Introduction
O3 is an important atmospheric trace gas. In the stratosphere O3
absorbs ultraviolet (UV) radiation, thereby protecting all living organisms.
During the second half of the last century stratospheric ozone has
experienced significant depletion. With the implementation of the Montreal
Protocol in 1987 and its amendments, the anthropogenic emissions of the mainly
responsible chlorine and fluoride compounds were strongly restricted and
stratospheric O3 depletion has progressively diminished
Scientific Assessment of Ozone Depletion, World Meteorological
Organization,, and it is expected to become negligible by the
end of this century . In the troposphere O3 is an
important pollutant and greenhouse gas and has
increased since pre-industrial times . O3 is hazardous
to human health and a severe problem in many cities around the globe as it is
produced by complex photochemistry in the presence of anthropogenic pollutants
(NOx, CO, and VOC) and UV radiation e.g..
To understand the stratospheric or tropospheric O3 evolution the
whole atmosphere has to be considered. For instance,
showed that there is a correlation between the stratospheric cooling,
stratospheric O3 depletion and the increase of greenhouse gases (mainly
anthropogenic CO2). Increases in tropospheric greenhouse gas
concentrations warm the lower and middle troposphere and cool the
stratosphere. The changed vertical temperature structure affects atmospheric
dynamics and stratospheric O3 distribution. The complexity of these
interactions make it difficult to predict how, when and to what extent
stratospheric O3 recovery will take place .
Because tropospheric O3 is a greenhouse gas, increasing tropospheric
O3 levels will have an effect on the evolution of stratospheric O3
concentrations. Similarly, tropospheric O3 levels are influenced by the
stratosphere and stratospheric O3 concentrations in different ways. A
direct connection exists during stratosphere–troposphere exchange events.
Furthermore, stratospheric O3 or aerosol loading affect the UV radiation
penetrating to the troposphere and consequently the photochemistry of
tropospheric O3 and other species .
A comprehensive investigation of these complex climate-chemistry interactions
and the particular role of O3 is only possible by combining atmospheric
models with observations . In this context
ground-based high-resolution solar absorption FTIR spectrometer measurements
have been proven to be useful, providing information on the vertical
distribution of O3 and other trace gases
e.g.. High-resolution and high-quality FTIR measurements are organized in the framework
of NDACC and are made at about 20 globally distributed sites
(http://www.acom.ucar.edu/irwg/). Due to the high-quality and long-term
characteristics, the NDACC FTIR data are very interesting for trend studies
e.g.. However, the NDACC-like
ground-based FTIR measurements are mainly performed at remote sites (i.e. far
away from polluted areas) and are very scarce at tropical latitudes. The only
NDACC-like FTIR sites reporting atmospheric data within or close to the
20∘ N–20∘ S latitude belt are Mauna Loa (19.5∘ N,
155.6∘ W), Addis Ababa 9.0∘ N,
38.8∘ E,, Paramaribo 5.8∘ N,
55.2∘ W, and La Réunion
21.1∘ S, 55.4∘ E,. Medium-resolution
FTIR spectrometers have also been used to measure atmospheric O3 amounts
e.g. over the megacity of Paris,. However, a
comprehensive quality documentation of the O3 data retrieved from medium-resolution FTIR spectra is still missing.
Vertical distribution of O3 and location of O3 measuring
instrumentation in the surroundings of Mexico City. (a) WACCM
version 6 profiles of O3 volume mixing ratios (blue line) and O3
abundances (black line) used as a priori for the FTIR retrieval over Mexico
City. The yellow stars indicate altitude regions that are analysed in the
following sections. (b) Schematics indicating the medium-resolution
FTIR experiment and the three in situ instruments (at the stations PED, COY
and SUR) in Mexico City at and close to UNAM (at about 2.3 km a.s.l.) and
the high-resolution NDACC FTIR experiment outside the city in Altzomoni (at
about 4 km a.s.l.).
Here we present atmospheric O3 profiles obtained from solar absorption
spectra, measured by two different FTIR spectrometers in central Mexico (at
about 19∘ N, 99∘ W). The first instrument is a
high-resolution FTIR spectrometer located at a high-altitude station
(Altzomoni), but very close to Mexico City. In the meanwhile these FTIR
activities form part of NDACC and together with the Paramaribo measurements
represent the only NDACC FTIR contribution from Latin America. The second
instrument is a medium-resolution FTIR spectrometer located inside of Mexico
City, a megacity with emissions that have been investigated in great detail
in the context of MILAGRO Megacities Initiative: Local And Global
Research Observations,and references therein. We would like to
point out that ground-based remote sensing measurements of the vertical
O3 distribution between the boundary layer and the upper stratosphere
are challenged by the fact that the O3 concentrations strongly vary with
altitude. This is shown in Fig. a,
which shows a typical tropical O3 profile (blue line for volume mixing
ratios and black for abundances relative to total abundances).
The focus of this paper is to demonstrate the reliability of the FTIR O3
profiles between the boundary layer and the upper stratosphere and in the
following sections we will have a closer look at the altitude regions marked
by yellow stars in Fig. a.
Section presents the high- and medium-resolution FTIR
remote sensing experiments, the remote sensing retrievals and discusses the
main characteristics of the retrieval products. In Sect. we
analyse the O3 concentrations above the boundary layer and show that the
total column amounts and lower- and middle-stratospheric O3 amounts
retrieved from the medium-resolution FTIR measurements are in good agreement
with the amounts retrieved from the high-resolution FTIR measurements. In
Sect. we investigate the possibility of observing O3
concentrations in the boundary layer by the medium-resolution FTIR instrument
that is located in Mexico City. We empirically prove this possibility using
boundary layer in situ O3 data as a reference and show that by combining
the two remote sensing experiments we can generate a boundary layer O3
remote sensing product with improved precision. Section
consists of a summary and outlook.
Ground-based FTIR remote sensing
Figure b indicates the location of the
two ground-based FTIR remote sensing and the three in situ surface
instruments that are used in the context of Sect. . The first
FTIR instrument is located in Altzomoni within the Izta-Popo National Park at
nearly 4000 m a.s.l. between the Popocatépetl and the Iztaccílhuatl
volcanoes at 19.12∘ N, 98.65∘ W, about 1700 m above the
Mexico City basin. The second instrument is located at the UNAM (Universidad
Nacional Autónoma de México) in Mexico City, on the rooftop of the
CCA (Centro de Ciencias de la Atmósfera, 19.33∘ N,
99.18∘ W, 2260 m a.s.l.). The UNAM stations is at a horizontal
distance of about 60 km to Altzomoni in north-westerly direction and both
stations are operated by the Spectroscopy and Remote Sensing Group of
CCA/UNAM.
Experimental set-up
A ground-based FTIR remote sensing experiment consists of a solar tracker and
a Michelson-type interferometer. The solar tracker captures the direct solar
light beam and couples it into the interferometer, which splits the solar
light into two beams. The first beam traverses a fixed distance and the
second beam a variable distance. Finally the intensity of the recombined beam
is analysed by a detector. In the middle infrared, where the two
instruments used here are taking measurements, photoconductive as well as photovoltaic
detectors are applied. The intensity of the recombined beam depend on their
optical path difference and these intensity signals are called the
interferogram. The spectrum is then calculated by a Fourier transformation of
the interferogram.
The maximum optical path difference, the apodization and misalignments of the
interferometer (which can be minimized but not completely avoided)
determine the instrumental line shape (ILS, the response of the instrument to
a monochromatic input radiation). Understanding the ILS is important for
correctly interpreting the measured spectra. The effect of misalignments on
the ILS can be determined by low-pressure gas cell measurements. Since the
pressure and the absorption characteristics of the gas in the cell are known,
we can retrieve the ILS from such measurements .
Retrieval method
The measured solar absorption spectra (expressed as spectral bin vector
y) and the atmospheric state (expressed by atmospheric state vector
x) are connected via a radiative transport model (forward model
F):
y=F(x,p).
Here the vector p represents auxiliary atmospheric parameters (like
temperature) or instrumental characteristics (like the instrumental line
shape). Generally, there are many different atmospheric states x that
can explain the measured spectrum y equally well within its
measurement noise level. This means that we face an ill-posed problem and we
need to constrain the solution state. The constraint solution is at the
minimum of the cost function:
y-F(x,p)TSϵ-1y-F(x,p)+x-xaTSa-1x-xa.
Here the first term is a measure of the difference between the measured
spectrum (y with Sϵ capturing the measurement
noise covariance) and the spectrum simulated for a given atmospheric state
(x). The second term is the regularization term. It constrains the
atmospheric solution state (x) towards the a priori state
xa, in which the kind and the strength of the constraint are defined
by the covariance matrix Sa. For more details about solving
ill-posed problems in atmospheric remote sensing please refer to
.
Since we face a non-linear problem the solution is reached iteratively,
and for the (i+1) iteration step it is as follows:
xi+1=xa+SaKiTKiSaKiT+Sϵ-1y-F(xi)+Kixi-xa.
Here K is the Jacobian matrix which samples the derivatives
∂y/∂x (changes in the spectral radiances y for
changes in the vertical distribution of the atmospheric constituents
x).
An important addendum of the retrieved solution vector is the averaging
kernel matrix A. It samples the derivatives
∂x/∂xact (changes in the retrieved
concentration x for changes in the actual atmospheric concentration
xact) describing the smoothing of the actual atmospheric state by
the remote sensing measurement process:
x-xa=Axact-xa.
The matrix A can be calculated as follows:
A=GK=KSϵ-1KT+Sa-1-1KTSϵ-1K.
Here G is the gain matrix, which samples ∂x/∂y
(changes in the retrieved state x for changes in the spectral
radiances y). The kernels are rather important since they document
what is actually measured by the remote sensing system. Without this
information, the remote sensing data cannot be used in a sensible manner. In
addition, the trace of A quantifies the amount of information
obtained by the measurement. It can be interpreted as the degrees of freedom
of signal (DOFS) of the measurement.
In this study we work with the PROFFIT retrieval code , which
has been used for many years by the ground-based FTIR community to
evaluate high-resolution solar absorption spectra. PROFFIT offers three
kinds of constraints for solving the inversion problem: scaling of a priori
profiles, optimal estimation using an Sa constructed from
a model or measurement climatology, and Tikhonov–Phillips
method using an ad-hoc-created Sa-1 to
obtain absolute profile values or the profile shape,.
Uncertainties used for the error estimation. The second column gives
the uncertainty value and the third column the partitioning between
statistical and systematic sources.
Source
Uncertainty
Statistical/
systematic
Measurement noise
0.3 % (for Altzomoni)
100/0
0.5 % (for UNAM)
100/0
Baseline (channelling, assuming four frequencies:
0.2 %
50/50
0.005, 0.2, 1.0, and 3.0 cm-1)
Baseline (offset)
1 %
50/50
Instrumental line shape (mod. eff. and pha. err.)
5 % and 0.01 rad
50/50
Temperature profile
1.5 K (surface–12.5 km a.s.l.)
70/30
1.5 K (12.5–45 km a.s.l.)
70/30
6 K (above 45 km a.s.l.)
70/30
Line of sight
0.2∘
90/10
Solar lines (intensity and ν-scale)
1 % and 10-6
80/20
Spectroscopic parameters of O3
2 % (line intensity)
0/100
5 % (pressure broadening)
0/100
Interference with water vapour
100 % (atmospheric H2O)
50/50
We use a model atmosphere with 41 and 44 discretized grid levels from the
surface up to 120 km for the Altzomoni and UNAM retrievals, respectively, in
which the grid levels 1 to 3 of UNAM are situated below Altzomoni and the
grid levels 4 to 44 of UNAM are the same as the grid levels 1 to 41 of
Altzomoni. The O3 inversions are regularized by a Tikhonov–Phillips
constraint of the vertical profile slope and the absolute value for the
uppermost atmospheric model altitude (i.e. the 120 km grid level). The
strength of the constraint is determined by starting with a weak constraint
and then increasing it until a significant increase in the residual of the
spectral fit is observed (L-curve criterion). The inversion is made on the
logarithmic scale of O3 volume mixing ratios . As
a priori volume mixing ratio profiles (xa) for O3 and
all interfering species, we use climatologies from the Whole Atmosphere
Community Climate Model (WACCM, version 6). The O3 a priori profile is
depicted in Fig. a. The temperature
and pressure profiles are from the National Centers for Environmental
Prediction (NCEP) analysis. For altitudes above 50 km we use monthly mean
CIRA temperature climatologies . Spectroscopic line
parameters for O3 and interfering species are taken from the
high-resolution transmission molecular absorption (HITRAN) 2008 database
, except for water, for which the HITRAN 2009 update is used.
Error analyses
The errors are estimated in the form of a error covariance matrix
Se:
Se=GKpSpKpTGT.
Here G is the gain matrix, Kp are the Jacobians with
respect to the parameters p (changes of spectral bins due to changes in the
parameters p) and Sp is the uncertainty covariance for the
parameters p. We assume uncertainties for the parameters listed in Table , which are uncorrelated between the
different parameters.
We observe white noise in the measured spectra of 0.3 and 0.5 %
(ratio between noise and highest intensity in the measured spectral region),
which is what we typically observe in the Altzomoni and UNAM spectra. As
baseline channelling amplitude we assume 0.2 %, which is also what we
occasionally observe in NDACC FTIR spectra measurements. We assume a
relatively high baseline offset of 1 % higher than for other error
assessment studies, e.g., because in routine operations the
spectra are often taken of sky that is partially covered by clouds.
Generally, clouds cause very noisy measurements and can be easily identified.
However, occasionally there might be clouds in the line of sight for a few
seconds during a measurement (a measurement takes several minutes).
Interferograms recorded during such intensity fluctuations then lead to
spectra with an increased baseline offset. For the ILS we assume an
uncertainty in the form of a linear decay of the modulation from the zero path
difference to maximal optical path difference by 5 % and for the phase
error 0.1 rad for all positions of the interferometer mirror. The
atmospheric temperature uncertainty from NCEP is assumed to be within 1.5 K
(from the surface up to 45 km, whereas we separately consider tropospheric
and stratospheric uncertainties) and 6 K for higher altitudes. For the
uncertainty of the solar tracker we use 0.2∘, which is a rather
conservative estimate, since it is close to the radius of the solar disc (an
error close to the radius of 0.25∘ would be clearly visible from
high noise levels in the measured spectra). To estimate the effect of
solar lines we work with uncertainties that have been used in many other
assessment studies e.g.. The spectroscopic parameters have
typical uncertainties of 2 and 5 % for line intensity and pressure
broadening, respectively (typical values as given in the HITRAN line
parameter lists). Finally, we assume a 100 % uncertainty in the
atmospheric water vapour content. The large variability of tropospheric water
vapour might affect the O3 retrievals, even though the selected spectral
windows contain only weak spectroscopic water vapour signatures.
Observations at Altzomoni
Infrared solar absorption spectra have been recorded at Altzomoni since May
2012 with a Bruker IFS 120/5HR spectrometer (an IFS 120HR which has been
upgraded with the electronics of an IFS 125HR). The sunlight is followed and
guided to the spectrometer with a solar tracker, which is equipped with two
plane ellipse-shape mirrors and two motors of rotation stage, one with which to access
different elevation angles and the other to reach azimuthal directions. The
pointing of the solar tracker is monitored and controlled with a standard
CMOS USB-camera and the Camtracker software . The solar tracker
is protected with a motorized dome, and the FTIR spectrometer and solar
tracker are operated remotely from the UNAM campus in Mexico City. The
spectrometer allows measurements with very high resolution
(0.0035 cm-1, i.e. maximum optical path difference of 257 cm) and is
equipped with two beam-splitters: a potassium bromide (KBr, which is normally
used) and a calcium fluoride (CaF2, which is only occasionally used).
Routine measurements are performed with three detectors:
mercury cadmium telluride (MCT), indium antimonide (InSb) and
indium gallium arsenide (InGaAs). The first two are nitrogen cooled and cover
the spectral range of 700–4200 cm-1 (mid-infrared region), and the third
detector works at room temperature in the spectral range
4000–12 800 cm-1 (near-infrared region). In order to increase the
signal-to-noise-ratio in the mid-infrared spectral region, six NDACC-type filters
are used to cover different spectral subregions. The Altzomoni FTIR
experiment forms part of NDACC (http://www.ndacc.org) and also contributes to
the MUSICA activities .
The spectral windows used for the retrievals with the NDACC FTIR
instrument at Altzomoni. An example for a typical measurement is shown
(22 March 2013, 17:51 UT; solar elevation: 67.9∘; O3 slant
column: 283 DU). Black line: measurement; red dashed line: simulation; blue
line: residual (difference between measurement and simulation).
In order to investigate a potential misalignment of the interferometer and
interpret the observed line shapes of the solar absorption spectra in a
correct manner , we obtained the ILS from HBr cell
measurements. This cell has a length of 2 cm and is filled with HBr at a
nominal pressure of 200 Pa (total pressure is between 200 and 250 Pa). The
modulation and phase error of the ILS were calculated from the cell
measurements using the LINEFIT code . The estimated ILS was
then used for the retrieval process. An example of a cell measurement and a
corresponding ILS retrieval is shown in Appendix .
Characterization of the O3 volume mixing ratio profiles
obtained from the FTIR measurements at Altzomoni as shown in
Fig. . (a) Row averaging kernels, all kernels are
depicted as grey lines and kernels for a few different altitudes are
highlighted by different colours. (b) Estimated statistic and
systematic errors, where the errors resulting from the different uncertainty
sources as listed in Table are represented in different
colours. The total error is the root-sum-square of the individual errors and
is shown as thick black line.
O3 was retrieved from the solar absorption spectra obtained with the
photoconductive MCT detector, in the 750–1300 cm-1 filter region at a
spectral resolution of 0.005 cm-1 and an aperture of 1.7 mm was applied.
Every spectrum is calculated by averaging six scans with maximal resolution
(total recording time of one spectrum is 12 min). Figure gives an example of a typical measurement showing the five
spectral windows that we use for the retrievals of O3:
991.25–993.8, 1001.47–1003.04, 1005–1006,
1007.3–1009 and 1011.1–1013.6 cm-1. These windows are further
refined if compared to the windows presented in (in
order to reduce water vapour interferences) and are the standard setting in
PROFFIT NDACC/FTIR O3 retrievals. We independently fit the
48O3, the asymmetric 50O3, the symmetric 50O3
and all the 49O3 isotopologues. All the O3 isotopologues are
fitted on a logarithmic volume mixing ratio scale. Furthermore, we perform
simultaneous fits on a linear volume mixing ratio scale of the interfering
species H2O, CO2 and C2H4, which all have small signatures in the
used spectral windows. The position of the solar lines with respect to
terrestrial lines is determined in a proceeding analysis of a spectral window
that contains well isolated solar and terrestrial lines.
Figure characterizes the O3 remote sensing data
obtained at Altzomoni. Panel (a) shows the averaging
kernels and panel (b) the estimated uncertainty for
the O3 retrieval made with the typical FTIR measurement at Altzomoni as
shown in Fig. . This measurement gives a DOFS value of
4.26, which is a typical value obtained for the Altzomoni O3
retrievals. A DOFS of about 4.0 means that the O3 values can be
retrieved independently for about four different vertical altitude regions.
To illustrate, we highlight the row kernels for the retrievals at 4, 17, 28
and 42 km altitude using thick lines and different colours. The retrieval at
4 km is mainly influenced by actual atmospheric O3 variations between 4
and 10 km (black line), the retrieval at 17 km by actual variations between
15 and 23 km (red line), the retrieval at 28 km by actual variations
between 23 and 32 km (green line) and the retrieval at 42 km by actual
variations between 32 and 45 km (blue line).
The estimated errors are depicted in
Fig. b. It shows the square root
values of the diagonal elements of Se calculated
according to Eq. (). Below 30 km altitude total statistical
and systematic errors are smaller than 5 %. For higher altitudes the
errors strongly increase and are larger than 10 % above 40 km.
Concerning the statistical error, uncertainties in temperature and baseline
are the leading error sources, followed by uncertainties in the ILS and
measurement noise. The systematic errors are dominated by uncertainties in
the spectroscopic line parameters of O3. Uncertainties in temperature,
baseline and ILS are of secondary importance. Statistical and systematic
errors due to uncertainties in the line of sight, solar lines and
interferences with atmospheric H2O variations are smaller than 0.1 %
throughout the atmosphere and are thus not depicted.
The errors for the total column abundances are listed in Table . They are calculated by applying the total column
operator to the left and transposing the total column operator to the
right of Eq. () and then taking the square root. The total
column errors are between 2 and 3 %, in which the statistical error is
mainly due to temperature uncertainty and the systematic error is due to
uncertainties in spectroscopic line intensity parameters of O3.
For this work we analysed 1672 individual Altzomoni spectra, measured on 143 individual days between November 2012 and February 2014. We found that
occasionally there are spectra with rather high noise levels, which then lead
to retrievals with relatively low DOFS values. The high noise levels are probably
caused by clouds that pass through the line of sight when recording
the interferograms. The Fourier transformation of such an interferogram would
then lead to artefacts in the spectrum, such as increased baseline offsets. In
order to avoid that artefacts affect our study, we only define as valid
measurements those for which the retrieval gives a DOFS value of at least
3.9, which is at the lower end of the DOFS values obtained for O3
retrievals made with high-resolution middle infrared solar absorption spectra
e.g.. The DOFS filter leaves us with
1040 individual measurements made on 120 individual days between November
2012 and February 2014.
Estimated errors for the total column abundances of O3 remote
sensing data obtained at Altzomoni and UNAM.
Error source
Altzomoni
UNAM
Stat./sys.
Stat./sys.
Measurement noise
0.1 %/–
0.5 %/–
Baseline
0.9 %/0.9 %
1.0 %/1.0 %
Instrumental line shape
0.4 %/0.4 %
1.0 %/1.0 %
Temperature
2.4 %/1.0 %
2.5 %/1.1 %
Line of sight
0.1 %/< 0.1 %
0.1 %/< 0.1 %
Solar lines
< 0.1 %/< 0.1 %
< 0.1 %/< 0.1 %
O3 spectroscopy
–/2.0 %
–/2.0 %
Interference with H2O
< 0.1 %/< 0.1 %
< 0.1 %/< 0.1 %
Total
2.6 %/2.5 %
2.9 %/2.7 %
It is reasonable to assume that the Altzomoni solar absorption spectra are
only very weakly affected by the large diurnal variations that take place in
the polluted boundary layer. Most measurements are made before 14:00 (UT - 6 h), when
the boundary layer top altitude is below or weakly above the altitude of
Altzomoni . Furthermore, a very strong vertical
mixing of air from below is generally linked to the presence of fog or
clouds. Under such conditions the FTIR is not operated. Nevertheless, the
O3 background levels at Altzomoni depend on the outflow of polluted
boundary layer air, and on larger timescales (e.g. monthly means) the
Altzomoni spectra are affected by the boundary layer pollution.
Same as Fig. but for the spectral window used for
the retrievals with the FTIR instrument at UNAM. An example is shown for
22 March 2013, 17:57 UT; solar elevation: 68.2∘; O3 slant
column: 285.4 DU.
Observations at UNAM
The UNAM atmospheric observatory is located to the south of Mexico City on
the roof of the CCA, within the main UNAM campus. The infrared solar
absorption spectral measurements started in 2008 using a Bruker Opag 22
spectrometer measuring with spectral resolution of
0.5 cm-1,. Since June 2010, spectra have been
recorded with a Bruker Vertex 80 spectrometer allowing measurements with a
maximum optical path difference of up to 12 cm, corresponding to a spectral
resolution of 0.06 cm-1. The spectrometer is equipped with a KBr beam
splitter and two detectors (a nitrogen-cooled MCT detector and an InGaAs
detector working at room temperature). In May 2012 we started to use four
filters in the measurement routine, which cover different spectral
subregions. The sunlight is guided by a solar tracker built in-house which
is covered with a motorized dome. For details see .
The ILS of the interferometer is retrieved from HBr cell measurements
and then used for the retrieval process (an example
of a cell measurement and a corresponding ILS retrieval is shown in Appendix ).
Same as Fig. , but for the profiles obtained from
the medium-resolution FTIR measurements at UNAM as shown in
Fig. .
The spectra for the O3 retrievals are measured with the photoconductive
MCT detector and with an aperture of 1.5 mm using a long-wave pass filter,
which cuts on wavelength at 7.4 µm (i.e. the filter is transparent for
wave numbers up to 1350 cm-1). Every spectrum is obtained from averaging
10 scans with a resolution of 0.1 cm-1, stored every 12 s in the
computer. For the retrievals we use one spectral window between 991 and
1073 cm-1. Figure shows the spectral window for a
measurement that has been taken in temporal coincidence with the Altzomoni
measurement shown in Fig. . It is the same spectral window
that has been used by . We fit the O3 volume mixing
ratios on a logarithmic volume mixing ratio scale, and all the different
O3 isotopologues are treated as a single species. As interfering species
we simultaneously fit H2O, CO2, SO2, NH3 and C2H4, in which
we only allow the prescribed first guess profiles to be scaled (which are
the same as the profiles used as a priori, i.e. from the WACCM climatology).
Furthermore, we fit phase and amplitude of channelling with a frequency of
0.39 cm-1 (this kind of channelling is what we occasionally observe in
the UNAM spectra). The solar lines are simulated according to fixed model
parameters.
Figure shows the averaging kernels and error estimations
for the retrieval made with the measurement as shown in
Fig. . The depicted row kernels belong to an averaging
kernel matrix with a DOFS of 3.20, which is a typical value obtained for
the UNAM O3 retrievals and means that O3 values can be retrieved
independently for about three different vertical altitude regions. In
Fig. we highlight the row kernels corresponding to
retrievals of the 2.3, 17 and 32 km altitudes. The retrieval for 2.3 km
represents actual atmospheric O3 variations between 2.3 and 10 km, the
retrieval for 17 km the actual variations between 15 and 24 km and the
retrieval for 32 km the actual variations between 24 and 36 km. Even for
spectra recorded with medium resolution the retrieval allows us to separate
tropospheric, lower stratospheric and middle stratospheric O3
variations.
Below 30 km the O3 errors estimated for UNAM are higher than the errors
estimated for Altzomoni. We calculate total statistical and systematic errors
of about 7.5 %. The respective statistical errors are mainly controlled
by uncertainties in the ILS, the baseline and the atmospheric temperatures.
The respective systematic errors are dominated by uncertainties in the
spectroscopic line parameters of O3, the ILS and the baseline. Above
30 km the statistical errors increase to 10 %, mainly caused by the
increased importance of temperature uncertainties and measurement noise. The
systematic errors slightly decrease to about 6 % (above 30 km), mainly
caused by decreasing importance of the uncertainties in the ILS and the
baseline. Errors due to uncertainties of the line of sight, solar lines and
interference with atmospheric H2O variations are smaller than 0.1 %
throughout the atmosphere and not depicted.
The errors for the total column abundances are listed in Table . The respective UNAM errors are very similar to the
Altzomoni errors (between 2 and 3 %). As for Altzomoni the statistic
error is mainly due to temperature uncertainty and the systematic error is due
to uncertainties in spectroscopic line intensity parameters of O3.
For this work we analysed 1625 individual UNAM spectra measured on 88 individual days between November 2012 and May 2013 (in June 2013 the
measurements had to be interrupted due to construction works at UNAM).
Similar to Altzomoni we filter the UNAM measurements with respect to the DOFS
values obtained from the retrieval. We found that a DOFS value of 3.1 is a
good threshold. Lower DOFS are strongly correlated to high noise levels and
in particular to systematic residuals in the 1040–1045 cm-1 region.
This DOFS filter leaves us with 497 individual measurements made on 47
individual days between November 2012 and May 2013.
Logarithmic-scale representation
In the equations and function ()–() a
logarithmic volume mixing ratio scale is used for the atmospheric O3
state (we perform the O3 retrieval on the logarithmic scale). In this
context the errors shown in Figs. and
are from the logarithmic-scale error covariance matrix
Se according to Eq. (); i.e. they represent
actual errors in the logarithm of the O3 volume mixing ratio. We
interpret these logarithmic-scale errors as relative errors, because
dlnx=dxx and Δlnx≈Δxx.
Throughout the paper we will use differentials or differences in the
logarithms of O3 that are equivalent to relative O3 differentials or
differences, e.g. in this section in the context of error assessment studies
and also in the following sections when comparing two different data sets.
For the latter the differences in the logarithms of the two data sets will be
calculated and then interpreted as relative differences.
Free tropospheric and stratospheric O3
In this section we analyse the O3 variations that are not linked to
ozone smog events. To do so we work with free tropospheric O3 data
measured outside Mexico City and with stratospheric O3 data. First we
discuss the seasonal cycles observed above Altzomoni and then demonstrate
to what extent the stratospheric O3 variations can also be observed from
the medium-resolution instrument that is located in the Mexico City boundary
layer.
Seasonal cycle above Altzomoni
Figure depicts the monthly averages of O3 total column
amounts (panel a) and volume mixing ratios for
different altitudes (panel b) for the months in which
there are at least Altzomini FTIR measurements on 3 different days. The error
bars indicate the standard error of the mean, which is the standard deviation
divided by the square root of the number of measurements used when
calculating the mean value. The volume mixing ratios are presented for the
altitudes for which the row kernels are highlighted in
Fig. a. The seasonal variation at
the different altitudes is shown in
Fig. b in a single graph, but with
different volume mixing ratio scale. The data points and the corresponding
volume mixing ratio scales can be identified by different colours: dark grey
for 4 km, red for 17 km, green for 28 km and blue for 42 km.
The seasonal cycle of the O3 total column amounts above Altzomoni is
rather smooth with a maximum in summer (in August 2013 almost 290 DU is
reached) and a minimum in January (the January average is 241 DU). This is
significantly different to the seasonal cycles of O3 total column
amounts observed over subtropical, midlatitudinal and polar sites. There
the maximum is reached in spring and the minimum in autumn
e.g.. A look at the seasonal cycles for different
altitudes can help us to understand the particularity of the seasonal O3
distributions over central Mexico.
Seasonal O3 cycles as obtained from the Altzomoni FTIR
measurements. Means are shown for all the months with measurements for at
least three different days. (a) For the total column
amounts. (b) For the altitudes that are highlighted in the kernel plot of Fig. a.
Please note that for
November–February the monthly means are calculated with data from two
different years (2012/13 and 2013/14) for the other months the monthly means
are calculated from data of 2013 only.
In the free troposphere (represented by the 4 km retrievals) there seems to
be two clearly distinguishable periods. A winter period from November to
February with O3 values of about 0.04 ppmv and a second period from
April to August with O3 values between 0.05 and 0.06 ppmv. This is
similar to the seasonal cycle of midlatitudinal and subtropical tropospheric
O3 found for the two high-altitude stations Izaña and Jungfraujoch
. In an exemplary study for 2006,
found that most of this spring-to-summer increase over
central America occurs between 5 and 12 km altitude. They attributed the
increased tropospheric background O3 levels to the accumulation of earlier
stratospheric O3 input and to more O3 pollution being imported from
lower altitudes. The importance of the Mexico City emissions on free
tropospheric O3 levels in the surroundings of Mexico City has also been
demonstrated in the context of the model study of .
In the upper troposphere and lower stratosphere (UTLS, represented by the
17 km retrievals) the seasonal variation is smoother and there is a slow but
consistent gradual increase in O3 concentrations between January and
August. Unfortunately we do not have a significant number of observations in
September and October, but the August, November and December data seem to
suggest a gradual decrease between August and December/January. The maximum
in late summer and the minimum in winter is very different to what has been
observed at other sites , where the seasonal cycle of
the UTLS is strongly linked to the seasonal variation of the tropopause
height. Between the subtropics and polar regions the tropopause is highest at
the end of the summer and lowest in winter/spring, resulting in low UTLS
O3 volume mixing ratios in summer and high ratios in winter. Above
Altzomoni it is the other way round. The reason is the importance of
isentropic mixing of O3-rich air from higher latitudes into the UTLS
above Altzomoni. This mixing is strongest in summer and is also seen in
space-based observations .
investigated the longitudinal, seasonal and interannual variations of these
mixing events and showed that the mixing is strongest in the Northern
Hemispheric summer and is close to the Asian and North American monsoon regions.
Furthermore, they found particularly strong mixing for negative ENSO (El
Niño Southern Oscillation) years. Isentropic mixing may be determining
the seasonal signal in the UTLS region and also affecting the seasonal cycle of
the total column amounts in central Mexico, but this needs to be further
investigated. Long-term FTIR measurements at Altzomoni, which is situated in
the North American monsoon region, will allow us to study these
stratosphere–troposphere exchange processes and their link to climate
patterns like ENSO.
In the middle stratosphere (represented by the 28 km retrievals) we observe
an increase between January and May and a decrease between May and November;
i.e. the maximum values are already achieved in May. For the retrieval at
42 km (representative for the middle and upper stratosphere), we observe a
maximum in late summer and a minimum in winter. These middle and upper
stratospheric cycles at Altzomoni are similar to the cycles observed at
the subtropical site of Izaña .
Intercomparison between UNAM and Altzomoni data
When comparing different remote sensing products we have to consider the
respective averaging kernels. The kernels as depicted in
Figs. and reveal that at Altzomoni we
can observe more details of the vertical O3 distribution compared to
UNAM. Therefore before comparing the Altzomoni and the UNAM retrieval
products we have to account for this difference by smoothing the retrieved
Altzomoni O3 state vector (xALTZ) with the UNAM
averaging kernel (AUNAM).
xALTZ∗=AUNAMxALTZ-xaALTZ+xaALTZ.
Here the vectors xALTZ and xaALTZ have
been expanded by three additional dimensions, which correspond to the three
altitude grid levels below 4 km. For these three grid levels we set
xALTZ=xaALTZ=xaUNAM.
Correlation of O3 data products obtained from the Altzomoni and
UNAM FTIR measurements. The yellow stars represent the a priori values, the
black lines are the one-to-one diagonals and the red lines are the fitted linear
regression lines. (a) Total column abundances. (b) Volume
mixing ratios at 17 and 32 km a.s.l. (in the UNAM kernel plot of
Fig. the respective kernels are highlighted by red and
green colour). Please note that for this comparison the Altzomoni data have
been smoothed by the UNAM averaging kernels according to
Eq. ().
Applying Eq. () makes the data more comparable but does
not fully remove the effect of the different averaging kernels. In order to
ensure that we perform a reasonable comparison we calculate the covariances
Scmp which estimates the averaging-kernel-induced uncertainty
for the comparison between the UNAM remote sensing data and the Altzomoni
remote sensing data after smoothing according to Eq. ().
According to we calculate
Scmp=AUNAM-AUNAMAALTZScovAUNAM-AUNAMAALTZT,
where AUNAM is the averaging kernel matrix for UNAM
and AALTZ is the averaging kernel matrix for Altzomoni,
being expanded by three columns and three rows with 0.0 entries
corresponding to the three altitude grids below 4 km altitude. The matrix
Scov describes the O3 covariances in the atmosphere. Here
we use Scov=Scov,meas. (see Appendix
for more details). For a reasonable comparison we require that the square
root of the diagonal of Scmp that represents the altitude under
consideration is smaller than 5 %.
Figure shows a comparison of the total column amounts as
well as of volume mixing ratios at 17 km and 32 km a.s.l. after applying
the smoothing from Eq. () and the aforementioned filtering.
We pair UNAM and Altzomoni data that are measured within 2 h and then
calculate the hourly means for all the pairs. This gives 53 individual data
pairs that belong to measurements made on 20 individual days and over 6
months from November 2012 to April 2013.
The total columns are calculated from the retrieved UNAM profiles (state
vector xUNAM) and the retrieved and smoothed Altzomoni
profiles (state vector xALTZ∗ above 4 km, according
to Eq. ). As documented by
Fig. a according to the model WACCM,
about 97 % of the O3 total column abundances measured at UNAM are
situated above the altitude of Altzomoni and indeed we observe a very good
correlation between the data from both stations (a correlation coefficient
R2 of 92 % and a slope m for the linear regression line of 1.00).
The mean difference (UNAM-Altzomoni) is +4.9 % and
the 1σ scatter is 1.4 %. The bias of +4.9 % is in
reasonable agreement with the typical relative O3 abundances between 2.3
and 4 km (see black line in Fig. a).
The correlations between the volume mixing ratios obtained at 17 km are also
strong (R2 of 87 %). However the slope of the regression line is
larger than unity (m=1.17). For the mixing ratios at 32 km the correlation
is a bit weaker, although still clearly observable (R2 is 75 %), and
the slope of the regression line is close to unity (m=0.90). The mean
differences and scatter (UNAM-Altzomoni) are
+13.3 % ± 4.3 % for 17 km and -1.1 % ± 2.3 % for
32 km, respectively. The bias and scatter between the two data sets might be
explained by uncorrelated errors in the data, remaining differences in the
smoothing characteristics (although of less importance due to applying the
filter based on the calculation according to Eq. ) and the
detection of different air masses.
Boundary layer
This section focuses on the O3 volume mixing ratios in the boundary
layer of the Mexico City basins, for which variations are driven mainly by
photochemistry from anthropogenic pollutants (photochemical ozone smog).
In situ monitoring of air pollution in Mexico City
To validate the boundary layer remote sensing product we use in situ
measurements made in Mexico City in the framework of the RAMA (Red
Automática de Monitoreo Atmosférico) network. RAMA performs
continuous measurements of different gases and particles at 34 stations
spread around Mexico City in order to provide information about the air quality of
this megacity (more details about RAMA are available at
http://www.aire.df.gob.mx/default.php).
The in situ O3 monitoring data are freely available with hourly time
resolution. We work with the three in situ stations Pedregal (PED), Santa
Ursula (SUR) and Coyoacan (COY), which are all situated within a circle of
about 5 km radius around the UNAM station, thereby constituting an excellent
reference for assessing the potential of the UNAM remote sensing experiment
for observing boundary layer O3 mixing ratios. We calculate the mean
value from the three stations for each hour and only consider situations
where all three stations provide data.
Figure depicts the monthly means of the diurnal cycles
obtained from these three in situ stations and represents the known O3
variability with highest monthly surface averages during the months
March–May. It is during this period that the city suffers the largest number
of exceedances and that the government activates the contingency plans for
minimizing the high O3 pollution episodes. The observed high
diurnal variability, which is evidently larger than the seasonal variability,
reveals the photochemical nature and reactivity of this urban atmosphere. The
blue dots depict the time period in which the FTIR measurements are typically
performed (between 11:00 and 14:00 local time). Within these 3 h O3
concentrations show a strong increase. Any comparison study has to consider
these fast-changing O3 concentrations and we only compare data that are
measured within the same hour. A discussion on the O3 boundary layer
variability of Mexico City on timescales beyond the diurnal timescale is
given in .
Intercomparison of remote sensing data with in situ data
For the comparison between the in situ and remote sensing data, we require
that during a certain hour (given in local time) all the three experiments
provide at least one measurement. We created a table that contains the data
from the three experiments for these coincidences and then calculated the
hourly mean data. This gives 32 individual hourly mean data triplets
belonging to measurements made during 15 individual days over 6 months (between November 2012 and April 2013).
Seasonality of the diurnal cycles of O3 volume mixing ratios
measured by the three in situ monitoring stations at about 2.3 km a.s.l.
close to UNAM. The blue dots indicate the time frame when FTIR measurements
at UNAM have been typically performed.
Correlation between O3 volume mixing ratios measured by the in
situ instruments for the boundary layer of Mexico City and the ratios
obtained by the remote sensing experiments. The yellow stars represent the a
priori values, the black lines are the one-to-one diagonals and the red lines
are the fitted linear regression lines. (a) In situ O3 versus
O3 obtained from the Altzomoni FTIR measurements for
4 km a.s.l.
(b) In situ O3 versus O3 obtained from the FTIR UNAM
measurements for 2.3 km a.s.l. (c) In situ O3 versus the
combined FTIR O3 product for 2.3 km a.s.l. (calculation according to
Eq. ).
Figure shows the correlation plots between the O3 in
situ data (measured in the Mexico City boundary layer at about
2.3 km a.s.l.) and the O3 values obtained for the lowermost altitude
for the retrievals at Altzomoni and UNAM. Panel (a)
depicts the correlation between the in situ data and the Altzomoni retrieval
product for 4 km a.s.l. We observe no correlation (R2 < 1 %) and
a mean difference and 1σ standard deviation of the difference of
-31.8 % ± 24.4 % (for Altzomoni – in situ). This is not
surprising given the significant horizontal distance between the location of
the in situ instruments and Altzomoni and the fact the Altzomoni data have no
sensitivity below 4 km a.s.l. This implies that the variation observed by
the in situ instruments are rather local and vertically and/or horizontally
limited to the area around the in situ instruments (or the basin of Mexico
City).
Figure b presents the correlation
between the same in situ data and the UNAM retrieval product for
2.3 km a.s.l. There is a clear correlation between the two data sets (R2=54 %), although the UNAM FTIR data do not fully capture the magnitude
of the O3 variability at the surface (slope m of the linear regression
line is only 0.53). A slope of below 0.5 is actually what can be expected
from the respective averaging kernels. The thick black line in
Fig. depicts the row kernel for the UNAM retrieval at
2.3 km. Summing up all the contributions of the row kernel for the boundary
layer (values between surface and 4 km a.s.l.) we get about 0.35. If we
assume that the diurnal O3 increase is present throughout the entire
boundary layer and if we further assume that it is stronger a few hundred
metres above the surface (compared to the increase observed in the mean
values of the three in situ stations), we can expect a slope above 0.35.
Between 09:00 and 14:00 LT such an assumption is reasonable, judging from
the vertical O3 profiles shown in and the depth of
the boundary layer . The mean difference and 1σ
standard deviation of the difference is -12.0 % ± 16.2 % (for
UNAM – in situ).
The good agreement with the in situ data empirically proves the profiling
capability of the UNAM FTIR experiment: it is able to detect the O3
variations that take place in the Mexico City boundary layer, although it is
a relatively thin layer containing only a small portion of the total column
O3 abundance above UNAM (see
Fig. a).
O3 row kernels for the lowermost altitude of different FTIR
products. Black: 2.3 km row kernel for the UNAM product; grey: 4 km row
kernel for the Altzomoni product; red: 2.3 km row kernel for the combined
product.
A combined UNAM/Altzomoni remote sensing product for the boundary layer
We have two remote sensing experiments located close to each other but at
different altitudes. In this section we present a product that combines the
measurement made by the two instruments. The objective is to investigate
whether the combination can improve the remote sensing boundary layer data.
A simple method is to calculate the differences between the total columns
measured above UNAM and above Altzomoni, i.e. the differences of the total
column values depicted in Fig. a.
However, we have to consider that the partial column between 2.3 and 4 km is
only about 2.5 % of the total column amount (see
Fig. a). Even after treating the
Altzomoni data with the UNAM kernel (according to Eq. ),
the UNAM and the Altzomoni total column still do not have the same
sensitivity. Actually if we use the metric as described in the context of
Eq. () we find that the partial columns above 4 km altitude
can typically agree only within 0.9 %. A better agreement cannot be
expected due to the different sensitivities of the two remote sensing
experiments. In addition there are errors in the total column amounts, which
are estimated at 2.5–3 % for each of the two experiments (see
Table ). In summary, the difference between the two
column amounts is affected by different sensitivities and errors, and both
effects together sum up to about 4–5 %. This is larger than the expected
value for the difference of about 2.5 % and in addition it has to be
considered that the boundary layer sensitivity is only 35 % (sum of the
diagonal elements of the averaging kernel corresponding to boundary layer
altitudes; see discussion in the context of
Fig. b). We cannot expect to detect
a 2.5 % × 35 % < 0.9 % signal by a measure that has
an uncertainty of 4–5 %, and as a consequence the difference of the
total column amounts cannot provide useful information about the partial
column between 2.3 and 4 km. Actually, we have calculated the total column
amount differences and indeed find no correlation with the boundary layer in
situ data.
We need a more sophisticated method to combine the two experiments. We can
start with the UNAM product at 2.3 km, which shows correlations to the
boundary layer in situ data (recall
Fig. b). The typical row kernel for
the UNAM retrieval at 2.3 km is depicted as thick black line in
Fig. . The kernel indicates some sensitivity for
altitudes at and below 4 km, although the sum of the diagonal elements of
the kernel corresponding to these altitudes is typically 0.35, i.e.
significantly smaller than 1.0. It can also be seen that the 2.3 km
retrieval is sensitive to the actual atmosphere above 4 km (the row kernel
values only slowly decrease for altitudes above 4 km); i.e. free
tropospheric O3 variations can significantly interfere with the boundary
layer variations. Our idea is to improve the sensitivity for the boundary
layer and at the same time reduce the interferences from higher altitudes by
combining the UNAM measurements with the Altzomoni measurements. The
Altzomoni data are promising for reducing the interferences because they are
sensitive to the free troposphere above 4 km, but completely insensitive to
the boundary layer below 4 km (see the row averaging kernel for the
Altzomoni retrieval at 4 km depicted as a thick grey line in
Fig. ).
Analytical description of the combined product
We introduce an operator C to combine the two retrievals:
C=1∑iϵBLacomb(i,i)I-AUNAM.
Here I is a
nol × nol identity matrix
and AUNAM the nol × nol
averaging kernel matrix for UNAM, where nol is the number of grid
points of the model atmosphere used for the UNAM retrieval process (i.e. here
nol = 44). So C is a nol×(2×nol) matrix. The matrix is normalized to
∑iϵBLacomb(i,i), where
acomb(i,i) are the diagonal elements of
Acomb and iϵBL are the indices for the
altitudes at and below 4 km (i.e. the boundary layer, BL). The matrix
Acomb is calculated according to Eq. (),
so to calculate Acomb we actually need the operator
C, which in its turn needs diagonal elements from matrix
Acomb. So we need two steps to calculate C
and Acomb correctly: first we apply
Eq. () using 1.0 for the normalization, then we apply
Acomb according to Eq. (). This gives us
the correct normalization factor with which to calculate the correct operator
C.
With the combination operator C the combined product can be
calculated and comprehensively characterized in a similar way to the
individual products. The combined state vector xcomb can be
calculated from the UNAM and Altzomoni state vectors (xUNAM
and xALTZ, respectively) as follows:
xcomb=CxUNAMxALTZ-xaUNAMxaALTZ+xaUNAM.
Here xaUNAM and xaALTZ are the a
priori state vectors for UNAM and Altzomoni, which are identical. As for
Eq. () the Altzomoni state vectors are expanded to 44 dimensions and we define
xALTZ=xaALTZ=xaUNAM
for the three vector components corresponding to the three altitude levels
below 4 km.
The averaging kernel for the combined product can be calculated as follows:
Acomb=CAUNAMAALTZ.
Here the matrix AALTZ is the same as in
Eq. (); i.e. the original averaging kernel matrix of Altzomoni
expanded by three columns and three rows with 0.0 entries corresponding to
the three altitude grids below 4 km altitude (this gives a
nol×nol matrix).
Similarly to Eq. () we can calculate error covariances for the
combined product:
Se,comb=CGUNAM00GALTZKp,UNAM00Kp,ALTZSp,UNAMSp,xSp,xSp,ALTZKp,UNAMT00Kp,ALTZTGUNAMT00GALTZTCT.
Here the matrix GALTZ is the original gain matrix for
Altzomoni expanded by three lines (corresponding to the three altitude grid
levels below 4 km) to nol lines. The entries in these lines are
0.0. The matrices Kp,UNAM and
Kp,ALTZ are the Jacobians with respect to parameter
p and the matrices Sp,UNAM and
Sp,ALTZ give the uncertainty covariances for
parameters p for the UNAM and the Altzomoni retrievals, respectively. The
block Sp,x defines the correlation between uncertainties for
UNAM and Altzomoni. Here we assume that all uncertainties are uncorrelated,
except for the temperature uncertainties above 12.5 km, for which we assume
full correlation (i.e. Sp,x has only entries for the elements
that correspond to temperature uncertainties above 12.5 km). In the
troposphere we assume that the uncertainty in the temperatures above UNAM and
Altzomoni are uncorrelated, because here small-scale variations are likely.
For altitudes above 12.5 km we assume correlated temperature uncertainties
because at these altitudes the variations on smaller scales are less likely.
In this context please be aware that for both sites we assume the same
temperatures, which are from NCEP and the climatology of CIRA for altitudes
above 50 km.
The working principle of the combined product
In the previous subsection we give the formula that analytically describes the
characteristics of the combined product. It is fully traceable to the
Jacobians matrices, gain matrices and averaging kernel matrices of the
individual retrieval products (see Eqs. –). In this subsection we provide some additional
intuitive explanations with the objective of better communicating the working
principle of the method.
For our combined product we are able to optimize the boundary layer data
quality, because to the measurement in the boundary layer (the UNAM spectrum)
we add a second measurement above it (the Altzomoni spectrum). The best way
to do this would be to fit both measured spectra within a single inversion
process similar to what is done for limb sounding retrievals,
e.g., which would,
however, mean the set-up of a new inversion algorithm software. Our method is
a workaround of such multispectral inversion algorithm, because it works with
the two individual retrieval results and not with the individual
measurements. The method consists of an a posteriori combination of the two
individual retrieval results with the objective of optimally exploiting the
synergies of the two individual measurements.
Equation () shows how the combined product is calculated. By
inserting C from Eq. () we get the following:
1∑iϵBLacomb(i,i)xUNAM-xaUNAM-AUNAMxALTZ-xaALTZ+xaUNAM.
This reveals that the method consists in principle of calculating the
differences of the two measurements, but also accounts for the different
sensitivities and ensures that the kernel of the combined product is
normalized for the boundary layer (it is required that the sum of its
boundary layer diagonal elements is 1.0).
Discussion
The row of Acomb corresponding to 2.3 km is depicted as
a thick red line in Fig. and indicates that, in
comparison to the UNAM product, the combined retrieval product has a much
larger sensitivity in the boundary layer but at the same time much less
sensitivity to variations that occur above 4 km. In this context our
approach using two remote sensing experiments observing at different
altitudes seems to be very promising.
However, we also have to consider the errors. By combining two measurements
we increase the errors because the errors of the two measurements are largely
independent. In addition, by using the normalization factor we make the data
more sensitive to actual atmospheric variations, but also to uncertainty
sources. Table collects the errors estimated for the UNAM
and the combined product at 2.3 km altitude. The values are the square roots
of the diagonal entries of Se and
Se,comb, according to Eqs. () and
(), respectively, which correspond to the altitude of
2.3 km. Because we assume that the UNAM and Altzomoni uncertainties are
uncorrelated (except for temperatures uncertainties above 12.5 km) and
because we increase the sensitivity (by using the normalization factor), the
errors are significantly larger in the combined product than in the UNAM
product. We estimate that the total statistical and systematic errors are
as large as 20–25 %. So while the combination strongly reduces interferences
from O3 variations above 4 km and increases the sensitivity to actual
boundary layer O3 variations, it significantly increases the errors by
summing up error contributions from two different experiments and by
increasing the sensitivity to all uncertainty sources.
In Fig. c we compare the combined
product with the in situ data (for exactly the same coincidences that are
shown in panels a and b). We find that the combined
product clearly correlates better with the in situ data than the UNAM product
(R2 increases from 54 to 70 %). The slope m does increase
significantly, which is achieved by the normalization factor when calculating
C according to Eq. (). The normalization factor
ensures that for the combined product the DOFS for the boundary layer is 1.0.
This result is clear empirical evidence that by combining the solar
absorption spectra measurements of UNAM and Altzomoni according to
Eq. (), we can generate data that capture the variations
taking place in the Mexico City boundary layer well. However, at the same
time we observe a significant bias with respect to the in situ data. The mean
difference and 1σ standard deviation of the difference is
+29.3 % ± 19.0 % (combined – in situ). This bias is above
the upper limit of the estimated systematic errors.
Estimated errors for the boundary layer O3 remote sensing
(retrieval results for 2.3 km altitude). Listed are statistical and
systematic errors of the UNAM product and the combined product.
Error source
UNAM product
Combined product
Stat./sys.
Stat./sys.
Measurement noise
1.3 %/–
4.3 %/–
Baseline
1.0 %/1.0 %
4.4 %/4.4 %
Instrumental line shape
4.7 %/4.7 %
14.5 %/14.5 %
Temperature
3.4 %/1.5 %
6.5 %/2.8 %
Line of sight
0.1 %/< 0.1 %
0.5 %/0.1 %
Solar lines
< 0.1 %/< 0.1 %
0.1 %/< 0.1 %
O3 spectroscopy
–/5.3 %
–/17.7 %
Interference with H2O
< 0.1 %/< 0.1 %
< 0.1 %/< 0.1 %
Total
6.0 %/7.3 %
17.1 %/23.5 %
There can be different reasons for this bias. It might be that we
underestimate the systematic errors by assuming too low uncertainties in the
spectroscopic line parameter data of O3 or in the modulation efficiency.
On the other hand at least part of the systematic difference between the
in situ and the remote sensing data might be explained by the fact that the
two measurement techniques observe different air masses. The in situ
instruments detect air at 2–5 m above the surface, while the remote sensing
data represent the first 2 km above the surface. In model studies
e.g. as well as from observations
e.g., increased O3 concentrations
have been found about 1 km above the surface. This increase from the surface
up to 1 km above the surface seems to be particularly significant in the
morning and disappears at midday . So when interpreting
the differences between the in situ and remote sensing data we have to keep
in mind that both data represent different altitude regions. Actually this
makes the remote sensing data especially interesting: they complement the
in situ data by giving information about a 2 km thick layer above the
surface, thus opening the possibility for extraordinary
enhancements to be detected, such as in , or for
a residual layer within the boundary layer to be quantified as is suggested in this study.
Summary and outlook
To our knowledge we present the first ground-based FTIR remote sensing data
of O3 profiles for Latin America. We work with two different FTIR
experiments, one of which is located in the megacity of Mexico City and another about
1700 m above the city at a distance of 60 km. The instrument in the city is
a moderate resolution instrument and is installed on the top of a building at
the main UNAM campus. The instrument outside the city is a high-resolution
instrument, situated on the high-altitude observatory of Altzomoni and
contributes to the NDACC (currently it is the highest NDACC FTIR station
worldwide).
It is demonstrated that with the high-altitude NDACC instrument one can
typically detect the O3 volume mixing ratio variations of four different
altitude regions: the free troposphere, the UTLS, the middle stratosphere and
the middle/upper stratosphere. This is in agreement with other sites for which
ground-based FTIR remote sensing profiles of O3 have been characterized.
We found that the error in the profiles are typically within 5 % except
for altitudes above 30 km, where they can reach 10–20 %. Statistic
errors are dominated by uncertainties in the used atmospheric temperature
profiles and systematic errors are mainly due to uncertainties in the
spectroscopic line parameters of O3. These results are in agreement with
previous error estimation studies for NDACC FTIR O3 data retrievals at
other stations.
We show a first error estimation for O3 profiles obtained for retrievals
that use moderate-resolution solar absorption spectra (the UNAM
measurements). The estimated profile errors are below 10 % between
surface and 50 km, being dominated by uncertainties in atmospheric
temperature (statistical error) and uncertainties baseline of the measured
spectra and the spectroscopic line parameters of O3 (systematic error).
It is documented that remote sensing with the moderate-resolution instruments
still allows three different altitude regions to be detected: the first 7 km
above the surface, the UTLS region and the middle/upper stratosphere.
The theoretical error estimations are confirmed by comparing the UNAM FTIR
product with the Altzomoni FTIR product. We find very good agreement for both
data sets concerning total column amounts, UTLS volume mixing ratios and the
ratios in the middle/upper stratosphere. A comparison of the lowermost layer
is not possible, because the UNAM FTIR data are strongly affected by local
O3 variations in the polluted boundary layer of Mexico City, whereas the
Altzomoni FTIR experiment is measuring mainly above this layer. To
empirically validate the boundary layer data obtained from the UNAM remote
sensing retrievals, we use in situ data obtained at three stations close to
the UNAM instrument. We found good correlation between the UNAM FTIR and
the in situ boundary layer O3 data and a slope of the linear regression
line that is in agreement with the sensitivity as given by the averaging
kernels.
The Altzomoni FTIR measurements will be very useful in the future, when
several years of measurements are available. The seasonal cycles observed
in the total column and the UTLS above Altzomoni are distinct to the cycles
observed at all the other NDACC FTIR stations in the subtropics,
midlatitudes and polar regions. The observation of a UTLS O3 maximum in
late summer instead of late winter and spring (like at other sites) is most
probably due to isentropic mixing of O3-rich air from the high
latitudinal stratosphere. This mixing takes place in the northern tropics. In
previous studies it has been shown that it is linked to climate patterns like
the Asian and North American monsoons and that it is affected by the ENSO
phase. Establishing the Altzomoni FTIR as a long-term activity might in the
future enable us to investigate the importance of climate patterns, such as the
North American monsoon or the ENSO, for such stratosphere–troposphere
exchange events.
Having two nearby remote sensing measurements, a first one in the boundary
layer and a second one just above this layer most of the time, gives the
opportunity of creating a combined remote sensing product with improved
representativeness of the near-surface processes. We have introduced the
theory of such combined product and characterized it by calculating averaging
kernels and errors. We are able to prove that the combined FTIR O3
boundary layer product better represents the near-surface variations than the
product obtained by a single FTIR experiment. The approach for combining the
retrievals of the two FTIR experiments can be applied for any other trace gas
in order to reliably detect the Mexico City boundary layer variations of many
different trace gases. Nevertheless, for scientific usage of this remote
sensing boundary layer product it is important to better understand the bias
of 30 % with respect to the in situ data. The
bias can be due to systematic errors in the remote sensing data, but also due
to the fact that the remote sensing experiments observe different air masses.
By measuring O3 profiles with radiosondes one can detect the same air
masses as the remote sensing experiments. In this context, performing in situ
O3 profile measurements at the UNAM station (radiosonde or balloon) at
the same time as Altzomoni and UNAM FTIR measurements would be very helpful,
since it could help us to understand the actual systematic error in the
boundary layer remote sensing data. With more data and further analysis, our
product could be used to quantify the O3 residual layer and to
understand the complex dynamical processes affecting the large variability
and high episodes taking place at the surface.