From 23 January to 13 February 2020, 20 manned research
flights were conducted over the tropical Atlantic, off the coast of Barbados
(13∘30′ N, 58∘30′ W), to characterize the trade-wind
clouds generated by shallow convection. These flights were conducted as
part of the international EUREC4A (Elucidating the role of
cloud–circulation coupling in climate) field campaign. One of the
objectives of these flights was to characterize the trade-wind cumuli at
their base for a range of meteorological conditions, convective mesoscale
organizations and times of the day, with the help of sidewards-staring
remote sensing instruments (lidar and radar). This paper presents the
datasets associated with horizontal lidar measurements. The lidar sampled
clouds from a lateral window of the aircraft over a range of about 8 km,
with a horizontal resolution of 15 m, over a rectangle pattern of 20 km by
130 km. The measurements made possible the characterization of the size distribution
of clouds near their base and the presence of dust-like aerosols within and
above the marine boundary layer. This paper presents the measurements and
the different levels of data processing, ranging from the raw Level 1
data (10.25326/57; Chazette et al., 2020c)
to the Level 2 and Level 3 processed data that include a horizontal cloud
mask (10.25326/58; Chazette et al., 2020b)
and aerosol extinction coefficients (10.25326/59;
Chazette et al., 2020a). An intermediate level, companion
to Level 1 data (Level 1.5), is also available for calibrated and
geolocalized data (10.25326/57; Chazette
et al., 2020c).
Introduction
Subtropical regions play a major role in the radiation balance of the Earth
due to their dry free troposphere and their ability to emit a large amount
of heat to space (Pierrehumbert, 1995). Within the marine
boundary layer, these regions are associated with low-level clouds that also
contribute to cool the Earth through the reflection of sunlight. In the
trade-wind regimes, the prevailing clouds are shallow cumuli
(Norris, 1998). They are so ubiquitous that
their response to changes in the environment has the potential to greatly
influence the global radiation budget. In climate models, the differing
responses of these clouds to global warming has been identified as one of
the leading causes of uncertainty in climate sensitivity
(Bony and Dufresne,
2005; Brient et al., 2016; Medeiros et al., 2015; Vial et al., 2017). The
models that predict a significant decrease in shallow cumuli with warming
predict a higher climate sensitivity than the models that predict weak or no
change. To assess the credibility of climate projections, it is thus
necessary to understand how these clouds interact with their environment.
This was one of the main motivations of the EUREC4A (Elucidating the role of clouds-circulation coupling in climate) field campaign
which took place in January–February 2020 over the western tropical
Atlantic, west of Barbados (Stevens et al., 2020). This experiment was
originally designed to test our understanding of low-cloud feedbacks
(Bony et al., 2017),
especially the physical processes that control the cloud fraction around
cloud base, where climate models predict the largest changes in cloudiness
with warming. In addition, clouds in the trade-wind regimes exhibit
prominent forms of convective organization (Stevens et al. 2020), and the
mesoscale cloud patterns depend on environmental conditions and influence
the reflection of sunlight (Bony et al., 2020). The
question thus arises as to whether changes in the mesoscale organization of
clouds might play a role in low-cloud feedbacks
(Nuijens and Siebesma, 2019). Answering this question
constitutes another key objective of the EUREC4A campaign. To address
these issues, EUREC4A aimed at characterizing the field of trade
cumuli, in particular the horizontal cloud coverage around cloud base, the
spatial arrangement and the size distribution of clouds, through
complementary platforms and instruments, including airborne lidars.
Indeed, from a remote sensing point of view, shallow cumuli count among the
most challenging clouds. They are small, broken and sometimes very
optically thin, so their detection by radiometry can be difficult. In
contrast, lidars have the potential to detect them much better
(Liou and Schotland, 1971; Spinhirne et
al., 1982). Space-borne lidars associated with missions such as LITE (Lidar
In-space Technology Experiment, Winker et al., 1996), GLASS (Geoscience Laser
Altimeter System; Palm et al.,
2005; Spinhirne et al., 2005), CALIPSO (Cloud-Aerosol LIdar with
Orthogonal Polarization; Winker et al., 2003) or
more recently CATS (Cloud-Aerosol Transport System,
Yorks et al., 2016) have
even revolutionized our knowledge of the global distribution of clouds
(Berthier et al., 2008). However, cloud
observations from ground-based, airborne or satellite lidar technology were
made at the nadir or zenith. Due to the overlap of cloud layers, this can make
the observation of the cloud fraction around cloud base difficult. Moreover,
the laser beam is so thin that it can only sample a tiny fractional area of
the cloud field, especially in regions where the cloud fraction rarely
exceeds 10 %. To increase the areal sampling of the cloud field and
observe the cloud distribution at cloud base, EUREC4A introduced a new
sampling approach, consisting in using an aircraft carrying a
sidewards-staring lidar. This strategy was realized by implementing the
Airborne Lidar for Atmospheric Studies (ALiAS)
(Chazette et al., 2012b) with a
horizontal line of sight in the ATR-42 of SAFIRE (the Service des Avions
Français Instrumentés pour la Recherche en Environnment), using a
modified lateral window on the aircraft. A horizontally looking cloud radar
was also implemented on the same aircraft to complement the lidar
observations and benefit from the lidar–radar synergy for the detection of
clouds.
Horizontal lidar measurements have a great potential not only for the
observation of clouds, but also for the characterization of aerosols. During
the AMMA (African Monsoon Multidisciplinary Analysis;
Redelsperger et al., 2006) campaign,
Chazette et al. (2007) mounted a lidar on an ultralight aircraft and showed
that if the atmosphere is horizontally homogeneous along the line of sight,
horizontal shooting directly gives access to the extinction coefficient of
aerosols without any hypothesis on their nature
(Chazette
et al., 2007). The same approach was used during the Dust and Biomass
EXperiment (DABEX) with a combination between lidar
measurements from an ultralight aircraft and in situ measurements from the
UK FAAM aircraft (Johnson et al.,
2008). Therefore, during EUREC4A the horizontal lidar measurements made
from the ATR-42 were also used to characterize the marine boundary layer and
long-range transport of aerosols within the free troposphere.
The goal of this paper is to present the flight strategy, measurements, data
processing, and cloud and aerosol products derived from the horizontal lidar
measurements made during the EUREC4A campaign. Section 2 presents the
ALiAS lidar characteristics and Sect. 3 the implementation of the lidar in
the ATR-42 aircraft. The flight plan and its decomposition into different
phases are presented in Sect. 4. Section 5 describes the different levels
of data processing and the cloud and aerosol products that constitute the
final dataset. The conclusion is presented in Sect. 6 as well as how to
access the data.
Lidar characteristics
The ALiAS lidar was flown on board the ATR-42 (Fig. 1) of SAFIRE off the east coast of Barbados. Developed at LSCE (Laboratoire
des Sciences du Climat et de l'Environnement) following a precursor
instrument (Chazette et al., 2007), ALiAS is based on a frequency-tripled
Nd:YAG laser (ULTRA-100) manufactured by Lumibird Quantel emitting at the
wavelength of 355 nm. It satisfies eye safety requirements (EN60825-1) at
the output window considering the characteristics given in
Table 1 (emitted wavelength, pulse energy,
repetition rate, beam diameter and pulse duration). The UV pulse energy is
30 mJ and the pulse repetition rate is 20 Hz. The acquisition system is
based on a PXI-5124 (PCI eXtensions for Instrumentation) fast digitizer
working at 200 MHz and 12 bits, without going through a pulse (photon)
counter, leading to an initial resolution along the line of sight equal to
0.75 m. Using co- and cross-polarized channels relative to the linear
polarization of the emitted radiation, ALiAS was designed to monitor the
cloud, aerosol and hydrometeor distributions and dispersions in the low and
middle troposphere from aircrafts. It was successfully used on board the
Falcon 20 of SAFIRE to monitor and study the ash plume following the
eruption of the Eyjafjallajökull volcano
(Chazette et al., 2012b). The
main characteristics of ALiAS are given in Table 1.
ALiAS on board ATR-42 during the EUREC4A campaign.
Characteristics of ALiAS on board the ATR-42 during the
EUREC4A airborne campaign.
Wavelength355 nmPulse repetition rate20 HzPulse duration8 nsBeam diameter25 mmDivergence< 0.2 mradReception diameter150 mmFilter bandwidth0.2 nmField of view3 mradDetectorPhotomultiplierDetection modeAnalogueDigitalization12 bitsNative line-of-sight resolution0.75 mDimensions of the optical head45 cm (height), 28 cm (width), 18 cm (deep)Weight of the optical head∼ 15 kgWeight of the electronics∼ 20 kgPower supply220 V ACConsumption< 500 WImplementation in the aircraft
ALiAS was installed in the aft of the ATR-42 aircraft in an orientation that
enabled a direct near-horizontal line of sight. Its orientation was measured
the entire time by an inclinometer. The only possible solution for such an
implementation, in compliance with aviation regulation, i.e. without complex
modifications to the structure or aerodynamics of the aircraft, was to adapt
an optical window with a custom frame inside an existing passenger window
(Fig. 2). UV fused silica was chosen to ensure
correct transmission of several useful lidar wavelengths (355, 532, 830,
1550, 2000 nm) at affordable cost. The frame being 244 mm × 164 mm, a 20 mm
thickness was sufficient to ensure both a safety factor of ∼ 6
for mechanical resistance to air pressure difference and a wave front error
below λ/20 at 355 nm (Spark and Cottis, 1973). The
window flatness was specified to λ/4 at 633 nm, with an optical
coating of 315 nm of MgF2 to reduce theoretical reflection losses to around
4 %. The ∼ 15∘ inclination of the window due to
the curvature of the plane fuselage avoids harmful effects of the reflected
beam inside the lidar, as long as the receiving aperture is above the
emitting aperture, but extra beam tubing was found to be necessary to limit
the impact of diffuse echoes on the sensitive lidar detectors.
Location of the ALiAS lidar in the ATR-42 (a). The lidar is placed
horizontally (b) and the laser beam is guided to the MgF2 window (c) to
avoid laser reflections. Window (c) has replaced a passenger window (d) in
the back of the aircraft.
A specific study and certification were performed by SAFIRE itself to
install the window at the back of the ATR-42 aircraft, on the starboard
side. The optical head of ALiAS was already in a fibreglass container
adapted to aircraft operation. As shown in Fig. 1,
a standard aircraft-certified rack structure was fitted with a
carrying structure for this container, and the elements of the lidar
electronics were installed below, making the lidar system an easily mounted and
self-contained unit. It was operated in flight from a passenger sitting in
front of an in-flight checkpoint, allowing real-time validation of the cloud
base altitude sampling.
Flight strategy
The flight strategy was defined well before the intensive campaign and
presented in Bony et
al. (2017). It has been adapted to take into account the ATR-42 autonomy and
the coordination with the other platforms involved in EUREC4A. The goal
being to achieve a statistical sampling of the cloud fields, each flight
repeated more or less the same flight plan, twice a day, independent of
weather conditions.
On a given day of operation, the ATR-42 generally performed two flights,
with each flight having a duration of ∼ 4 h. The take-off time of
the ATR-42 was tightly coordinated with that of the High Altitude and
Long-Range Research Aircraft (HALO) operated by DLR (Deutsches Zentrum
für Luft- und Raumfahrt). The endurance of HALO (∼ 9 h)
allowed the two ATR-42 flights to be conducted within the timeframe of a
single HALO flight, taking into account the time for refuelling at Grantley
Adams International Airport (GAIA) in between ATR-42 flights. Most of the
ATR-42 flying time was spent off the east coast of Barbados within the
so-called HALO circle, along which HALO released dropsondes and observed the
atmosphere at nadir with a radar, a lidar and multiple radiometers
(Stevens et al., 2019).
The flight strategy is illustrated in Fig. 3 for
the flight on 26 January 2020. It was built along five major phases (see
Table 2), which contributed to the multi-aircraft
and statistical sampling strategy implemented during the field campaign.
Note that during straight-line flights, the typical speed of the aircraft
was ∼ 100 ms-1. Each phase was designed to address
particular scientific requirements of the lidar and other remote sensing and
in situ instruments composing the ATR-42 payload (radar, aerosol and cloud
microphysics, water vapour stable isotopes using cavity ring-down
spectrometry, turbulence).
On the way to the HALO circle, the ferry time was dedicated to perform an
aircraft sounding up to 2.5–4.5 km above mean sea level (a.m.s.l.) to
describe the vertical thermodynamical and dynamical structure of the lower
atmosphere and obtain a first guess of the location of cloud and aerosol
layers. Such an aircraft sounding was aimed at retrieving aerosol extinction
coefficient and volume depolarization ratio profiles from the lidar
measurements (see Sect. 5.3.2) and assess whether the upper part of the
sounding was conducted in aerosol-free and/or cloud-free conditions. It is
worth noting that several episodes of dust transport from West Africa were
evident from the lidar data during the campaign.
Upon arriving in the HALO circle, the ATR-42 started performing two or three
north–south-oriented rectangles (roughly orthogonal to the trade winds),
approximately 130 km long and 20 km wide. Each rectangle was flown in 45–50 min. The northwestern and southwesternmost corners of the rectangle were
positioned 10 Nm (1 Nm = 1.852 km) to the west of the HALO circle. In the
event that the ATR-42 circuit only included two rectangles, they were
always performed around the cloud base height (CBH). When the circuit included
three rectangles, on some occasions, the ATR-42 performed the first
rectangle near the altitude of the ferry, mainly to sample stratiform clouds
near the inversion level or the air just above. In such cases, the
sideways-pointing lidar ALIAS allowed the characterization of the
variability of aerosol-related extinction within the HALO circle in
cloud-free conditions, or was used to obtain a cloud mask and further
statistics on the properties of stratiform clouds, whenever they were
present at the altitude of the flight. The second and third rectangles were
always performed at CBH, to collect statistics on the spatial distribution
of marine boundary layer clouds, measure the cloud base cloud fraction and
provide a cloud mask. Figure 3 shows an example of a
flight plan during which the three rectangles were performed at CBH on 26
January. On one occasion (on 9 February) the ATR-42 circuit comprised four
rectangles performed at CBH (see Table 2),
After the rectangles, the ATR-42 performed two long L-shaped legs (of 20–25 min each) below CBH, one near the top and one near the middle of the subcloud layer.
The first part of the L-shaped leg consisted of a ∼ 70 km long
east–west-oriented run (approximately parallel to the mean trade winds), and
the second part of the L, also ∼ 70 km long, was oriented
perpendicularly to the first part. The return trip along the L-shaped legs
was generally performed at the same altitude (see
Fig. 3). These legs were essentially designed to
characterize the turbulent structure of the marine boundary layer. In our
case, they also allowed the characterization of the extinction and polarizing
capability of aerosols present in the marine boundary layer,
At the end of the return trip along the second L-shaped run, the ATR-42
generally performed an ultra-low pass at 60 m above sea level for
∼ 10 min in order to measure turbulent heat fluxes and marine
aerosols within the lower part of the planetary boundary layer.
The ATR-42 cruised back towards Barbados around 3 km a.m.s.l. (see
Fig. 3). Meanwhile, the aircraft soundings allowed
a second retrieval of aerosol extinction coefficient and volume
depolarization ratio profiles which were used to assess how the geometrical
and optical properties of aerosol layers evolved in the course of the
flight.
Flight strategy of ATR-42 loading ALiAS for flight no. 04 on 26
January 2020. The five phases of the flight are highlighted.
The flight strategy was sometimes slightly adjusted based on the
meteorological situation, e.g. depending on the presence of a stratiform
cloud layer near the trade inversion level. The details on the ATR-42 flight
blocks (rectangles, L-shaped legs, surface legs) are given in
Table 2. It should be noted that prior to the
beginning of Phase 2, a best-guess estimate of the CBH, assessed from
multiple sources of information (radiosoundings launched from BCO (Barbados Cloud Observatory) and
research vessels, dropsondes released from other aircraft), was provided to
the ATR-42 scientists via the on-board chat capability. The flight altitude
was then adjusted using real-time lidar echoes, cloud droplet counts from
cloud microphysics probes, and visual observations by the pilots and the
lidar operator through lateral windows.
Main flight blocks (rectangles, L-shaped legs, surface legs) for the
ATR42 flights as well as flight dates.
The data are presented from their raw form to the analytical products. They
are classified into Level 1 to Level 3 as defined in
Table 3. Up to Level 2 (included), lidar profiles
are processed on an individual basis. The statistics performed on Level 2
data are gathered in the Level 3 data. Statistics are computed for all
flights for the aerosol Level 3 products and for Phase 2 of the flights
for the cloud products. The step from Level 1 data to the final products of
Level 2 and Level 3 is schematized in Fig. 4.
This section presents the steps taken to derive data products. The data
recording format is detailed in Sect. 6. It should be noted that for
Level 1 to Level 2, the location and attitude of the aircraft are also
reported for each horizontal lidar profile.
Lidar data processing diagram starting from raw data (1) and
calibrated data (Level 1.5) to products (Level 2 and Level 3). The grey
cells summarize the actions to be implemented for the data processing. The
green colour refers to data level in the pre-processing phase. Level 2 and
Level 3 are subdivided into cloud (blue) or aerosol (orange) products.
Data level with their type and main derived products.
Data levelData typeMain products1Raw geolocalized dataRaw profiles recorded by the acquisition system1.5Range corrected lidar dataBackground radiance (BR) Overlap function (F) Apparent backscatter coefficient (ABC) calibrated linear volume depolarization ratio (VDR)2Inverted dataCloud mask associated with each profile Aerosol extinction coefficient (AEC)3Statistical dataProbability density functions of cloud width (PDF) Mean vertical profile of AECLevel 1Description
Level 1 data are raw data expressed in volts. They are the result of time
sampling at a frequency of 200 MHz. The raw sampling of the lidar profiles
is 0.75 m along the line of sight, and an average over 50 shots is performed
during the acquisition, corresponding to about one recording every 5 s (2.5 s averaging time and 2.5 s recording time). The first 2000 points are
recorded before the laser emission is triggered. This offset makes it possible
to record for each profile the contribution of the background sky radiance
(BR) to the lidar signal (scattering of solar radiation in the atmosphere).
This contribution must then be corrected during pre-processing.
The lidar signal S, for each polarization channel, of Level 1 data is
expressed in the measurement configuration adopted for EUREC4A as
Sx,z=Cx2⋅Fx⋅βmz+βaz+βcx,z⋅exp-2cosθz⋅τmx+τax+τcx,z+BR(z).
In this expression, the signal S depends on both the horizontal distance to
the aircraft x and the flight altitude z. The system constant C is a function
of various components of the lidar system such as the emitted energy and the
quantum efficiency of the detectors
(e.g. Shang and Chazette, 2015). The overlap factor F characterizes the overlap between the transmission and
receiving fields of view and must be determined to exploit near-field data.
As the laser beam propagates through the atmosphere, it is backscattered by
air molecules (subscript m in the following), aerosols (subscript a) and/or
clouds (subscript c) towards the receiving system. This interaction is
characterized by the volume backscattering coefficient βk (k= m, a or c). The laser radiation is also attenuated by the atmospheric medium
via the same actors, and this attenuation is quantified by the optical
thickness τ, which is defined as a function of the extinction
coefficient αk by the relation
τkx,z=∫0xαk(x′)⋅dx′.
Equation (1) assumes that the optical properties of molecules and aerosols remain
constant along the line of sight. A deviation from this assumption can be
easily verified on Level 1.5 data, as will be shown. In the presence of
clouds, the heterogeneity is too strong for this hypothesis to be true.
As the laser beam emitted from the aircraft may not be completely
horizontal, a viewing angle θ (with respect to the true horizon)
must be taken into account. In addition to Level 1 data, the aircraft
attitude parameters (pitch, roll, heading) that allow the assessment of θ
are recorded, as well as the geo-positioning of the measurements (longitude,
latitude and altitude).
Baseline check
Baseline distortion can significantly increase the rates of non-detection
and/or false detection of cloud structures. Hence, in Level 1 data,
potential drifts of the lidar signal baseline are checked for each flight,
in order not to introduce any bias during data processing, mainly in the far
field (i.e. beyond 4–5 km). This is done by comparing the BR from the
pre-trigger with that computed in the far field, where the laser backscatter
contribution becomes negligible, beyond 8 km in our case. As an example,
Fig. 5 shows the scatter plots of the BR computed
on all the lidar profiles for the two channels of ALiAS on 26 January 2020.
There is a little more spread on the parallel channel because it is more
energetic than the perpendicular channel and the contribution of the laser
can still exist beyond a horizontal distance of 8 km. Nevertheless, it is
noticeable that for both channels the scatterplot data points are aligned
along a straight line with a slope of 1, so there is no noticeable deviation from
the baseline over the whole useful distance range (between 0 and 8 km).
Verification of the linear behaviour of the relationship between
the background sky radiance (FC) computed for the pre-trigger (FCpt) and
far field (beyond 8 km in horizontal distance, FCff) for (a) the parallel
and (b) the perpendicular channels. The example presented is from flight F03
on 26 January 2020.
Level 1.5Description
To build Level 1.5 data, the raw sampling along the line of sight has been
degraded in order to ensure the independence of each point on the horizontal
lidar profile. The final resolution is then 15 m. The ALiAS-derived
Level 1.5 data are then profiles corrected from both geometric factor and
solid angle of detection. They are also corrected for molecular transmission
via the molecular optical thickness τm to produce the apparent
backscatter coefficient (ABC, also referred to as attenuated backscatter
coefficient) which is expressed as
ABCx,z=Sx,z-BR(z)⋅x2Fx⋅exp2cos(θ(z))⋅τm(x)︸molecular transmission.
An example of the ABC for the flight on 28 January 2020 is given in
Fig. 6. The ABC decreases away from the plane
because of attenuation by aerosols and clouds (passing from orange to green
in Fig. 6a). In parallel with the ABC profiles, the
volume depolarization ratio (VDR) is calculated from the two polarized lidar
channels according to a procedure explained in
Chazette
et al. (2012a, b). The relationships are recalled below. They take into
account the transmissions of the parallel polarization of the two Brewster
plates used: T0∥ for channel 0 (T0∥≈0.45) and T1∥ for channel 1 (T1∥≈0.40). The signals on the two lidar channels contain a contribution of the
complementary polarization. The VDR is then expressed as a function of the
ratio of the gains Rc of the two channels.
VDR(xz)≈T1//⋅S⊥x,z-BR⊥Rc⋅S∥x,z-BR∥-1-T0∥⋅1-T1∥
with
Rc≈S⊥(x,z)-BR⊥⋅T1∥S∥(x,z)-BR∥1-T0∥⋅1-T1∥+VDRm
The molecular volume depolarization ratio VDRm is equal to 0.3945 % at
355 nm (Collis and Russel, 1976). The term 1-T0∥⋅1-T1∥
measures how the lidar system is affected by imperfect separation of
polarizations. The laser residual cross-polarization of 0.002 can be
neglected for ALiAS. The calibration of the depolarization consists in
estimating Rc from measurements in a molecular atmosphere, above any
aerosol layer. The flight of 25 January around Barbados was dedicated to
this calibration with an excursion of the aircraft above 4.5 km a.m.s.l. The
calibration obtained is shown in Fig. 7a. The
variability of Rc is less than 2 %, which leads to an absolute error
on the VDR of the order of 0.2 %. It is verified a posteriori that there is little
aerosol at the calibration altitude, as shown (Fig. 7b) by the vertical profile of the aerosol extinction coefficient for the
flight considered (see Sect. 5.3).
Example of the apparent backscatter coefficient (ABC) for the
flight F05 on 28 January 2020, for the first rectangle of Phase 2. The lidar
data in (a) are presented as a nearly horizontal map of ABC (with each data
point being geo-localized in space as a function of latitude, longitude and
altitude) which is used to identify the clouds within the rectangle ABCD
described by the ATR-42. Panel (b) shows the same data as a function of longitude
and distance from the aircraft. The clouds are colour-coded in white in (a)
and brown in (b).
(a) Calibration coefficient Rc of the volume depolarization
ratio (VDR) derived from flight altitude above 4.5 km on 25 January 2020
(flight F04). (b) Vertical profile of the average aerosol extinction
coefficient (AEC) with its root-mean-square variability (RMS) for the flight
range on 25 January 2020. It corresponds to the Level 3 aerosol product. The
aerosol optical thickness (AOT) is also reported.
Overlap factor
The overlap factor corresponds to the overlap between the laser beam and the
field of view of the telescope. It is equal to 1 when the two fields
completely overlap and leads to a geometric attenuation of the lidar when
the overlap is partial. It can be computed from horizontal shots as
previously performed for ALiAS during flights with an ultralight aircraft
(Chazette et al., 2018). This
calculation requires an homogeneous atmosphere along the line of sight of
the lidar over a distance of about 1.5 km from the aircraft. To ensure this
homogeneity, we performed the calibration at high altitude during the flight
of 25 January 2020, above 4.5 km a.m.s.l., where the scattering is
essentially molecular. The overlap factor of the two ALiAS channels is given
in Fig. 8. It is similar for both channels beyond
300 m distance from the emission. Compared to the theoretical overlap factor
due to purely geometric effects, it shows a slight bump which is related to
a non-zero angle of incidence on the interference filters of the lidar, for
rays coming from the far field. This small deviation is nevertheless
corrected for Level 2 and Level 3 processing.
Overlap factor of ALiAS on board ATR-42 during EUREC4A.
Level 2 and Level 3
Level 2 data are products provided for each individual lidar profile, for
both cloud detection and calculation of the aerosol extinction coefficient
(AEC) along the horizontal line of sight. Level 3 data result from
statistics on Level 2 data.
Cloud productsDescription of Level 2 cloud products
Cloud detection is applied to the lidar data acquired during Phase 2 of
the flights (rectangles). It is the basis of the Level 2 cloud dataset. For
each lidar ABC profile, it uses a threshold approach as already considered
for lidar measurements at nadir
(Chazette et al., 2001;
Shang and Chazette, 2014). The threshold is proportional to the standard
deviation of the noise of the cloud-free signal during Phase 2 of a given
flight. Although the coefficient of proportionality Ce is constant, the
threshold varies with the distance from the aircraft owing to the decrease
in the signal-to-noise ratio (due to the increase in the clear-sky noise)
away from the aircraft. As for the aerosol products, a lidar profile is
considered to be cloud-free if the logarithm of the ABC can be considered linear with a relative error of less than 10 % (see Sect. 5.3.2). The
threshold is then calculated at a constant altitude (around the cloud base
height, where molecular and particle scattering can be considered constant)
and when the angle of the lidar line of sight with the horizontal does not
exceed 3∘ (the mean value is 1∘ and the standard
deviation is 0.5∘). Lidar profiles acquired during ATR-42 turns
are therefore excluded from the cloud Level 2 data.
Figure 9a shows the evolution of the cloud-free
lidar signal averaged over Phase 2 and the associated standard deviation
along the horizontal line of sight for flight F05 on 28 January 2020. The
standard deviation increases very rapidly with distance, just as the ABC
decreases. The cloud detection was tested for different values of the
coefficient Ce ranging from 1 to 8. The cloud mask turned out to be
fairly insensitive to the value of Ce as long as Ce ranges from 2
and 4. To construct the Level 2 data, we choose Ce= 2.5.
(a) Average apparent backscatter coefficient (ABC) per 500 m distance range for all cloud-free profiles of Phase 2 for the flight F05 on
28 January 2020. The standard deviation (STD) is also reported. (b) Binary
cloud detection matrix derived from ALiAS measurements along the horizontal
line of slight for the flight F05 on 28 January 2020, for the first
rectangle of Phase 2. The cloud mask is based on a cloud detection that uses
Ce= 2.5, D=30 m and Lmin= 45 m. It is part of Level 2 cloud
products.
Figure 9b shows that the cloud density decreases with the distance from the
aircraft, especially beyond 3–4 km. This results from two different effects:
as the distance from the aircraft increases, (1) the threshold for cloud
detection increases (mostly because the magnitude of the noise increases; see
Fig. 9a), and (2) the probability for the laser beam to be attenuated
increases if multiple clouds are present along the laser line of sight. In
general, one can be confident in the detection of semi-transparent cloud
layers over the first 3–4 km. Beyond that, cloud detection is still
possible, especially when there are no significant scattering layers (such
as a dense aerosol plume) between the laser source and the cloud, but with a
higher uncertainty on the detection of the cloud edges and thus the cloud
depth. The presence of dense clouds that cannot be traversed by the laser
beam will lead to an underestimate of the cloud cover and a negative bias on
the average cloud depth. At cloud base, such clouds were only present on a
few days during the campaign (e.g. F07, F12, F17, F18 and F19).
Two additional parameters are considered for the cloud detection that can
potentially be adjusted. First, we consider that two cloudy points separated
by clear sky correspond to two distinct clouds only if they are separated by
a distance of at least D (in other terms, two cloudy points separated by
clear sky but less than D apart will be considered to be part of the
same cloud). Recognizing that trade-wind cumuli can be very small and close
to each other (e.g. Zhao and Di Girolamo,
2007), we chose D=30 m. Second, to avoid interpreting as a cloud a peak
of the signal that would arise from noise, we impose that a cloud
corresponds to a segment of adjacent cloudy points (along the line of sight)
longer than a certain threshold referred to as Lmin. The width Lmin
is more difficult to estimate. We use Lmin=45 m to eliminate
isolated peaks (one to two points only) of the lidar profiles that result from
noise and strongly influence the statistics of cloud detection beyond 3–4 km. The two parameters D and Lmin are tuneable, and the points of the
cloud mask affected by these parameters are flagged in a quality indicator.
Level 2 products also include the distance d0 beyond which the lidar
signal (ABC) can be considered undistinguishable from noise (10
consecutive points within noise after the last cloud point detected). This
distance is located in a non-cloudy part of the horizontal lidar profile. It
is worth noting that the ABC of clouds is more than an order of magnitude
greater than that of clear air and that the lidar signal can be in the noise
at d0 while still showing the presence of a cloud at a greater distance.
Illustration of the cloud detection procedure on the apparent
backscatter coefficient (ABC) during flight F11 on 5 February 2020
(10:13:29). Two clouds are detected that correspond to successive points
for which the ABC exceeds the ABC threshold (in red). An isolated peak is
not considered to be a cloudy point. The distance d0 at which the ABC can
be considered to be embedded in the noise is reported. The blue dotted line is
the cloud-free ABC for Phase 2 of flight F11. The standard deviation of
the cloud-free ABC is also reported (blue vertical bars). At each distance,
the threshold for cloud detection is defined as Ce times the threshold
value.
Number of clouds detected along the horizontal line of sight of
the lidar that correspond to different cloud widths during Phase 2 of
flight F05 on 28 January 2020 (Level 3 cloud product). Also reported
(right-hand-side vertical axis) is the probability distribution function of
cloud widths for the clouds detected at horizontal distances from the
aircraft ranging from 0.1 to 8 km (black solid line) and from 3 to 8 km
(red solid line). The picture illustrates the type of cloud field sampled
during this flight.
Figure 11 shows an example of the detection of cloud structures on one of
the lidar profiles of flight F11 (2020-02-05 10:13:29). Two clouds are
detected at a distance of about 0.9 and 3.8 km from the ATR-42. They
correspond to segments composed of at least three successive points for which
the ABC exceeds the threshold value. On the other hand, despite their ABC
larger than the threshold, the segments shorter than Lmin or the
“isolated peaks” are not considered to be cloudy points. The distance d0 is
reported around 4.2 km.
Description of Level 3 cloud products
Level 3 cloud products consist of probability distribution functions (PDFs)
of cloud chords along the laser line of slight computed during Phase 2 of
the flight. It is worth noting that owing to the integration and acquisition
time of the lidar measurement (5 s) and the aircraft speed
(100 ms-1), we are unable to derive a cloud mask along the direction
of aircraft motion (the minimum distance we can resolve along this direction
is 500 m, which is roughly the upper bound of the cloud chords measured
along the line of sight of the lidar). The cloud mask distributed in the
Level 2 and Level 3 datasets thus corresponds to the cloud detection done along
the line of sight of the lidar only. If clouds were homogeneously
distributed within the field of view of the lidar, and perfectly detected by
the lidar, similar PDFs would be inferred whatever the distance from the
aircraft. Figure 11 shows the cloud width histogram
derived at cloud base during flight F05 on 28 January 2020. The distribution
obtained for the whole field of view of the lidar (clouds detected for
horizontal distances between 0.1 and 8 km) is compared to the distribution
obtained for clouds detected between 3 and 8 km from the aircraft. The good
match of the two PDFs shows that, from a statistical point of view, the
cloud detection is not biased with the distance from the aircraft, at least
for cumulus cloud fields composed of optically thin clouds (also referred to
as “sugar” patterns; Stevens et
al., 2020). In the case of flight F05, the mean cloud width is about 130 m with a standard deviation of 80 m.
The cloud detection quality indicator/flag
Level 2 cloud product also includes a binary quality indicator (or flag)
coded with “1” and “0” over 6 bits, denoted Qflag. This indicator is
defined in Table 4. It takes into account for each
range gate along the lidar line of sight (i) the detection or not of a cloud
(bit 1); (ii) the aggregation or not of nearby cloud structures separated by
less than D=30 m (bit 2); (iii) the detection of narrow cloud structures
(cloud width along the line of sight <Lmin=45 m), which can
be considered signal noise and which are not considered as clouds (bit
3); (iv) the vertical positioning with respect to the horizontal (Δz) of the cloud point, which depends on the angle between the line of slight
and the horizontal (bits 4 and 5); and (v) visual information on the level of
soiling on the external face of the aircraft window crossed by the laser
beam. In order to simplify its re-reading by users, the indicator is
converted into real numbers in Level 2 files. Before being used, it must be
converted back to binary. For example, the real number 52 corresponds to the
binary number “110100”.
Cloud detection quality indicator (Qflag) defined on 6 bits.
The AECs are the second Level 2 and 3 products derived from the horizontal
line of sight of the ALiAS lidar. The Barbados area is a region where a very
wide variety of aerosols can be found. The main ones are marine aerosols to
which can be added terrigenous aerosols and even biomass burning aerosols.
It has been known for decades that these terrigenous aerosols mainly
originate from West Africa and that their concentration over Barbados is
marked by a strong seasonality (Prospero, 1968) with a
maximum during the boreal summer. Dust aerosols are carried across the North
Atlantic by trade winds (Trapp et al.,
2010) and their concentration depends on the meteorological conditions over
both Africa and the tropical North Atlantic Ocean. Main studies on desert
dust aerosols have been conducted on the basis of dust events in Barbados
whose sources were located more than 5000 km away over the western Sahara
(e.g.
Haarig et al., 2017; Trapp et al., 2010). Although this type of event occurs
rarely in winter, during several flights, we observed strong AEC values
associated with a significant depolarization signature. Terrigenous aerosols
were actually observed for about half of the ATR flights during EUREC4A
(Table 5).
The process for determining the AEC from horizontal lidar measurements was
first described in
Chazette
et al. (2007). The horizontal configuration allows the direct measurement of the
AEC, by measuring the exponential attenuation of the signal, provided the
atmosphere is sufficiently homogeneous over a few kilometres, i.e. in
clear-sky air (αnz=0). Under the
conditions of the field experiment, in order to limit the effect of both the
signal noise and the overlap factor, the calculation of the AEC is performed
by linear regression on LnABC(x,z) in the range from 0.2 to
1 km away from the aircraft. The slope of the regression line is equal to
-2αaz and is given by (Chazette,
2020)
αaz=-12∂LnABC(x,z)∂x.
Only AECs associated with a relative regression error of less than 10 %
are retained. This avoids cloud-contaminated profiles in the regression
range. The determination of the AEC is direct, without any hypothesis on the
nature of the aerosol. In order to limit the effect related to a deviation
from the horizontal, profiles with angles to the horizontal greater than
10∘ are removed. It should be noted that an angular deviation of
15∘ induces an error of 0.01 km-1 on the AEC. The mean VDR
(VDR‾(z)=1/0.8∫0.21VDR(x,z)⋅dx) is also calculated over the same distance range as
the AEC and is part of the aerosol Level 2 data.
Level 3 aerosol data consist of average AEC and VDR profiles calculated
over each entire flight. Standard deviations on the AEC and VDR are
associated with them. It was chosen to discretize the atmosphere with
altitude steps of 100 m for these mean profiles.
Figure 12 shows the evolution of the AEC and VDR
over the entire flight F07 on 31 January 2020 (Level 2 products). The
aerosol loading is significant during Phase 2 of the flight, where cloud
detection is performed. AECs of ∼ 0.3 km-1 and even
higher are observed. These values should be compared to the background
values which are well below 0.1 km-1. VDRs are also high, above 2 %,
which is the signature of terrigenous particles in the atmosphere
(e.g. Flamant et al., 2018).
Aerosol optical properties derived from ALiAS measurements along
the horizontal line of slight on 31 January 2020 (flight F07): (a) aerosol
extinction coefficient (AEC) and (b) volume depolarization ratio (VDR) which
correspond to Level 2 aerosol products.
Available dataOverview of available data
The ALiAS system has been successfully operated during the 20 flights of the
EUREC4A field campaign from 23 January to 13 February 2020. The related
dataset is summarized in Table 5. Flights where the
lidar sampled a significant number of clouds (1000) are highlighted in
bold font. The mean value of the AEC and its standard deviation informs on
the amount of aerosols encountered during Phase 2 of each flight
(Fig. 3). Note that Phase 2 was not carried out
during the test flight on 23 January 2020.
General flight characteristics of ATR-42 when operating ALiAS. The
mean, standard deviation and maximum value of the aerosol extinction
coefficient (AEC) and the volume depolarization ratio (VDR) for each Phase 2
(Fig. 3) of each flight are reported. The flights
in bold font are those associated with the detection of many clouds. The italic font is used for values of the VDR. The
comment “Strong presence of dusts” corresponds to VDR > 2 %
and the comment “Presence of dusts” corresponds to 1 % < VDR
< 2 %. Flights with a reduced detection range due to window
clogging by dusts and/or sea salt aerosols are indicated by “X”.
FlightDateStart and end timeAltitude rangeAEC ± SD (km-1)Comment(dd/mm)(UTC, HHMM)(km)max(AEC) VDR±SD (%) max(VDR) during Phase 2F0123/011900–21000.06–3.5Test flightTest flightF0225/011330–17450.3–4.80.03±0.03 0.24 0.3±0.11.1–F0326/011200–16000.06–4.5No aerosol dataModified field of viewF0426/011700–21000.06–2.60.02±0.02 0.1 0.8±0.10.9–F0528/011615–20500.4–3.20.06±0.04 0.3 0.5±0.12.9Presence of dustF0630/012030–00450.3–3.20.09±0.10 0.5 1.4±0.53.2Presence of dustF0731/011500–18450.3–3.250.14±0.06 0.6 2.1±0.22.7Strong presence of dustF0831/011945–24000.3–3.250.20±0.08 0.7 2.2±0.33.2Strong presence of dust XF0902/021145–15450.3–3.250.14±0.06 0.5 3.0±0.64.6Strong presence of dustF1002/021645–21000.06–3.250.16±0.04 0.4 2.7±0.43.7Strong presence of dustF1105/020845–13000.06–3.250.13±0.08 0.87 1.4±0.12.1Presence of dust
Continued.
FlightDateStart and end timeAltitude rangeAEC ± SD (km-1)Comment(dd/mm)(UTC, HHMM)(km)max(AEC) VDR±SD (%) max(VDR) during Phase 2F1205/021345–18150.06–3.250.13 ± 0.07 0.53 1.4±0.21.8Presence of dustF1307/021130–15450.06–3.250.06±0.04 0.36 0.4±0.32.1–F1407/021700–21450.06–3.250.04±0.04 0.27 0.3±0.2 0.7–F1509/020445-09000.06-4.40.18±0.10 0.53 0.6±0.10.9XF1609/021400–18150.06–4.50.18±0.07 0.55 0.9±0.21.5XF1711/020600–10300.25–4.50.15±0.16 1.2 0.7±0.11.1–F1811/021130–16000.06–40.19±0.13 0.92 1.0±0.2 1.4Presence of dust XF1913/020730–11450.06–3.250.09±0.08 0.39 0.6±0.32.3–F2013/021300–17300.06–2.50.05±0.04 0.37 0.6±0.42.1–Files format
For each flight, data are available within the database as NetCDF files
(version 4) for the four levels of processing described in Sect. 5. The
Level 1 NetCDF file contains raw data recorded during the whole duration of
the flight. It contains all the scalar and time-dependent parameters needed
to properly process the signal recorded by each lidar channel. The Level 1.5
NetCDF file contains pre-processed lidar profiles of ABC and VDR along the
lidar line of sight, as a function of time. Also provided are the distance
from the aircraft (time dependent) and flight attitude, localization, and
altitude useful for data geo-localization.
Level 2 and Level 3 are concatenated into one single NetCDF file, separately
for cloud and aerosol products. The aerosol Level 2 and 3 NetCDF file
contains cloud-free AEC individual values with the corresponding altitude,
time and geo-localization parameters and the mean vertical profile of AEC
within the altitude range of the flight, respectively. The cloud
Level 2 and 3 NetCDF file contains the ABC used in the detection algorithm of
clouds, a binary cloud detection array (cloud mask) and a quality flag
array. All-three are given as a function of the distance from the aircraft
and are restricted to the rectangle flight patterns of Phase 2 and profiles
with roll–pitch angles close to 0∘. The Level 2 and 3 NetCDF file
also includes the probability density functions (PDFs) of the cloud widths
encountered during Phase 2 of the flight. PDFs are computed along the
horizontal lines of sight for distances ranging between 0.1 and 8 km, and
between 3 and 8 km, to check for the consistency of measurements in the near
and far fields.
Data availability
The entire dataset is published open access on the AERIS database
(https://en.aeris-data.fr/, last access: 12 November 2020). The digital object identifier (DOI) for Level 1
and Level 1.5 data is 10.25326/57 (Chazette et al., 2020c). For Level 2 and 3 data, it is
10.25326/58 for the cloud products (Chazette et al., 2020b) and 10.25326/59 for the aerosol
products (Chazette et al.,
2020a). The typical sizes of the different NetCDF files are (i) ∼ 195–420 Mb for Level 1 data, (ii) ∼ 11–29 Mb
for Level 1.5 data, (iii) ∼ 4–18 Mb for Level 2&3 cloud
products and (iv) ∼ 60–190 Kb for Level 2&3 aerosol
products.
Summary
An airborne sidewards-staring lidar was implemented on board the ATR-42 for
the EUREC4A field campaign. Twenty flights were conducted from 23
January to 13 February 2020 over the west Atlantic Ocean tropical region,
off the coast of Barbados. The horizontal line of sight of the lidar allowed
us to characterize horizontal fields of shallow cumuli with a much better
sampling than would have been the case with nadir or zenith measurements.
This new dataset will make it possible to analyse the macroscopic properties
of shallow cumuli near cloud base for a range of meteorological conditions
and mesoscale organizations. It will also offer a baseline measurement to
assess the value of future space-borne missions as the forthcoming Earth
Clouds, Aerosols and Radiation Explorer mission (EarthCARE;
Illingworth
et al., 2015) and to evaluate the realism of the new-generation climate
models. Aerosol optical parameters were also derived; biomass burning and
dust aerosol plumes were present during the field campaign. The data have
been classified according to the level of numerical processing applied: (i) Level 1 data are the raw horizontal lidar profiles, (ii) Level 1.5 data are the
calibrated lidar profiles corrected from system characteristics, (iii) Level 2 data are the geophysical parameters directly derived from the
individual profiles and (iv) Level 3 data are the synthesis of these
parameters. Level 2 and Level 3 data have been combined in the same NetCDF
files. All these data are available on the AERIS database
(https://en.aeris-data.fr/, last access: 12 November 2020).
Author contributions
PC participated in the field experiment
on board ATR-42; analysed the data; developed the algorithms for the
Level 1.5, Level 2 and Level 3 datasets; and wrote the paper. JT, AB and CF participated in the field experiment on board
ATR-42 and contributed to the paper writing. SB coordinated the
project, participated in the field experiment on board ATR-42 and
contributed to the paper writing.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Elucidating the role of clouds–circulation coupling in climate: datasets from the 2020 (EUREC4A) field campaign”. It is not associated with a conference.
Acknowledgements
The idea of trying horizontal lidar measurements to
characterize clouds at cloud base was suggested to us by Bjorn Stevens at
the outset of the EUREC4A project. The authors gratefully acknowledge
Jean-Christophe Canonici, Jean-Christophe Desbios, Thierry Perrin, Laurent
Guiraud and all the technicians, engineers, pilots and director from SAFIRE,
the French facility for airborne research (http://www.safire.fr, last access: 12 November 2020), and
airplane delivery, for making the preparation of the ATR and the
EUREC4A airborne operations possible. We thank the Caribbean Regional
Security System (RSS) for hosting the ATR and the ATR team in Barbados
during the experiment; David Farrell and the Caribbean Institute for
Meteorology and Hydrology (CIMH) for their logistical and administrative
support; and the Department of Civil Aviation in Barbados and Andrea Hausold
(from DLR), for their help and support of airborne operations. The authors
also thank AERIS for their support during the campaign and for managing the
EUREC4A database. The authors are thankful to the three anonymous referees whose
comments helped improve the overall quality of the paper.
Financial support
The EUREC4A project was supported by the European Research Council (ERC) under the European Union’s Horizon 2020
research and innovation programme (grant agreement no. 694768), with some additional support from the French Space Agency CNES through the EECLAT proposal.
Review statement
This paper was edited by Gijs de Boer and reviewed by three anonymous referees.
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