AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-3359-2017Tropospheric ozone profiles by DIAL at Maïdo Observatory (Reunion Island): system description, instrumental performance and result comparison with ozone external data setDuflotValentinvalentin.duflot@univ-reunion.frBarayJean-Luchttps://orcid.org/0000-0003-4711-6310PayenGuillaumeMarquestautNicolasPosnyFrancoisehttps://orcid.org/0000-0001-5115-3646MetzgerJean-MarcLangerockBavoVigourouxCorinneHadji-LazaroJuliettePortafaixThierryDe MazièreMartineCoheurPierre-FrancoisClerbauxCathyCammasJean-PierreLaboratoire de l'Atmosphère et des Cyclones (LACy), UMR8105, Saint-Denis, Réunion, FranceObservatoire des Sciences de l'Univers de La Réunion (OSUR), UMS3365, Saint-Denis, Réunion, FranceLaboratoire de Météorologie Physique (LaMP), UMR6016, Observatoire de Physique du Globe de Clermont-Ferrand, CNRS - Université Blaise Pascal, Clermont-Ferrand, FranceRoyal Belgian Institute for Space Aeronomy (BIRA-IASB), 3, Av. Circulaire, 1180, Brussels, BelgiumLATMOS/IPSL, UPMC Univ. Paris 06 Sorbonne Universités, UVSQ, CNRS, Paris, FranceSpectroscopie de l'Atmosphère, Service de Chimie Quantique et Photophysique, Université Libre de Bruxelles (ULB), Brussels, BelgiumValentin Duflot (valentin.duflot@univ-reunion.fr)15September2017109335933739December201625January201713July201724July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/3359/2017/amt-10-3359-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/3359/2017/amt-10-3359-2017.pdf
In order to recognize the importance of ozone (O3) in the troposphere
and lower stratosphere in the tropics, a DIAL (differential absorption lidar)
tropospheric O3 lidar system (LIO3TUR) was developed and
installed at the Université de la Réunion campus site (close to the
sea) on Reunion Island (southern tropics) in 1998. From 1998 to 2010, it
acquired 427 O3 profiles from the low to the upper troposphere and has
been central to several studies. In 2012, the system was moved up to the new
Maïdo Observatory facility (2160 m a.m.s.l. – metres above mean sea
level) where it started operation in February 2013. The current system
(LIO3T) configuration generates a 266 nm beam obtained with the fourth
harmonic of a Nd:YAG laser sent into a Raman cell filled up with deuterium
(using helium as buffer gas), generating the 289 and 316 nm beams to enable
the use of the DIAL method for O3 profile measurements. The optimal
range for the actual system is 6–19 km a.m.s.l., depending on the
instrumental and atmospheric conditions. For a 1 h integration time,
vertical resolution varies from 0.7 km at 6 km a.m.s.l. to 1.3 km at
19 km a.m.s.l., and mean uncertainty within the 6–19 km range is between
6 and 13 %. Comparisons with eight electrochemical concentration cell
(ECC) sondes simultaneously launched from the Maïdo Observatory show good
agreement between data sets with a 6.8 % mean absolute relative
difference (D) between 6 and 17 km a.m.s.l. (LIO3T lower than ECC).
Comparisons with 37 ECC sondes launched from the nearby Gillot site during
the daytime in a ±24 h window around lidar shooting result in a
9.4 % D between 6 and 19 km a.m.s.l. (LIO3T lower than ECC).
Comparisons with 11 ground-based Network for Detection of Atmospheric
Composition Change (NDACC) Fourier transform infrared (FTIR) spectrometer
measurements acquired during the daytime in a ±24 h window around lidar
shooting show good agreement between data sets with a D of 11.8 % for
the 8.5–16 km partial column (LIO3T higher than FTIR), and comparisons with
39 simultaneous Infrared Atmospheric Sounding Interferometer (IASI)
observations over Reunion Island show good agreement between data sets with a
D of 11.3 % for the 6–16 km partial column (LIO3T higher than IASI).
ECC, LIO3TUR and LIO3T O3 monthly climatologies all exhibit
the same range of values and patterns. In particular, the Southern Hemisphere
biomass burning seasonal enhancement and the ozonopause altitude decrease in
late austral winter–spring, as well as the sign of deep convection bringing
boundary layer O3-poor air masses up to the middle–upper troposphere in
late austral summer, are clearly visible in all data sets.
Introduction
Because of its interaction with solar and terrestrial radiation, ozone
(O3) is an important contributor to the Earth's radiative balance, and any
changes in its atmospheric distribution contribute to the radiative forcing
of climate change (Lacis et al., 1990). O3 is also an important
pollutant and impacts the oxidative capacity of the atmosphere (Martin et
al., 2003). In the troposphere, the O3 budget is influenced by transport
from the stratosphere, by in situ photochemical production associated with
O3 precursors emitted by anthropogenic activity, biomass burning,
lightning and surface deposition (Stevenson et al., 2006).
Reunion Island is a tropical island located in the south-western part of the
Indian Ocean at 20.8∘ S and 55.5∘ E. It is seasonally
impacted by biomass burning plumes transported from southern Africa, South
America and south-eastern Asia which can significantly affect the free
tropospheric concentrations of O3 and other pollutants like CO (Edwards
et al., 2006 ; Duflot et al., 2010). Moreover, it is affected by
stratospheric intrusions associated with the dynamical influence of the
subtropical jet stream (Baray et al., 1998; Clain et al., 2010) and the
tropical cyclone deep convection (Leclair De Bellevue et al., 2006).
The barrier effect and dynamical exchanges between the tropical reservoir and
midlatitudes and vertical exchanges between the troposphere and
the stratosphere, affect the O3 balance and distribution in both the
troposphere and stratosphere and are then of great interest in the
documentation of climate change. Tropospheric O3 measurements are
performed routinely on Reunion Island by O3 sondes at the Gillot site
(see Fig. 1 and
Table 1) since 1992 (in the framework of the Network for the Detection of
Atmospheric Composition Change, NDACC, since 1996 and of the Southern
Hemisphere ADditionnal OZone sondes, SHADOZ, network since 1998) and by lidar
at the Université de la Réunion campus site (see Fig. 1 and Table 1)
since 1998 (Baray et al., 1999, 2006).
To improve the ability of the ground-based remote sensing instruments to
probe the upper-troposphere/lower-stratosphere (UT/LS) region, a high
atmospheric facility was built in 2012 at the summit of the Maïdo
mountain (see Fig. 1 and Table 1), and most of the instruments previously installed
close to the coast at the Université de la Réunion campus site were
moved up to this new facility in the year 2012 (Baray et al., 2013). Being
inside the boundary layer during the day and most of the time inside the free
troposphere during the night (except during the warm and rainy season), the
Maïdo Observatory is dedicated to the investigation of the boundary layer
composition and processes (especially in the framework of the Global
Atmospheric Watch network – GAW), as well as to the study of the low–middle
atmosphere (especially in the framework of the NDACC). Four lidar systems are
permanently deployed and routinely operated at the Maïdo Observatory:
a Doppler wind lidar dedicated to the study of the middle atmosphere dynamics (Khaykin et al., 2016);
the LIO3S, a lidar dedicated to stratospheric O3 measurements (Portafaix et al., 2003, 2016);
the LI1200, a lidar dedicated to tropospheric water vapour (Hoareau et al., 2012; Dionisi et al., 2015; Vérèmes et al., 2017) and stratospheric-mesospheric temperature measurements (Morel et al., 2002; Keckhut et al., 2004, 2015; Sivakumar et al.,
2011a);
and the LIO3T lidar (Baray et al., 1999, 2006; Clain et al., 2009, 2010; Vérèmes et al., 2016), dedicated to the observation of tropospheric O3 (as well as aerosols from the free troposphere up to the lower stratosphere).
It is noteworthy that the LIO3T system was very recently affiliated in the
NDACC for O3 measurements. This paper aims to provide a technical
reference socle for further use of the O3 data provided by the LIO3T
system: we first present the data processing, then give a brief historical
review of the tropospheric O3 lidar system when installed at the
Université de la Réunion campus site (1998–2010) together with a
description of the current LIO3T system installed at the Maïdo
Observatory. We show comparisons between the LIO3T O3 measurements and
O3 external data set. We finally present an overview of the lidar
tropospheric O3 profiles database.
In the following, the system will be “LIO3TUR” when referring to
its installation at the Université de la Réunion, and the current
system (installed at the Maïdo Observatory) will be referred to as
“LIO3T”.
Map showing the locations of the different measurement sites
(Maïdo Observatory, Gillot and the university on Reunion Island) and
instruments (LIO3TUR, ECC, FTIR and LIO3T) used in this study.
Coordinates and distance to Maïdo Observatory from the observation
sites used in this study.
The programme used to calculate the O3 profile, uncertainties and
resolution is adapted from the stratospheric O3 programme DIAL (differential absorption lidar), which
has been described and intercompared by Godin et al. (1999) and is currently
used for the stratospheric DIAL O3 retrievals at Reunion Island (NDACC
affiliated).
Lidar equation
The lidar DIAL technique (Hinkley, 1976)
relies on the difference between two backscattered lidar signals at two
different wavelengths, one where O3 is strongly absorbed (ON, here
289 nm) and the other one where O3 absorption is weaker (OFF, here
316 nm). The O3 number density nO3(z) at altitude z (in
molec cm-3) is retrieved from the Rayleigh lidar signals according to
the following equation (Harris et al., 1998):
nO3(z)=-12ΔσO3(z)ddzlnP(λON,z)-B(λON,z)P(λOFF,z)-B(λOFF,z)+δnO3(z),
where ΔσO3(z)=σO3(λON,z)-σO3(λOFF,z) is
the differential O3 absorption cross section, P(λi,z) is the
number of detected photons, B(λi,z) is the background noise and
detector noise, and δnO3(z) is a correction term corresponding to
the absorption by other constituents of the atmosphere, expressed as follows:
δnO3(z)=1ΔσO3(z)12ddzlnβ(λON,z)β(λOFF,z)-Δσatm(z)natm-∑igΔσig(z)nig(z)].β(λi,z) is the coefficient of extinction of the
molecules and particles, Δσatm(z) and natm the
differential cross section and the density of the atmosphere, respectively,
and Δσig(z) and nig(z) the differential cross section
and the number density of interfering gas, ig, respectively. According to
Leblanc et al. (2016b), the interfering gases that should be considered in practice are
NO2, SO2 and O2. NO2 and SO2 are negligible in most
cases of tropospheric O3 retrieval, except in heavy volcanic aerosols
loading conditions. The absorption by O2 should be considered if any of
the detection wavelengths are shorter than 294 nm (which is the case here as we
use the 289 nm wavelength). However, in our retrieval, we do not take into account
any interfering gases for the time being. It is part of our
plans to include them in the DIAL code. The background light, the
saturation of the detector and the noise from detectors must be added to
Eq. (2).
Saturation, correction and vertical resolution
The saturation is defined as the phenomenon in which the amount of output
signal is no longer proportional to the incident light intensity. It is a
non-linear phenomenon, depending on the dead time of the detector. In the
LIO3T case, due to the detector sensitivity and the geometry of the
instrument, we found that saturation occurs only below 7 km. To correct it, we
apply the scheme described in Pelon (1985, Annex 2):
Nc=1+1-τδtNr-1e-τδtNr,
with Nc the number of photons counted, Nr the number
of photons received, τ the dead time of the detector and δt the
integration time.
The vertical resolution is directly linked to the filtering of the lidar
signal. For LIO3TUR, the signal was filtered using a Taylor
derivative filter together with a polynomial low-pass filter of the order of 2, and
for LIO3T, we filter the signal with the Savitzky–Golay derivative filter of
the order of 2, also called the least-squares smoothing filter (Savitzky and Golay,
1964). To take into account the decreasing signal-to-noise ratio with
altitude, the number of points of the used filters (for both
LIO3TUR and LIO3T) increases with altitude (and, consequently,
the vertical resolution decreases with altitude, see Sect. 3.2 and Fig. 3). To calculate the resulting vertical resolution, the frequency approach
detailed in Leblanc et al. (2016a) is used.
LIO3T instrumental schema.
Uncertainty
Uncertainty calculations for DIAL O3 retrievals are described in
Leblanc et al. (2016b). The most significant sources of uncertainties are
found to be the detection noise, the O3 cross section uncertainties and
the background noise.
Using our acquisition card in photon-counting mode, we calculate the
detection noise by assuming that the signal's standard deviation is equal to
that which is expected for a Poisson statistical distribution of detected
photons. The corresponding uncertainty is thus estimated directly from the
signal intensity (Leblanc et al., 2016b – Eqs. 28 and 29).
O3 cross sections from Molina and Molina (1986) and Bass and Paur (1984) were
used for O3 profile retrievals for LIO3TUR and LIO3T,
respectively, both with an uncertainty equal to 5 %.
The background noise includes the background light, which is
altitude independent, and the detector noise – dark noise and induced signals
– which are altitude dependent. We extract the background noise from the
lidar signal by fitting the uppermost part of the lidar signal using a linear
or polynomial regression function and by subtracting the result from the
signal.
To take into account the propagation of these errors in the lidar equation,
and assuming that all uncertainties are independent, we follow the approach
detailed by Leblanc et al. (2016b – Eq. 4 with no covariance term).
Instrumental description and performanceHistorical context and main instrumental features
A Rayleigh–Mie scattering lidar was first installed at
the Université de la Réunion campus site in 1993 to monitor
stratospheric and mesospheric aerosols in the southern tropics. From 1993 to
1998, the lidar system evolved both in terms of emission and reception
(Nd:YAG laser replacement, mosaic telescopes addition, polarization channels
installation, infrared channel reception set up) to improve aerosol detection
and characterization and to allow stratospheric–mesospheric temperature
measurement.
In 1998, an extension was installed on the existing system to perform O3
measurements in the free troposphere, including the upper troposphere. Baray
et al. (1999) give a complete description of the LIO3TUR and
provide justifications of the technical choices that were made at this time.
Note that the first “home-made” acquisition chain was exchanged for a LICEL
one in 2007, but this exchange did not cause significant differences in the
profiles acquired.
In late 2012, the Maïdo Observatory new facility was complete and
the fixed lidar systems were moved from the Université de la Réunion
campus site and installed in the Observatory. Since temperature measurements
are now performed with the LI1200 system – also dedicated to water vapour
measurement (Dionisi et al., 2015; Vérèmes et al., 2017) – the
previous LIO3TUR was modified into a system dedicated to the
measurement of tropospheric O3 (and aerosols): the LIO3T.
Figure 2 sketches the experimental schematic of the O3 DIAL part of the
LIO3T and gives its main technical characteristics. The LIO3T mainly relies
on the LIO3TUR design (Baray et al., 1999). We use the same
approach to generate a 266 nm beam going through a deuterium-filled Raman cell
(using helium as buffer gas), shifting the incoming frequency to 289 and 316 nm
signals. The backscattered photons are collected by the same
4 × 500 mm telescope mosaic focusing on 1.5 mm diameter optical fibers. Hamamatsu
R9880-110 and R7400P-03 photomultiplier tubes are used for 289 and 316 nm
channels, respectively. Further details on the LIO3T features can be found in
Baray et al. (2013).
The detection and characterization of the tropospheric
aerosols by the LIO3T is currently performed using the emitted 532 nm
“residual” beam, a 200 mm telescope for reception of the elastic signal and
a polarization detection system. This aerosols detection wing of the LIO3T
will be the subject of dedicated studies.
Performance
The LIO3TUR was only operated at night to increase the
signal-to-noise ratio (Kovalev and Eichinger, 2004). Due to the overlap
factor (the height at which the telescope's field-of-view and laser beam
overlap completely and above, where it remains constant) and detection limit,
the LIO3TUR optimal range was 3.5–17 km above mean sea level
(a.m.s.l.) (Baray et al., 1999). Note that in the following all altitudes
will be given as a.m.s.l. Figures 3 and 4 give the mean vertical resolution
and uncertainty profiles for LIO3TUR over the 13 years of
operation. The temporal resolution (or integration time) depended on the
atmospheric conditions (i.e. the cloud-free sky duration) and varied roughly
between 40 min and 3 h. The vertical resolution varies from 0.1 km at
3 km to 1.8 km at 17 km. The mean uncertainty varies from
≈ 6 % (≈ 3.8 ×1010 molec cm-3) at
3 km to ≈ 15 % (≈ 7 ×1010 molec cm-3) at 16 km and increases up to 60 %
(≈ 3.5 ×1011 molec cm-3) at 17 km (not
shown) where the detection noise dominates.
The altitude of the Maïdo Observatory being 2160 m, the transfer of the
tropospheric O3 DIAL system from the university (80 m) to this location
increases the upper limit of the profile probed, but also increases the lower
limit: the optimal range is now 6–19 km. The free troposphere, the tropical
tropopause layer (TTL) and lower stratosphere are thus covered by the current
system. It is worth mentioning, however, that depending on experimental
conditions (lidar alignment, stability of emitted power at the transmitted
wavelength, atmospheric conditions, etc.), the validity domain can vary from
one day to another.
Similarly to the LIO3TUR, the LIO3T is only operated at night to
increase the signal-to-noise ratio and twice a week in routine conditions
(i.e. out of campaigns). We use three main integration times: 20 min for
night-time series, 1 h for comparison with collocated ECC soundings (1 h is roughly the time for the balloon to travel the troposphere), and
≈ 3 h (between ≈ 2 and ≈ 4 h, depending on
the clear-sky time duration) for full night-time profiles. Figure 3 also shows
the vertical resolution resulting from each of these integration times for
LIO3T. For the 20 min integration time, the resulting resolutions are 0.9
and 1.6 km at 6 and 19 km, for the 1 h integration time
they are 0.7 and 1.3 km at 6 and 19 km, and for the 3 h integration time they are 0.3 and 1.2 km. The difference between the
LIO3TUR and LIO3T vertical resolutions results from the use of
different filters and numbers of points for the signal filtering (see Sect. 2.2).
Mean vertical resolution of LIO3TUR profiles (dashed
green curve) and LIO3T profiles for integration times greater than 1 h
(black curve), equal to 1 h (red curve) and 20 min (blue curve).
Mean uncertainties in % (a) and
molec cm-3(b) of the LIO3TUR profiles (dashed
green curve) and LIO3T profiles for integration times greater than
1 h (black curve), equal to 1 h (red curve) and equal to 20 min (blue
curve).
Figure 4 also shows the mean uncertainties for LIO3T for the three main
integration times in % (panel a) and molec cm-3 (panel b). Mean
uncertainty varies between ≈ 7 % (≈ 6 ×1010 molec cm-3) at 6 km and ≈ 5 %
(≈ 5.5–8 ×1010 molec cm-3) at 19 km with a
peak at ≈ 10 % (≈ 5 ×1010 molec cm-3), ≈ 12 % (≈ 6 ×1010 molec cm-3) and ≈ 15 %
(≈ 7.5 ×1010 molec cm-3) at 16 km for the
> 1 h, 1 h and 20 min integration times, respectively. These figures
are in agreement with the recently published work of Leblanc et al. (2016b),
showing uncertainty profiles for a 2 h DIAL tropospheric O3 measurement
between 7 and 11 %. One can notice that, above 16 km, the
LIO3TUR uncertainty increases and is greater than the LIO3T one.
By contrast, the LIO3T uncertainty decreases (in %) between 16 and 19 km.
This can be explained by the fact that LIO3TUR reaches its
detection limit between ≈ 16 and ≈ 17 km (where the
detection noise dominates), while for LIO3T the increase in the detection
noise is balanced by the increase of the O3 abundance when entering the
stratosphere.
The main benefit of the instrument altitude change from 80 to 2160 m is
that it enables the UT/LS region to be documented with relevant vertical and time
resolutions together with a reasonable uncertainty (1.5 km, 20 min and
10 % at 18 km).
Comparisons of LIO3T measurements with O3 external data set
The goal of this section is to validate the LIO3T O3 measurements by
comparing them to the O3 external data set. Four types of correlative data
are used here: eight collocated ECC soundings (i.e. launched from the Maïdo
Observatory during a lidar shooting), 37 routine NDACC/SHADOZ ECC soundings
performed during the daytime at the Gillot site (see Fig. 1 and Table 1), and
Fourier transform infrared spectrometer (FTIR) tropospheric partial columns
measurements from both daytime ground-based (12 comparison pairs) and
night-time Infrared Atmospheric Sounding Interferometer (IASI) (39 comparison
pairs) data.
In the following, we compare N LIO3T O3 measurements
MLIO3T with N correlative data MCD by calculating
the mean absolute relative difference between data sets D (in %) defined
as follows:
D=1N∑n=1N|rn|,
with rn the relative difference (in %) between two observations
MLIO3Tn and MCDn defined as follows:
rn=100⋅MLIO3Tn-MCDnMLIO3Tn+MCDn2.
Comparison with ECC
ECC sondes measure the oxidation of a potassium iodine (KI) solution by O3
(Komhyr et al., 1995). Their precision is 5–10 % throughout the
troposphere and TTL (Smit et al., 2007) and they are commonly used for the
validation of ground-based and space-borne O3 observations. Here below,
we compare LIO3T O3 profiles with both collocated Maïdo ECC
soundings and Gillot SHADOZ/NDACC routine daytime ECC soundings. All these
ECC profiles are generated following the “Guidelines for homogenization of
ozonesonde data” (Smit et al., 2012). The Gillot SHADOZ/NDACC reprocessed
ECC data set was recently presented by Posny et al. (2016) and Witte et
al. (2017) and is used in this article. Moreover, similar reprocessing was
applied to the ECC soundings performed at the Maïdo Observatory. From
August 2007 to December 2016, ECC soundings were performed at Reunion Island
using the ENSCI/0.5 % full buffer solution instead of the standard half
buffer. This specificity of the Reunion Island ECC soundings is not taken
into account in the SHADOZ/NDACC reprocessed ECC data set yet. Following the
work of Johnson et al. (2002, 2016), who intercompared various KI and buffer
solutions, we found that this ENSCI/0.5 % full buffer solution tends to
overestimate the amount of O3 by 1.7 % on average in the
troposphere. Consequently, an adapted correction was applied to the ECC
profiles acquired during this period.
(a) Mean LIO3T O3 profile (red curve) and mean ECC
profile (blue curve) measured during the eight intercomparison measurements
performed at Maïdo. The dashed lines give the 1 standard deviation around
the mean. (b) Mean r between the LIO3T and ECC profiles (red
curve), mean LIO3T uncertainty around zero (black dashed lines) and mean
LIO3T uncertainty and ECC precision around zero (black lines). The red dashed
lines give the 1 standard deviation around the r mean. The green line
(upper x axis) gives the number of LIO3T profiles used for comparison.
Same as Fig. 5 for NDACC/SHADOZ Gillot ECC soundings and full
night-time LIO3T profiles.
Figure 5 shows the comparison between LIO3T and eight ECC soundings
collocated in time and space: two were performed in June 2013, four in May
2015 and two in July 2015. Note that these last six were part of the
Maïdo ObservatoRy Gaz and Aerosols Ndacc Experiment (MORGANE) campaign
that took place in May–July 2015 (Portafaix et al., 2016; Duflot et al.,
2016; Posny et al., 2016; Vérèmes et al., 2017). The integration time
for the LIO3T profiles used here is 1 h (starting at the ECC sonde launch
time) and corresponds roughly to the time for the balloon to travel the
troposphere. Note that the discontinuities in the mean profiles shown on
Fig. 8 are caused by the varying valid ranges in the LIO3T profiles (see
Table 2), and note that no profile goes above 17 km for these eight
comparisons. In particular, the valid range in May and July 2015 (during the
MORGANE campaign) is bounded up at 17 km by the volcanic aerosol loading
coming from the Calbuco volcano (Chile, 41.32∘ S,
72.62∘ W), which erupted late April 2015 and whose volcanic plume
reached the TTL above Reunion Island on the 6 May 2015 before slowly
vanishing near the end of July 2015 (Bègue et al., 2017). This aerosol
enhancement is clearly visible on the 355 nm channels of the stratospheric
O3 and LI1200 lidars and on the 532 nm channel of the LIO3T (not
shown), and back trajectories together with CALIOP observations (on board
CALIPSO – not shown) show that the detected plume comes from the Calbuco
volcano (Bègue et al., 2017). Consequently, although we do not have any
information on the corresponding aerosol and SO2 amount, we consider it
wise to assume that, in the layer where this volcanic plume lies (i.e.
between 17 and 22 km), the SO2 and aerosols loading is too strong to
allow a correct O3 retrieval (Ancellet et al., 1987; McGee et al., 1993).
One can see on Fig. 5 that there is an overall agreement between LIO3T and
the ECC considering the lidar uncertainty and ECC precision (panel b). D is
6.8 % for the whole probed column (LIO3T lower than ECC). This value
agrees with the ones recently reported for single or multiple ECC-lidar
comparisons (between 6 and 20 % reported by Uchino et al., 2014; 20 %
reported by Sullivan et al., 2015; 8 % reported by Gaudel et al., 2015).
Figure 6 shows the comparison between the SHADOZ/NDACC Gillot routine ECC
soundings and LIO3T profiles. As the first ones are performed during the daytime
(usually around 15:00:00 LT) and the last ones during night-time (between
19:00:00 and 01:00:00 LT), ECC soundings are taken into consideration when
performed 1 day before or after a LIO3T profile acquisition; we find 37
pairs for comparison over the years 2013–2015. The LIO3T profiles used here
are full night-time profiles. Once again, note that the discontinuities in
the mean profiles shown on Fig. 6 are caused by the varying valid ranges in
the LIO3T profiles (and one can see that only one profile is above 18 km).
Despite the fact that the instruments were neither collocated in time nor
space (the ECC launch site – Gillot – is 26 km away from the Maïdo
Observatory (see Table 1) and balloons are advected by the wind), one can see
that there is an overall good agreement between measurements considering the
lidar uncertainty and ECC precision, with a mean D equal to 9.4 % over
the entire 6–19 km column (LIO3T lower than ECC).
Dates of comparisons with collocated ECC soundings and corresponding
LIO3T O3 profile valid ranges. Italicized dates indicate profiles
impacted by the Calbuco eruption.
DateProfile validrange (km)2013/06/246–142013/06/256–142015/05/116–172015/05/1510–162015/05/266–122015/05/286–172015/07/066–152015/07/076–17Comparison with ground-based and space-borne FTIRs
In this section we compare the LIO3T profiles with collocated partial column
measurements performed by two FTIRs: the Bruker 125HR installed at the
Maïdo Observatory since 2013 and IASI on board the MetOp-A satellite.
Comparison with NDACC ground-based FTIR measurements
A Bruker 125HR FTIR spectrometer started operating at the Maïdo
Observatory in March 2013 with a primary dedication to NDACC measurements
(Zhou et al., 2016). This NDACC ground-based FTIR observes the absorption of
the direct solar radiation with high spectral resolution
(0.0035–0.0110 cm-1) and uses the pressure-broadening effect of
absorption lines to retrieve volume mixing ratio (vmr) low vertical
resolution profiles of target gases. The FTIR O3 measurements show good
sensitivity from the ground up to about 45 km. Within this vertical range,
about four vertical layers can be distinguished; i.e. the vertical resolution
varies from 8 to 15 km (Vigouroux et al., 2015). In this study, the FTIR
retrievals are based on an optimal estimation method (Rodgers, 2000) carried
out with the SFIT4 algorithm (https://wiki.ucar.edu/display/sfit4),
which is an open source code, jointly developed at the NASA Langley Research
Center, the National Center for Atmospheric Research (NCAR), the National
Institute of Water and Atmosphere Research (NIWA) and the University of
Bremen. HBr cell measurements are performed on a daily basis to verify the
alignment of the instrument and to obtain the instrument line shape (ILS)
using the LINEFIT14.5 programme (Hase et al., 1999). The retrieval scheme is
described in Vigouroux et al. (2015) and closely follows the recipe of the
Jungfraujoch station (except for the ILS which is fixed from LINEFIT results
at Maïdo): the retrieval microwindow is 1000–1005 cm-1, the a
priori data come from the WACCMv6 model and pressure and temperature a priori
profiles were obtained from the National Centers for Environmental
Prediction. The a priori water profile is obtained from a dedicated
pre-retrieval. Each O3 profile is retrieved with the signal to noise of
the source spectrum. The total uncertainty of the O3 profile is
dominated by the smoothing error (i.e. the poor vertical resolution of the
profile), the temperature and the spectroscopic uncertainties. We use the
following approach for comparison:
Lower x axis: ground-based NDACC FTIR (black curve and circles)
and IASI (black dashed curve and squares) averaging kernels for the 8–16 and 6–16 km partial columns, respectively. Upper x axis: ground-based
NDACC FTIR (blue curve and diamonds) and IASI (blue dashed curve and
triangles) O3 a priori profiles.
(a) Smoothed LIO3T (red circles) and ground-based NDACC
FTIR (blue squares) 8.5–16 km O3 partial columns. Vertical bars give
uncertainties for each measurement. (b)r (%) between LIO3T and
FTIR measurements (blue crosses) superimposed on LIO3T + FTIR
uncertainties around zero (black dotted lines and dots).
FTIR observations were performed during the daytime. Each LIO3T measurement is compared to all FTIR measurements within a 24 h time
window.
For each pair (114 pairs in total), the LIO3T profile is regridded consistently with the
FTIR.
FTIR measurements are averaged within the 24 h time window around a single LIO3T measurement for
comparison.
At this stage we have a set of comparable pairs of measurements with various validity domains for LIO3T profiles; however, the method needs constant boundaries for the partial column used for comparison. We then choose the partial column shared by a sufficient number of LIO3T profiles to allow a reasonable comparison. The upper and lower limits of this partial column are hereafter called “valid range for
comparison”.
The regridded LIO3T profile is smoothed with the FTIR averaging kernel matrix and a priori
(see e.g. Rodgers and Connor, 2003; Vigouroux et al., 2008). To allow for
smoothing, the LIO3T measured profiles are extended by the FTIR a priori
outside the valid range for comparison. By smoothing the LIO3T profiles, we
degrade them to the FTIR low vertical resolution, and we can get rid of the
FTIR smoothing uncertainty associated with the
comparison.
Finally, a partial column is calculated from this smoothed LIO3T profile in the valid range of comparison.
(a) Smoothed LIO3T (red circles) and IASI (blue squares)
6–16 km O3 partial columns. Vertical bars give uncertainties for each
measurement. (b) r (%) between LIO3T and IASI measurements (blue
crosses) superimposed on LIO3T + IASI uncertainties around zero (black
dotted lines and dots).
We find 12 comparison pairs over the studied period within the 8.5–16 km
valid range for comparison. In this 8.5–16 km partial column, the
ground-based NDACC FTIR has 1.1 degree of freedom (Rodgers, 2000) and a mean
total uncertainty of 7.5 %. Figure 7 shows the FTIR a priori profile and
averaging kernels for this 8.5–16 km partial column, both of which are
used to smooth the LIO3T measurements to compare them with the FTIR ones.
Figures 8 shows the comparison of the FTIR and LIO3T partial columns
available over the January 2013–January 2016 period. One can see that there is good
agreement between the data sets, taking into account the uncertainties. We find a D
of 11.8 % between data sets (LIO3T higher than FTIR). Note that, due to
the sparse comparison points, the Southern Hemisphere biomass burning season
is not visible on this plot.
Comparison with IASI measurements
IASI is on board the MetOp-A satellite, launched in a Sun-synchronous orbit
around the Earth at the end of 2006. A second IASI was launched on board
MetOp-B in September 2012 and the launch of the third one (MetOp-C) is
planned for late 2018. In this comparison, IASI/MetOp-A data are used. IASI
is a FTIR instrument that measures the thermal infrared radiation emitted by
the Earth's surface and atmosphere in the 645–2760 cm-1 spectral range
with a spectral resolution of 0.5 cm-1 apodized and a radiometric noise
below 0.2 K between 645 and 950 cm-1 at 280 K (Clerbaux et al.,
2009).
IASI is an interesting instrument for our intercomparison effort as it
provides global Earth coverage twice daily with overpass times at 09:30:00
and 21:30:00 mean local time and a nadir footprint on the ground of 12 km.
IASI has significant sensitivity to tropospheric O3. As LIO3T usually
fires between 19:00:00 and 01:00:00 local times, here we used the IASI
night-time overpass measurements. The IASI data used in this study come from
the FORLI-O3 v20151001 scheme (Hurtmans et al., 2012; Boynard et al.,
2016).
To compare measurements from both instruments, IASI retrievals are averaged
over a 1∘× 1∘ box around the Maïdo Observatory
location. We then use the same approach as described in Sect. 4.2.1 (except
points i and iii). We find 39 comparison pairs over the studied period
within the 6–16 km valid range for comparison. In this 6–16 km partial
column, IASI has 1.6 degree of freedom (Rodgers, 2000) and a mean total
uncertainty equal to 18.4 %. Figure 7 shows the mean IASI a priori
profile and mean averaging kernels in the 6–16 km partial column for the 39
comparison pairs. In the following, LIO3T measurements are smoothed according
to the characteristics of the IASI retrievals.
Figure 9 shows the comparison of the IASI and LIO3T partial column time
series. We obtain a good agreement between the data sets considering the
uncertainties. We find a D of 11.3 % between data sets (LIO3T higher
than IASI). These results are in agreement with the 5–15 % O3
abundance difference of IASI in the troposphere compared to ECC soundings
reported recently by Boynard et al. (2016). Note that, due to the sparse
comparison points, the Southern Hemisphere biomass burning season is barely
visible on this plot.
Number of O3 profiles per month for ECC (1998–2015, 568
profiles), LIO3TUR (1998–2010, 427 profiles) and LIO3T (January
2013–January 2016, 84 profiles).
Monthly O3 climatology between 0 and 19 km derived from ECC
sondes over 1998–2015 at the Gillot site (a), from LIO3TUR
over 1998–2010 at Université de la Réunion campus site (b)
and from LIO3T over 2013–2015 at Maïdo Observatory (including data
routinely performed and from intensive period of observations) (c).
(a) Seasonal LIO3T O3 profiles for DJF (blue curve –
8 profiles), MAM (green curve –30 profiles), JJA (red curve – 25 profiles)
and SON (black curve – 21 profiles). The shaded areas give the 1 standard
deviation around the mean. (b) Number of LIO3T profiles used for
each climatological profile.
Data set and climatologies
Figure 10 shows the monthly distribution of the number of O3 profiles
acquired by the NDACC/SHADOZ ECC (Gillot, 1998–2015, 568 profiles),
LIO3TUR (Université de la Réunion, 1998–2010, 427
profiles), and LIO3T (Maïdo Observatory, 2013–2015, 84 profiles). The
low number of lidar profiles in the austral summer period (especially
December–January) is explained by the high occurrence of cloudy skies.
In particular, one can see that only one LIO3T profile is available for December
(which ends up at 10 km due to a misalignment of the LIO3T). The lower limit of
LIO3T profiles range from 6 to 10 km and the upper limit ranges from 12 to 19 km.
Most LIO3T profiles start at 6 km and end at 17–18 km.
Figure 11 shows the three resulting monthly tropospheric O3
climatologies, on which the following seasonal features can be observed:
A clear increase of O3 abundance is seen over the whole tropospheric column –
especially between 2 and 10 km – starting in June and ending in December with a
maximum in October of ≈ 10 × 1011 molec cm-3 on average between 4 and 10 km.
This increase is due to the influence of air masses coming from South America, southern Africa and south-eastern Asia (Edwards et al., 2006; Duflot et al., 2010), where the biomass burning season occurs every year during this period. O3 abundance then presents a slow decay over the entire tropospheric column from January to
May.
There is a decrease of the ozonopause altitude from ≈ 17 km in December–July
down to ≈ 15 km in August–November (Sivakumar et al., 2011b), which is likely
a combination of the spring and summer maximum of occurrence of stratosphere-to-troposphere
exchanges (STE) above Reunion Island (Clain et al., 2010) and of the wintertime thermal effect on the troposphere
thickness.
The minimum of O3 abundance occurs in February between 10 and 16 km (≈ 3 × 1011 molec cm-3 on average), which is likely a sign of the austral summer deep convection bringing boundary layer O3-poor air masses up to the middle–upper troposphere.
In conclusion, the three data sets show a remarkable – and reassuring –
agreement in terms of patterns and values.
Figure 12 shows the seasonal profiles derived from the LIO3T measurements.
The Southern Hemisphere biomass burning season is still clearly visible in
the September–October–November profile (SON), with an increase that covers
the whole of the probed column, and also on the June–July–August (JJA) profile from
6 to 13 km.
Conclusions and future plans
A DIAL tropospheric O3 lidar was operating on the Université de la
Réunion campus site from 1998 to 2010, providing 427 O3 profiles. In
2012, the system was moved up to the Maïdo Observatory and routine O3
observations started in February 2013 by the LIO3T. From then until January
2016, 84 O3 profiles were acquired and the LIO3T operation is ongoing. These
O3 measurements were recently affiliated in the NDACC.
The LIO3T observation scheme is based on the DIAL technique, which currently
detects two wavelengths, 289 and 316 nm, with multiple receivers. The
transmitted wavelengths are generated by focusing the output of a quadrupled
Nd:YAG laser beam (266 nm) onto a Raman cell filled with high-pressure
deuterium, using helium as buffer gas. With knowledge of the O3
absorption coefficient at these two wavelengths, the range-resolved number
density can be derived.
The optimal range for the actual system is 6–19 km, depending on the system
performance and atmospheric conditions. For a 1 h integration time,
vertical resolution varies from 0.7 km at 6 km to 1.3 km at 19 km, and
mean uncertainty over the 6–19 km range is between ≈ 6 and
≈ 13 %.
Comparisons with O3 external data set were performed showing good
agreement between data sets considering the uncertainties: we found a
6.8 % D between LIO3T observations and eight ECC sondes simultaneously
launched from the Maïdo Observatory (LIO3T lower than ECC), 9.4 % D
between LIO3T observations and 37 ECC sondes launched from the Gillot site
during the daytime in a ±24 h window around lidar shooting (LIO3T lower
than ECC), 11.8 % D between LIO3T and 12 ground-based NDACC FTIR
measurements acquired during the daytime in a ±24 h window around lidar
shooting in the 8.5–16 km partial column (LIO3T higher than FTIR), and
11.3 % D between LIO3T and 39 simultaneous night-time IASI observations
over Reunion Island in the 6–16 km partial column (LIO3T higher than IASI).
ECC, LIO3TUR and LIO3T monthly climatologies all exhibit the same
range of values and the same seasonal patterns:
the O3 abundance increase between 6 and 10 km in austral winter and
spring due to the Southern Hemisphere biomass burning season;
the ozonopause altitude decrease from ≈ 17 to ≈ 15 km from late austral winter to early austral summer due to the wintertime thermal effect on the troposphere thickness combined with the enhanced occurrence of STE in austral spring and summer;
the O3 abundance minimum between 10 and 16 km in late austral summer in the middle–upper troposphere due to deep convection uplifting O3-poor air masses from the boundary layer.
Moving this lidar from the Université de la Réunion campus site
up to the Maïdo observatory allows it to document the UT/LS region and to
follow stratospheric and tropospheric intrusions with relevant vertical and
time resolutions together with a reasonable uncertainty (1.5 km, 20 min and
10 %, respectively, at 18 km). This tropospheric O3 data set
covering the tropical free troposphere and UT/LS of a sparsely documented
region (south-western Indian Ocean) constitutes an extremely valuable
resource for the validation of satellite tropospheric O3 retrievals,
analysis of the O3 variability and sources, dynamics analysis of case
studies and for long-term atmospheric monitoring.
Future plans for the LIO3T are to (1) use the available 532 nm residual
beam to detect and study aerosols in the free troposphere, TTL and lower
stratosphere. The use of the infrared signal (1064 nm) to study aerosols is
also planned. (2) NDACC recommendations will be implemented in the data processing
(O3 cross sections, background and saturation corrections uncertainties
propagation, interfering gases). (3) Uncertainties will be calculated due to the
presence of aerosols in the troposphere using an iterative aerosol assessment
procedure, ideally using the 532 nm backscattered signal.
LIO3T system was very recently affiliated in the NDACC for
O3 measurements. The LIO3T data used in this article will be soon available
in the NDACC database, accessible at http://www.ndsc.ncep.noaa.gov/
(Network for the Detection of Atmospheric Composition Change, 2017). The IASI
L1C data and L2 temperature data used in this study are currently not
publicly available. These data were provided by the Aeris data infrastructure
(AERIS, 2017).
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Twenty-five years of
operations of the Network for the Detection of Atmospheric Composition Change
(NDACC) (AMT/ACP/ESSD inter-journal SI)”. It is not associated with a
conference.
Acknowledgements
The authors acknowledge the European Communities, the Région Réunion,
CNRS, and Université de la Réunion for their support and
contributions in the construction phase of the research infrastructure OPAR
(Observatoire de Physique de l'Atmosphère de La Réunion). OPAR is
presently funded by CNRS (INSU) and Université de La Réunion and
managed by OSU-R (Observatoire des Sciences de l'Univers de La Réunion,
UMS 3365). The authors also gratefully acknowledge Eric Golubic, Patrick
Hernandez and Louis Mottet, who are deeply involved in the routine lidar
observations at the Maïdo facility. Jacquelyn Cecile Witte (NASA/GSFC) is
acknowledged for the ECC data reprocessing. IASI is a joint mission of
EUMETSAT and the Centre National d'Etudes Spatiales (CNES, France). The IASI
L1C data are distributed in near real time by EUMETSAT through the EUMETCast
system distribution. The authors acknowledge the Aeris data infrastructure
for providing access to the IASI L1C data and L2 temperature data used in
this study (available at: http://www.aeris-data.fr/?locale=en). This
work was undertaken in the framework of the EUMETSAT O3M-SAF project
(http://acsaf.org/), the European Space Agency O3 Climate Change
Initiative (O3-CCI, http://www.esa-ozone-cci.org). The ULB French
scientists are grateful to CNES and Centre National de la Recherche
Scientifique (CNRS) for financial support. PFC is grateful to Belspo and ESA
(Prodex IASI.Flow project) for financial support. The colleagues from
BIRA-IASB acknowledge the support from the Belgian Science Policy Office, as
well as from ESA/PRODEX and the Copernicus programme
(CAMS-VAL). Edited by: Gabriele
Stiller Reviewed by: three anonymous referees
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