig. 9) and the subsequent delay between acquisitions. To monitor
changes in the atmospheric conditions and in the power output of the laser,
an acquisition in a reference laser position (e.g. the setup chosen with the
telescope mapping and used for routine measurements) is performed before and
after each zenith swipe. This is highlighted in Fig. 9, where at the
beginning and at the end of each column (i.e. zenith angle swipe) the laser
beam returns to the same pair of zenith and azimuth values.
Time-interpolated data acquired in the reference position
SCH01ref(R) are used to normalize the measurements during the
mapping.
(a) Laser mapping scan geometry, after each zenith scan a
measurement in the reference position (black square) is performed. (b) Schematic of the telescope FOV and laser beam for different orientations.
To estimate the overlap function, for each range value R, the maximum (or the
mean of the highest n values if the mapping is performed with sufficient
x–y resolution) of the normalized signal Smax′ is sought. Under the
previously stated assumptions, this value should represent the ratio of
a full overlap signal and SCH01ref affected by partial overlap.
Consequently, for a given range R, the overlap factor in the reference
position is 1/Smax′. We therefore calculate the overlap function O(R) for
each R between the minimum range with useful signal and the range in which
the full overlap is reached.
ResultsTelescope mapping and alignment
The objective of this telescope mapping session is to optimize the alignment
relative to the acquisition of 532 nm elastic backscatter by the 15 cm
telescope (CH01). This has been performed in two steps:
a preliminary mapping with a larger range and a coarser step in the three
dimensions to identify the sub-volume of optimal alignment;
a mapping in the sub-volume identified in the first mapping, close to the
optimal position and with finer resolution.
Overall view (a) and x–z section (b) of the results of the
telescope mapping of the first session (13 January 2021) showing the
normalized signal as a function of the x–y–z positions at a chosen range (15 µs – 2250 m a.g.l.).
Two steps are needed due to the time necessary to perform a scan with both
large x–y–z range and step. The first mapping could be skipped if the system
was recently aligned.
Signal mapping of the first session (13 January 2021) showing the
normalized signal for three planes at different z positions and chosen range
(15 µs – 2250 m a.g.l.). The position derived from the manual alignment is
highlighted by the black square.
The two sessions were performed on 13 January 2021 (18:01–23:04 UTC) and
on 26 January 2021 (17:02–20:01 UTC).
Table 6 reports the characteristics of the performed mapping.
Characteristics of the two telescope mapping sessions and the laser
mapping session; the channels used for the procedure are highlighted in
bold.
Mapping typeTelescopeTelescopeLaser13 Jan 2021,26 Jan 2021,15 Feb 2021,Time18:01–23:04 UTC17:02–20:01 UTC18:39–19:37 UTCMapped telescope/laser beam4 (15 cm)4 (15 cm)532 nmStarting/reference positionx, y, z/Az, Zen [mm]9.40, 10.60, 10.909.40, 10.60, 9.107.73, 7.97Range, step for x axis/Az axis [mm]0.8, 0.20.4, 0.10.21, 0.03Range, step for y axis/Zen axis [mm]0.8, 0.20.4, 0.10.09, 0.01Range, step z axis [mm]4.4, 0.42.0, 0.2Channels acquired1, 2, 4, 81, 2, 4, 81, 2, 4, 8Single acquisition duration [s]203030Bin size [µs]5.00.50.5Total mapping time2 h 52 min2 h 59 min58 min
With these settings, each acquisition for the mapping procedure took about
38 s of which 30 s are the effective acquisition time and 8 s are dedicated
to the data transfer and the movement of the motors that can be performed
one motor at a time.
From the results, which are depicted in Figs. 10 and 11, it is clear that the
telescope in the manually optimized configuration (x=9.40 mm, y=10.50 mm, z=10.50 mm, highlighted in Fig. 11a), despite being not far from the
optimal x–y position, is highly out of focus due to its z position. The overall
intensity of the signal in the x–y plane increases, changing the z position
(Fig. 11b), as the now focused image passes through the diaphragm without
being clipped. Moving further in the z axis, the signal starts again to
decrease (Fig. 11c). No clear asymmetries in the signal are found. The
presumed optimal position resulting from this session is x=9.40 mm, y=10.60 mm, z=8.50 mm.
The slight shift of the image centre (i.e. the centre of the area with maximum
values) at different z positions is caused by incoming light rays tilted with respect to the z axis. Moreover, looking at the uppermost useful range,
for x–y positions at the centre of the image the beam and telescope axis (more
precisely the FOV axis) should be approximately parallel. Assuming the z axis
as vertical, this information can be used to estimate the tilt of the laser
beam dividing the x–y shift of the centre of the image by the z displacement,
resulting in a tilt of ∼ 50 mrad.
Signal mapping (CH01 photon-counting signal) of the second
session (26 January 2021) at same z and different ranges (a: 5.0 µs – 750 m; b: 10.0 µs – 1500 m; c: 20.0 µs – 3000 m). Here the
signal is not normalized due to the lack of sufficient SNR from the second
channel in the lower range (highly incomplete overlap).
The second session was performed with finer steps and centred around the
presumed optimal position. In Fig. 12 is plotted the signal intensity S at
different ranges and a fixed z coordinate. In this case, the signal has not
been normalized due to highly incomplete overlap of the second channel in
the low range. The atmospheric and power variability has been monitored
qualitatively with channel-2 measurements at higher ranges. In the x–y plane,
as expected, the image shifts at different altitudes. No evident asymmetries
are present in the signal map.
Through this session, a definitive and well-aligned position can be selected
in the x–y plane as a trade-off between maximizing counts in the lower range
(optimizing the signal in the partial overlap range and lowering the full
overlap height) and maintaining the beam in the telescope FOV at high
ranges.
In the z coordinate, the optimal position is selected evaluating the
normalized signals at a medium range around the selected x and y position. As
shown in Fig. 13, the curve has a plateau in which the maximum value is
reached (i.e. the image is sufficiently focused and inside the diaphragm).
The selection of a position in the higher-value portion of the plateau
corresponds to a diaphragm position that better captures the signal in the
lowermost range (the focus shifts from infinity to lower ranges).
Based on the above considerations the derived optimal position is x=9.45 mm, y=10.55 mm, z=8.30 mm.
As mentioned in Sect. 3.1, measuring the angular distance between the
selected position and the centre of the image at high ranges (Eq. ), the
relative tilt between the telescope FOV axis and the laser beam is about 0.3 mrad.
Normalized signal at a chosen range (20 µs – 3000 m) for
different x and y positions as a function of z; data from both mapping
sessions (dashed line for the first session, solid line for the second). A
plateau is reached approximately between z=7 mm and z=9 mm. Each
colour corresponds to different x and y positions as indicated in the legend
of the plot.
Alignment validation
The selected alignment configuration was validated through a telecover test
and direct comparison of the signal profiles in the different positions.
The telecover test, described by Freudenthaler et al. (2018), is a quality assurance tool used for lidar system misalignment
diagnostic and evaluation of the full overlap range. Lidar profiles taken
with different sectors of the telescope aperture are compared to each other.
For well-designed and correctly aligned systems, the normalized signals
should only show differences in the partial overlap range.
Measurements were carried out during a single night session (2 February 2021, 18:55–21:26 UTC). Progressively the following measurements were
performed (see Fig. 14): a telecover test in the non-optimized starting
position (a), a full telescope measurement in the same position (b) and one
in the optimized position (c), and finally a telecover test in the optimized
position (d). Standard acquisition times were used (60 s), with an
integration time of 10 min for each telecover test sector or comparison
profile.
Profile comparison
Figure 15a shows the direct comparison of the background subtracted signal
profiles in the non-optimized starting position and in the optimized
position. Higher signal is found in the whole optimized profile
(>50 % relative normalized difference), confirming the
successful alignment procedure and no signal loss in the high range (Fig. 15b). The negative difference in the lowermost range is well below the full
overlap height (i.e. <500 m) and can be explained considering that
the large and less focused image of the non-optimized position can maintain
a partial overlap with the telescope FOV for a wider vertical range.
Validation measurement session (2 February 2021). Telecover (a) and full telescope measurement in the non-optimized position (b) and in the
optimized position (d and c, respectively).
Background subtracted counts (a) and relative difference between
the normalized signal CH01/CH02 profiles (b) in the selected position
(x=9.45 mm y=10.55 mm z=8.30 mm) and the starting one (x=9.40 mm
y=10.50 mm z=10.50 mm).
Telecover test results for the positions selected with manual (a) and mapping-based (b) alignment procedures.
Laser mapping signal (CH01 analogue signal before normalization
with the reference position signal) at three different ranges expressed as
delays at the top of the bin (a: 2.0 µs – 300 m; b: 3.0 µs –
450 m; c: 8.0 µs – 1200 m). The reference position (Az = 7.73, Zen =7.97) is highlighted by the black square. Measurements in this position
are acquired every zenith acquisition sequence and are represented in the
lower array.
Telecover
Two telecover tests were conducted: one in the non-optimized starting
position (Fig. 16a) and one in the optimized position (Fig. 16b). Figure 16a
shows that the height of full overlap is higher than 1500 m, far from the
expected modelled value of 501 m (see Table 5). Assuming a negligible impact
of mirror imperfections and the irregular shape of the laser beam, this has been
confirmed to be due to the diaphragm in an out-of-focus position for the z
axis. This leads to an image in the aperture plane with a large circle of
confusion and non-optimal alignment of the field of view (x and y axes).
Figure 16b depicts the results in the optimized position. The full overlap
height is around 1000 m or less, and the relative difference of the
signals in the partial overlap region has decreased. Atmospheric variability
and the presence of aerosol layers prevent a more precise evaluation of the
overlap height using the telecover QA method. As expected, less noise is
detected in all ranges due to the increased signal.
Laser mapping and overlap estimation
Once an optimized position has been selected and verified (see Sect. 4.1 and
4.2), a laser beam mapping with the purpose of estimating the overlap
function was performed (15 February 2021, 18:39–19:37 UTC). The
characteristics of the mapping are reported in Table 6. In Fig. 17 the
mapped analogue signal at three different ranges is shown. As in the
telescope mapping, a range-dependent shift of the signal away from the
reference position is visible in the lowermost range.
From this data, the overlap function using the methodology presented in Sect. 3.2 is calculated. The first five highest values are used for the calculation
of Smax′ at each range. In order to evaluate the possible impact of
the dead-time effect in the PC mode (Donovan et al., 1993; Cairo
et al., 1996), the analysis was performed also using data acquired in the
analogue mode. Figure 18 shows the estimated overlap function from analogue
(A) and photon-counting (PC) data; the resulting uncertainty is evaluated by the propagation of the signal uncertainties. As a reference, Fig. 18 also shows the overlap function computed with an analytical model with uniform beam
energy distribution (Stelmaszczyk et al., 2005) and a
Monte Carlo integration of Halldórsson and Langerholc (1978) equations with Gaussian beam energy distribution. Both models use the
relative inclination of the laser beam estimated via the telescope mapping
(see Sect. 4.1).
Estimated overlap function using analogue (A) and photon-counting
(PC) data compared to the overlap function calculated from models with the
assumption of a uniform/Gaussian beam, diaphragm in the focal plane, and
tilted beam (0.3 mrad).
The function O(R) reaches unity in the expected range and the experimental
results are in agreement with the models. Photon-counting data were corrected
for trigger delays (Freudenthaler et al., 2018) as
described in Appendix A. One evident feature is that after having reached
the maximum (at 500–800 m) the values start to slowly decrease. This
underestimation of the retrieved overlap function in the high ranges can be
explained by the methodology chosen for the maxima selection. In particular,
the systematic overestimation of the maxima Smax′ (due to the shot noise)
becomes relevant only above the range of interest (i.e. where O(R) has already
reached unity). The difference between the modelled/retrieved full overlap
height and the one found via the telecover test could be due to aerosol
variability in the latter or slight instabilities of the system
beam/telescope alignment and needs to be further investigated.
As an example of the application, Fig. 19 shows an uncorrected aerosol
backscattering profile and the corrected one using the PC-retrieved overlap
function applied from 200 to 500 m for channel 1 (11 February 2021,
19:45–20:44 UTC). Both the uncertainty associated with the overlap for the first four bins and the retrieval algorithm uncertainty have been taken into account for the corrected profile. Applying the overlap correction allows the
extension of the useful range of the aerosol backscattering profile down to 200 m.
Summary and conclusions
Taking advantage of the capability of the RMR 9-eyes lidar system to
electronically control, with motors, the orientation of the laser beam and the
position of the receiving optical system around the focal point of the
telescopes, a mapping procedure was developed to characterize the dependency
of the acquired signal from the system relative transmitter–receiver
geometry. The procedure consists in a set of programs controlling both the
signal acquisition as well as the motor movements. The developed approach
also includes solutions to account for atmospheric and laser power
variability likely to occur during the mapping sessions. The mapping
procedure allows applications such as the optimization of the telescope/beam
alignment and the estimation of the overlap function. It should be noted
that the results of the procedure can also be used to diagnose the overall
optical alignment and verify the adopted assumptions.
To optimize the RMR system for the objectives of ACTRIS/EARLINET (e.g. the description of aerosol optical properties in the lower troposphere
and PBL), this procedure was applied to the single combination of telescope
and laser beam (15 cm telescope, 532 nm) of the system that senses this region of the atmosphere better.
Another output of the procedure was the estimation of the absolute tilt of
the laser beam with respect to the z axis (∼ 50 mrad) and the
relative tilt with respect to the FOV axis (∼ 0.3 mrad). Such
values are fundamental to model the dependency of the signal from the system
geometry.
The presented methodology was tested to obtain an optimized laser-telescope
configuration starting from a non-optimized one. The mapping procedure
diagnosed an out-of-focus image and identified the correct z position. As a
result, the signal intensity increased in the whole channel 1 profile with
respect to the previous configuration. The effectiveness of the procedure
was verified comparing the results of a telecover test before and after the
alignment. The new configuration resulted in a lower full overlap height
(from 1500 m to less than 1000 m).
Aerosol backscattering coefficient (11 February 2021, 19:45–20:44 UTC); comparison between the overlap-corrected (solid black line) and
uncorrected (dashed red line) profile.
Once an optimized position has been selected and verified, a laser beam
mapping with the purpose of estimating the overlap function was performed.
The retrieved function was compared to the ones modelled using as input the
parameters obtained from the procedure showing good agreement. Correcting
the lidar profiles of channel 1 with this function allows extending the
useful range down to 200 m.
The developed mapping procedure will be applied to the remaining channels in
order to characterize each transmitter–receiver combination. Based on the
retrieved information it will be possible to define a set of configurations
aimed to satisfy the different scientific objectives (e.g. PBL, upper
troposphere–lower stratosphere). A simplified mapping procedure can be
used to complement the standard EARLINET quality assurance tests. For
example, a protocol coupling telecover test and mapping session are currently implemented in the RMR system. Monthly mappings are performed to
monitor and optimize the alignment and to estimate the overlap function,
whereas periodically required telecover tests (e.g. 1–2 times per year)
check and attest the obtained alignment and identify the minimum height with
full overlap.
Besides the applications presented in this study, a similar approach could
be adapted also to lidar systems with different hardware capabilities to
provide essential information about their transmitter–receiver geometry that
is needed for a complete characterization of the received signal.
The lidar photon-counting data range was corrected for trigger delay relatively
to the analogue data range using the following procedure. Assuming a linear
relation between photon-counting and analogue data (i.e. no saturation) and
after correcting the latter for voltage offset and dividing it by the
proportionality constant between the two, it is possible to write the
following equations:
A1A=fRR2,A2PC=fR-Δ(R-Δ)2,
where A is the corrected analogue signal, PC is the photon-counting signal,
f(R) is a function encompassing all the lidar equation terms apart from the
inverse square law, and Δ is the spatial delay between analogue and
photon-counting sampling ranges.
For low R a limited dependence of the function f on R variations with respect
to the inverse square law can be assumed. This is true especially for the
first bins where limited or no overlap is present and most of the signal
comes from laser secondary reflections and multiple scattering:
fR∼f(R-Δ).
Under this assumption, the ratio of the analogue and photocounting
signals is
APCR=(R-Δ)2R2,
from which it is possible to derive the difference Δ as a function
of R:
ΔR=-RAPC-1.
Figure A1a depicts the retrieved spatial delay computed with Eq. (A5)
using laser mapping data from Sect. 4.3. The assumptions hold for the first
two bins from which a delay of ∼ 25 m can be derived using an
average value. For higher ranges, the overlap function dependency from R
begins to be relevant, preventing the computation of the delay (i.e. the
assumption in Eq. (A3) is not valid anymore). This dependency is not
definite and partially randomized due to the variable emitting geometry and
resulting overlap function of the mapping lidar profiles. Once a delay is
retrieved the A/PC ratio can be plotted and compared with the range function
(R-Δ)2/R2 (Fig. A1b). As shown in Fig. A1b, the computed
range function represents the normalized ratio well.
Spatial delay (a) and ratio A/PC(b) as a function of range.
Code and data availability
Code and data are available upon request by contacting the authors.
Author contributions
MDP, GLL, and DD conceived the concept of this study. MDP, GLL, and DD
performed the lidar measurements. MDP developed the presented methods,
processed the data, and carried out the analysis. MDP, GLL, and DD
contributed to the interpretation, the validation, and the visualization of
the results. MDP wrote the original draft with input from all other
co-authors. All co-authors provided the review and the editing of the
paper.
Competing interests
The contact author has declared that neither they nor their co-author has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We acknowledge Sara Piermarini and Roberto Massimo Leonardi for the
development of the first versions of the motor control software. We also
acknowledge Raffaello Foldes for his contribution in the early development
of the mapping procedure. We thank Francesco Cardillo for the help in
performing lidar measurements.
Review statement
This paper was edited by Simone Lolli and reviewed by two anonymous referees.
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