Introduction
The upper troposphere/lower stratosphere (UTLS) is a highly dynamic region,
the composition of which is
determined by the interaction of stirring and mixing processes with transport
barriers. It exerts its influence on the whole climate system
e.g.. In this region, the subtropical jet forms
typically a barrier for troposphere-stratosphere exchange, which can weaken in
the presence of breaking Rossby waves and thus allow for isentropic transport
e.g.. Especially during summer, when the jet is
weak, the UTLS over Europe consists of a cascade of small filaments
.
Examining this region with airborne in situ instruments gives precise and accurate
information on the trace gas distribution confined to the flight path but allows
thus only for sketchy coverage. Using remote sensing instruments with a high
vertical resolution such as limb sounders offers a much more complete spatial
picture. Observing the
atmosphere with limb sounders from space e.g. has greatly increased our knowledge of the dynamical and
chemical structure of the atmosphere, but satellite-born instruments lack the
vertical resolution to observe the strong vertical gradients occurring around the
tropopause. Airborne limb sounders close the gap between in situ and space
instruments and thus allow for the observation of small-scale structures such as the
tropopause inversion layer .
The Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA)
is the first realisation of the limb-imagining technique originally proposed
for satellite applications . The instrument
combines an imaging detector with a Fourier transform spectrometer. To operate
on an aircraft, it is placed in a gimbal (a cardanic frame), which is used to
stabilise the pointing against movements of the carrier and to adjust the
viewing direction. It offers
a high vertical resolution of down to 250 m and, in combination with
tomographic measurement patterns, even the 3-D reconstruction of
atmospheric structures is feasible . GLORIA can be
tuned between the highly spatial “dynamics mode” and the highly spectral
“chemistry mode” resolution (see Sect. ). GLORIA was first operated on the
Geophysica research aircraft during the Esa Sounder Campaign (EsSenCe;
) based in Kiruna, Sweden, in 2011 with a limited number
of measured profiles. The first deployment with extended data coverage took
place during the TACTS (Transport And Composition in the upper
Troposphere/lower most Stratosphere) and ESMVal (Earth System Model
Validation) campaigns in the High Altitude LOng Range (HALO) aircraft
(a Gulfstream G550) in summer 2012.
This paper provides a complete picture of the 1-D level 2 processing of
GLORIA “dynamics mode” data. It continues the work presented by
by also providing the important tropospheric tracer
H2O with high sensitivity in the lower stratosphere. The selection of
spectral regions has been greatly improved upon by incorporating new insights
into instrument behaviour. Further, the accuracy of the retrievals has been
improved by employing a more accurate radiative transfer model. Lastly,
a detailed validation is presented that exploits the available in situ
instrumentation aboard HALO. While the described 1-D retrievals are
inaccurate in the presence of horizontal gradients (in contrast to the
3-D tomography of ), they do not require dedicated
flight patterns as tomography and can quickly provide an overview picture of the
dynamic situation.
First, the instrument will be described followed by a description of the
“dynamics mode” level 2 processor and the used configuration designated V1.00 for the GLORIA
data processing. The paper proceeds by presenting the derived distributions of
temperature and trace gas mixing ratios of H2O, O3, and
HNO3 from 7 to 15 km altitudes above northern Europe on
26 September 2012. After the discussion of systematic errors, the retrieved
trace gas distributions are validated against simultaneous in situ observations
on HALO during three flights in September 2012 covering polar, mid-latitude, and
subtropical regions.
Level 2 processing
This section gives an overview over the level 2 processing of calibrated GLORIA
dynamics mode spectra that were produced by the Python-based GLORIA level 0/level 1
processors named “gloripy”. These processors transform the detector signals as a function of the
interferometer sledge position to calibrated spectra seefor
details. The level 2 processing by the
Juelich Rapid Spectral Simulation Code V2 (JURASSIC2) and the JUelich
Tomographic Inversion Library (JUTIL) software packages map the radiance values
measured at different elevation angles (that is tangent altitudes) to the
geophysical quantities of temperature, trace gas volume mixing ratios, and
extinction values. This forms an ill-posed problem that is approximated by
a well posed one by means of a Tikhonov-type regularisation .
Let F:Rn↦Rm, n,m∈N, be
a (forward) model that maps a discrete representation of the atmospheric state
x∈Rn onto a set of radiances. The set of (imperfect)
measurements is represented by a vector y∈Rm, and the assumed
(prior) state of the atmosphere is given as xa∈Rn.
Approximating the behaviour of the instrument noise by a Gaussian error
covariance matrix Sϵ∈Rm×m and the
vertical correlation of atmospheric state variables by a Gaussian covariance matrix
Sa∈Rn×n, the original problem is approximated by
a minimisation problem:
J(x)=F(x)-yTSϵ-1F(x)-y+x-xaTSa-1x-xa⟶min.
This problem can be efficiently solved by quasi Newton-type methods, in our case
a truncated conjugate gradient-based trust–region scheme .
Retrieval targets
The aim of the inversion is here to retrieve the primary targets of temperature,
water vapour (H2O), ozone (O3), and nitric acid (HNO3).
The secondary targets of carbon tetrachloride (CCl4), CFC-11, and CFC-12
are retrieved to reduce systematic errors due to these background gases.
The Antarctic flight requires additionally the derivation of
chlorine nitrate (ClONO2) due to the large encountered volume mixing ratios (VMRs). In
addition, five different aerosol extinction profiles are
retrieved, whereby each aerosol is applied only to a non-overlapping spectral
region, and it is assumed that the optical characteristics of an aerosol remains
approximately constant over its applicable wavenumber range (see
Table ). The listed integrated spectral windows (ISWs) used for the
retrievals were selected by a genetic algorithm, which identifies the
location and width of ISWs that maximises the information gain. The algorithm
recombines initially randomly selected sets of ISWs preferring ”good” sets and
thus identifies a (nearly) optimal set much faster than a simple brute force
search. Details are given by . The resulting windows were then
modified to mitigate discovered instrument artefacts such as imperfectly
compensated emissions of the outer window due to fast temperature changes.
Generally, the volume
mixing ratio of trace gases is retrieved. But for H2O,
the logarithm of the VMR is retrieved instead of the
unmodified VMR. From a statistical point of view, this assumes that H2O
VMRs follow a log-normal distribution, which can be justified in the target
altitude range from radiosonde measurements e.g.. A full
list of atmospheric quantities taken into account in the retrieval is given in
Table .
The retrieval grid has a sampling distance of 125 m between the surface
and 18 km altitude, from where on the sampling becomes increasingly
sparse: 1 above 18 km, 2 above 24 km, and
4 above 30 km, with 60 km being the highest altitude.
All targets are retrieved up to 20 km altitude except for O3 and
HNO3, which are retrieved up to 60 km.
A list of integrated spectral windows (ISW) employed and their
spectral range. The last four columns shown the bias and SD
of the band and monochromatic model compared to RFM.
aerosol
band
monochromatic
ISW
index
range (cm-1)
bias (‰)
stddev (‰)
bias (‰)
stddev (‰)
0
0
790.625–791.250
2.851
0.762
0.074
0.011
1
0
791.875–792.500
-1.431
0.282
0.073
0.012
2
0
793.125–793.750
-2.075
0.531
0.160
0.019
3
0
794.375–795.000
1.634
0.490
0.124
0.019
4
0
795.625–796.250
-0.380
1.526
0.045
0.010
5
0
796.875–797.500
-0.028
0.740
-0.017
0.010
6
0
798.125–798.750
6.513
5.082
-0.108
0.030
7
0
799.375–799.375
1.168
2.487
-0.089
0.026
8
1
845.000–849.375
-0.016
0.251
0.047
0.008
9
1
850.000–854.375
-0.924
0.229
-0.238
0.023
10
1
855.000–859.375
-0.739
0.231
-0.177
0.027
11
2
883.750–888.125
-1.683
0.134
0.012
0.003
12
2
892.500–896.250
-0.998
0.099
-0.017
0.004
13
2
900.000–903.125
-0.889
0.144
0.119
0.026
14
2
918.750–923.125
0.233
0.236
0.048
0.017
15
3
956.875–962.500
-2.518
0.861
0.066
0.009
16
3
980.000–984.375
-5.826
0.696
0.076
0.005
17
3
992.500–997.500
-2.909
0.406
0.055
0.004
18
3
1000.625–1006.250
-0.696
0.337
0.023
0.004
19
3
1010.000–1014.375
-0.243
0.179
0.003
0.002
20
4
1388.125–1389.375
3.001
1.910
-0.001
0.013
21
4
1390.000–1391.250
1.102
0.812
0.012
0.024
22
4
1391.875–1393.125
-0.264
0.545
-0.002
0.036
23
4
1393.750–1395.000
0.391
0.476
0.001
0.004
24
4
1395.625–1396.875
0.973
1.418
0.016
0.019
25
4
1397.500–1398.750
-0.932
0.401
0.005
0.022
Regularisation and model a priori data
The inverse problem is inherently ill-posed and requires some additional
constraints to provide physically meaningful results. The JUTIL software package
supports several regularisation schemes. For the processing of the data
presented here, Tikhonov regularisation was chosen in combination with a rather
weak climatological weighting. This regularisation largely follows the
evaluation of previous campaigns e.g. with slight changes
due to the different signal-to-noise characteristics of the GLORIA
instrument.
The precision matrix Sa-1 is defined as
Sa-1=(α0)2L0TL0+(α1)2L1TL1+(α2)2L2TL2,
with α0,α1,α2∈R and
L0,L1,L2∈Rn×n. The constraint can be
separated into one constraint on the absolute value of retrieved target
compared to a (climatological) mean weighted with its standard deviation (SD) and two
smoothness criteria.
The matrix L0 thus consists of a diagonal
containing the reciprocal values of the SDs of the retrieved
entities. The matrix L1 is a matrix to compute the first
derivative of the vector xi by finite differences (it has -1 is on the
main diagonal and 1 is on the first upper side diagonal, except for some rows that
would take the difference of different quantities or non-neighbouring values).
In addition each row of L1 is scaled with the reciprocal of the
SD and cq/(2hi), with cq being
a quantity q specific correlation length and hi being the vertical distance
between the elements of the vector that are being subtracted from each other
. Table lists the empirically derived
correlations lengths.
L2 is set up similarly to L1 but with finite differences
approximating the second derivative instead of the first.
The sources for a priori values, background values, and
SDs are listed in Table .
Vertical correlation lengths employed for the regularisation.
parameter
value
parameter
value
caerosol0
640 km
caerosol1
640 km
caerosol2
640 km
caerosol3
640 km
caerosol4
10 km
ctemperature
0.9 km
cCCl4
2 km
cCFC-11
8 km
cCFC-12
8 km
cH2O
5 km
cHNO3
4 km
cO3
40 km
Sources for a priori or background values and associated SDs for atmospheric quantities. CLIM refers to the climatology by
, ECMWF and WACCM to the respective models, and GLATTHOR
refers to the profile derived by , where the
derived values are taken as 1-sigma uncertainty.
quantity
value
SD
temperature
ECMWF
1 K
pressure
ECMWF
0.3 %
CCl4
CLIM
CLIM
ClONO2
CLIM
CLIM
CH4
WACCM
CLIM
CO2
WACCM
CLIM
CFC-F11
CLIM
CLIM
CFC-12
CLIM
CLIM
CFC-113
CLIM
CLIM
CFC-114
CLIM
CLIM
HCFC-22
CLIM
CLIM
H2O
4 ppm
–
HNO3
CLIM
CLIM
HNO4
CLIM
CLIM
N2O
CLIM
CLIM
NH3
CLIM
CLIM
O3
CLIM
CLIM
OCS
CLIM
CLIM
SF6
CLIM
CLIM
SO2
CLIM
CLIM
PAN
0
GLATTHOR
gain
1
1 %
offset
0
5 nWcm-2sr-1cm
elevation
0
0.023∘
Generally α0 is chosen to be 0.1, which can be interpreted as an
increase of the SD by a factor of 10 for regularisation of 0th
order. The other α values are set to 1. For temperature and water vapour, the absolute
value remains fully unconstrained. The vertical derivative computed by
L1 of the profiles is
constrained for all retrieval targets except for temperature.
The log-normally distributed water vapour needs
to be exempted here as the SD values given in the climatology
were prepared
assuming a normal distribution; in effect, no altitude-dependent scaling is
performed for H2O (i.e. a SD of 1 is assumed for the
log-normally distributed water vapour). Lastly, the second vertical derivative
of the
temperature profile is constrained by the L2 matrix to produce
temperature profiles with a smoother lapse rate.
As input to the retrieval, analysis data supplied by the European Centre for
Medium-range Weather Forecasts (ECMWF) were used. The ECMWF data is available in
six hour time steps with T799/L91 resolution, which corresponds to a horizontal
resolution of ≈0.2∘ × 0.2∘ and 91 levels
in the vertical between the surface and 80 km. For the well-mixed trace
gases CO2 and CH4, data from the Whole Atmosphere Community
Climate Model, version 4 (WACCM4; ) were employed, mostly to
capture the steady increase of CO2 in time that influences retrieved
temperatures. The specific parametrisation used for the WACCM4 model
run can be
found in the publications of and .
JURASSIC2 band radiative transfer model
The JURASSIC2 band model is optimised for the fast simulation of measurements of
coarse or moderate spectral resolution. It is thus
suitable for the retrieval of large amounts of satellite data
e.g., but also for large-scale retrievals as posed by
cross-section or tomographic retrievals .
In a first step, the line-of-sight of a measurement is raytraced through the
1-D representation of the atmosphere . Here, also
temperature gradients along the line-of-sight are taken into account. The
horizontal temperature structure along the line-of-sight found in ECMWF model
data is expressed for each altitude layer as difference to the temperature found
horizontally at the closest tangent point location. This structure is then used
to derive the actual temperature at a given position within an altitude layer in
relation to the assumed or derived temperature at the tangent point location.
The atmosphere is sampled along the line-of-sight in 5 km steps taking
into account atmospheric refraction , forming a series of gas
cells that are assumed to be homogeneous for simulation purposes.
In a second step, the emissivity and source function are computed for each gas
cell and used to simulate the measured radiance. The band model uses tabulated
optical path values for the typical ranges of atmospheric pressure, temperature,
and number density values for the employed ISWs (which may be as small as
a single spectral sample of GLORIA, but usually consist of the arithmetic mean of
several neighbouring samples). The tables are generated by the reference forward
model (RFM v4.3; ) using the current HITRAN2012
spectral database including all updates up to June 2014; the
accuracy of the JURASSIC2 model is here always taken in reference to the RFM.
The tabulated optical path values are generated by convolving monochromatic
emissivities with the instrument line shape (ILS) and conversion to optical path
as a last step. This reversal of integration order makes this less exact, but
several orders of magnitude faster than typical line-by-line calculations. For
fast radiative transfer calculations, the Curtis–Godson approximation
(CGA; ) and the emissivity growth approximation
(EGA; ) are employed. For both the CGA and EGA scheme, the
optical path of the total column between the instrument up to and including the
current homogeneous gas cell is computed to derive the local optical path only
by forming the difference to the total optical path up to and including the
previous homogeneous gas cell (which was determined in the previous step). A regression-based scheme may also be used to
mitigate any bias introduced by the approximation, but is not needed for the
retrieval presented in this paper. Here, simply the arithmetic mean of the
values computed by the CGA and EGA methods is used e.g.,
as a later processing step corrects for any approximation errors introduced.
An important implementation detail is that the optical path was tabulated
instead of the emissivity or transmissivity. As the optical path is much more
linear with respect to number density than the transmissivity (due to the highly
non-linear exponential function) this reduces the table size significantly for
the same accuracy, and thereby reduces memory consumption and increases
processing speed.
One ray is computed for each row of the detector. In a second step, the computed
radiances are convolved with a field-of-view function of the instrument determined
from laboratory measurements compensating for optical and electronic effects.
Using additional intermediate rays did not change the retrieval results or
diagnostics significantly.
The model code uses a tool based on C++ operator overloading to provide
analytically correct derivatives with respect to all input parameters with
minimal computational overhead .
JURASSIC2 monochromatic radiative transfer model
A new addition to JURASSIC2 is a monochromatic model that serves as a fast
reference model. To be rather fast and accurate without becoming too complicated,
it uses tabulated extinction cross-sections values on a fine spectral grid. This
is considerably faster than actual line-by-line calculations for the spectral
regions and emitters used in our retrievals. It is similar in purpose and design
to the HIRDLS intermediate reference model . The spectral grid
is configurable but currently uses a sampling of 0.002 cm-1, which is
sufficient to resolve individual lines.
The ray tracing and field of view are computed in the same way as for the band
model so that the same homogeneous gas cells are used in the computation.
However, it is feasible to sample the atmosphere on different grids or use
Curtis–Godson means to combine neighbouring samples to larger cells in order to
reduce simulation time. In contrast to the band model,
the monochromatic model directly computes the emissivity for the local
homogeneous gas cells and does not rely on an emissivity growth approximation.
To retain a high accuracy, the extinction cross-sections are not simply linearly
interpolated as in the band model, but cubic splines are used. The spline
coefficients are not pre-computed, but generated on the fly using local
information only to reduce memory consumption and bandwidth. That means that in
a first step for each of the four pressure values surrounding the target
pressure, a six point cubic spline interpolation in temperature is performed.
Afterwards, the final value is derived from the previously computed four
extinction values by means of a four point cubic spline interpolation in
pressure. The boundary condition for each cubic spline is that the second
derivative should be zero. The pressure log-linear grid uses 42 points between
1017 and 0.0103181 hPa, while temperature is regularly
sampled in 5 K steps between 100 and 400 K. It thus
uses the same pressure and temperature grid as the band model. To reduce memory
consumption, only extinction cross-sections for required temperature and
pressure values are read into memory on-demand.
Continua and other smooth functions like the Planck function required in further
computations are sampled on a 0.256 cm-1 wavenumber grid and are
linearly interpolated in between. This greatly increases computation speed with
no noticeable degradation of accuracy, especially with respect to the water
vapour continua (MT_CKD version 2.5.2; ). By computing
simulated radiances for the retrieved atmospheres of one flight with both
JURASSIC2 models and RFM, the error of the band and the monochromatic model can
be estimated (see Table ). To provide only a comparison of the
radiative transport and not the ray tracing, RFM was only used to compute the
spectrally resolved emissivities of all homogeneous gas cells involved. Larger
differences found in previous comparisons
are largely attributable to the different ray tracing schemes and differences in
the interpolation of aerosol/extinction (linear compared to log-linear).
Typically, the error of the band model increases with decreasing tangent point altitude.
The monochromatic model code was also designed to provide analytically correct
derivatives by means of algorithmic differentiation. This allows the model to be used for validation of Jacobian
matrices and retrievals. While it could be tuned to be much faster (coarser
tables, less accurate interpolation, etc.), its primary purpose is to be highly
accurate with respect to the (even slower) RFM used for table
computation. However, as the campaign data set comprised of 62 960 measured
profiles is comparatively small (at least compared to typical satellite
experiments), it is feasible to process it in 1-D using the monochromatic model.
The retrieval result derived from the band model is used as initial guess for
the monochromatic model, thereby reducing the number of required iterations to
≈3 down from ≈10 on average, whereby only the first iteration
changes the result significantly. Using the more accurate model
removes a bias in retrieved trace gases, which is most notable for H2O
(≈-6 %) and HNO3 (≈2 %).
Error analysis
The errors of the retrieved quantities are analysed using a linear approximation
, which can be expressed in the same notation introduced in the
beginning of Sect. :
xf=Axt+(I-A)xa+Gϵ.
The retrieval result xf∈Rn is the sum of the true
atmospheric state xt∈Rn smoothed by the averaging kernel
matrix A∈Rn×n, the a priori influence,
and measurement errors ϵ∈Rm.
Whereby
G=Sa-1+F′(xf)TSϵ-1F′(xf)-1F′(xf)TSϵ-1
and
A=GF′(xf)T,
where F′(xf) is the first derivative (Jacobian matrix) of the forward model F evaluated at the retrieval result xf.
Given a covariance matrix S∈Rm×m describing the
effect of an arbitrary error source on the measurements, the gain matrix G
can be used to linearly estimate a covariance matrix describing the effect of this error
source on the retrieval result as GTSG. Such a covariance
matrix can be readily assembled at least approximately for many systematic error
sources using SDs and a reasonable
vertical correlation length using an auto-regressive approach .
We distinguish between random errors stemming from measurement noise and
other, usually systematic, error sources. The measurement noise is taken from
theoretical estimates given by . The characterisation
of actual noise figures is still in progress; however, initial results indicate
that the theoretically predicted values are sufficiently accurate for an error estimation.
For ISWs covering several samples, the assumed noise is divided by the square root of the
number of spectral samples. In addition, a fixed relative noise component
of 0.1 % is assumed for all ISWs.
The error estimate stemming from the noise error source is given as
precision value. The errors stemming from misrepresented background
gases, uncertainties in spectral line characterisation (taken to be 5 % under
the assumption that, statistically, some errors in individual line parameters
cancel each other out), uncertainties in instrument attitude, and calibration
errors are summed up under the label accuracy. It is assumed that gain
and offset errors are spatially uncorrelated, but spectrally fully correlated
(in the absence of a better characterisation, this provides a worse error
estimate than assuming no spectral correlation).
The sum over each row of the averaging kernel matrix A is supplied as
measurement contribution. The full width at half max of each row is also
computed using linear interpolation to provide a measure of the vertical resolution. The smoothing error is not given, as the underlying covariance
matrix Sa describing the prior atmospheric state is far from being
accurate in an optimal estimation sense. Still, the vertical resolution and
measurement contribution can be used to gain insight into the quality of the
data. Additionally, the horizontal resolution along the line-of-sight is
supplied, which can be derived by generating a special averaging kernel matrix
mapping a 2-D state of the atmosphere along the line-of-sight onto the
1-D retrieval result by multiplying the gain matrix G with
a 2-D Jacobian matrix of the forward model with respect to a 2-D
representation of the retrieved volume e.g..
As the logarithm of H2O VMRs is retrieved, the error analysis also
supplies variances with respect to the logarithm. This is somewhat problematic,
as the log-normal distribution in VMR space is biased, so that the mean
value depends on the assumed SD. To remove this dependency, the
median in VMR space is given instead of the mean; using qlog
as the retrieved logarithmic VMR and slog as an associated SD, the conversion from log- to VMR-space is performed with these formulae:
qVMR=exp(qlog)sVMR=exp(qlog)exp(slog2)-1.
The TACTS and ESMVal campaigns
The TACTS and ESMVal campaigns using the new German HALO aircraft took place in
August and September 2012. GLORIA was deployed during all scientific flights, and
it was operational during all but one short flight. The TACTS campaign focused on the
UTLS of the extratropics and the transition to the tropics, with the main scientific
objective to quantify the change of composition of the UTLS between summer
and autumn. Most flights took place over Europe with several additional flights
to Cape Verde including stops on the island. ESMVal
focused on delivering meridional transects covering as many latitudes as possible to
generate a comprehensive data set with the purpose of validation and enhancement
of chemistry–climate models.
HALO flight paths of all campaign flights are shown in
Fig. . Combining both campaigns, a broad
geographic region was covered: from the Spitsbergen islands at 80∘ N down
to close to Antarctica at 65∘ S, and from Cape Verde at 23.6∘ W to
the Maldives at 73.5∘ E. GLORIA took 62 960 spectrally resolved images
during the campaigns, consisting of 386.8 million spectra covering a horizontal
path of ≈66 000 km.
Of these, only a small subset of
several thousand profiles has currently been processed.
The tangent
points of profiles preliminary retrieved using the dynamics mode processor are
shown in Fig. . In addition, first 3-D
tomographic retrievals have been presented by .
The current state of level 0 and level 1 processing allows three flights to be
processed with good confidence in the results, the flight towards Antarctica on
13 September 2012, the flight towards Spitsbergen on 23 September 2012, and
a flight around the North Sea and the British Isles on 26 September 2012. Only this
last flight is shown as an example, but data for all three flights are
currently available on the , where also further flights will be
published as soon as they are available. Only profiles of these flights that
measure at a yaw angle of 89 ∘and move the sled in forward direction
have been processed.
Overview over the flights performed during TACTS and ESMVAL.
The flight paths are marked as black lines. Atmosphere measurements of GLORIA are
overlayed in dark and light green for chemistry and dynamics mode, respectively.
The tangent points of preliminarily processed profiles are shown in blue.
In situ instrumentation
The HALO aircraft carried many different scientific instruments during the
campaigns. Several of these measure the same quantities as GLORIA. Four of these
are used for the validation of retrieved primary targets.
The airborne Fast In-situ Stratospheric Hygrometer (FISH) measures water vapour
between 1 and 1000 ppmv. The measurement principle is based on
Lyman-α photo-fragment fluorescence, which enables the possibility to
measure low concentrations accurately. The instrument is regularly calibrated
against a reference frost point hygrometer (MBW DP30) and has an accuracy of
±7%+0.3 ppmv . The FISH hygrometer is
well established and was deployed on various aircraft campaigns as well as on both
laboratory intercomparison campaigns AquaVit in 2007 and AquaVit II
in 2013, and also on the aircraft intercomparison MACPEX in 2011 .
The HALO Atmospheric chemical Ionization Mass Spectrometer (AIMS) measures
HNO3 and other trace gases like HCl, ClONO2, and
SO2 in the UTLS region . In the flow reactor,
these trace gases react selectively with SF5- ions via fluoride transfer
, and the resultant product ions are detected with a quadrupole
mass spectrometer. The instrument is calibrated in flight using defined
concentrations of nitric acid supplied by a nitric acid permeation oven, in
total yielding an instrumental uncertainty of 25 % for HNO3 at
a temporal resolution of 1 Hz. Successful measurements have been performed during all
TACTS/ESMVal flights. On 23 September 2012, AIMS was operated in the water vapour mode
.
A light-weight (14.5 kg) instrument (named Fairo) for measuring ozone
(O3) with high accuracy (2 %) and high measurement speed (10 Hz)
was developed for the use aboard HALO. It combines a dual-beam UV photometer
with an UV-LED as light source and a dry chemiluminescence detector
. The performance of Fairo was excellent during all 13 flights
of TACTS/ESMVal.
The Basic Halo Measurement And sensor System (BAHAMAS) consists of a powerful
data acquisition system which monitors different interfaces of the aircraft
avionic systems as well as a suite of instruments belonging to the system itself
. These additional sensors allow for a precise
determination of basic meteorological parameters like pressure, temperature,
humidity and the 3-D wind vector. The temperature measurement on HALO is
based on the total air temperature (TAT) method using a separate inlet
(Goodrich Aerospace, formerly Rosemount, BW102) in combination with an open wire
PT100 element. Two of these sensors are mounted on the aircraft nose in order to
provide redundancy in the data. The TAT method and a respective error analysis
are described by . Since the calibration accuracy for the sensor
element is better than 0.1 K between -70 and +50 ∘C the
overall error in the aircraft temperature measurement is 0.5 K.
A Rosemount 858 flow angle sensor is used to measure the 3-D airflow as
well as the static and dynamic pressure at aircraft level. The probe and the
respective pressure sensors are mounted on a noseboom in order to reduce the
influence of the aircraft fuselage on the measurement. However, since the
pressure at the tip of the noseboom is still subject to an aircraft-induced
perturbation, the exact measurement of static pressure on HALO requires an
extensive in flight calibration. The flight test is described by
and demonstrates a 0.3 hPa accuracy in the static
pressure measurement (including a 0.1 hPa calibration accuracy for the
pressure sensor).
Flight on 26 September 2012
The last flight of the campaigns took place on 26 September 2012 starting from
Oberpfaffenhofen, Germany, and ending at the same site. The flight path is shown
in Fig. . A large hexagonal flight pattern over
Norway will allow a tomographic evaluation of measurements in future work. Except for the
beginning and end, the aircraft was nearly always within the lowermost stratosphere,
allowing for the measurement of the tongue of UTLS air stretching in
south-west/north-east direction in the trough between two crests of breaking
Rossby waves. The potential vorticity contours within this air mass follow mostly
this direction which usually indicates that trace gas filaments are similarly
oriented. In this fashion, the direction of the lines of sight is
roughly aligned with filamentary structures except for the second,
northward-bound leg of the flight.
An example of a spectrum is shown in Fig. . The ISWs used can
mostly be fitted within expected ranges with the exception of the vicinity of
the strong line of the CO2 Q-branch at 792 cm-1, where the discrepancy between measurement and simulation often surpasses the threshold value for noise. The cause of
this is likely an instrument artefact under investigation that introduces spatially and spectrally correlated noise in the vicinity of strong spectral features. The discrepancy in
the wavenumber range between 1100 and 1360 cm-1 is
caused by N2O and to a lesser extent by CH4 that are both not
retrieved. The wavenumber range around 830 cm-1 is influenced
significantly by the optical properties of the spectrometer window. These
optical properties vary quickly compared to the frequency of calibration
measurements and, consequently, the affected wavenumber range had to be excluded
from the retrieval ISWs.
The most important error sources for the primary retrieval targets are depicted
in Fig. . To mitigate the impact of filamentary
structures, an averaged error profile is shown. Obviously, the remaining
uncertainty of elevation angle knowledge is the largest contributor to
temperature and H2O accuracy at lower altitudes. Gain and
offset are the most important remaining contributors to accuracy followed by relevant
spectroscopic terms and CO2. The error introduced by uncertainty of background
CO2 VMRs is part of the motivation for the use of WACCM4 data, which capture
the general increase and also seasonal variations better than
the Remedios climatology .
The synoptic situation during the flight of 26 September 2012. The
flight path is marked as black line, the flight direction is clock-wise.
Atmospheric measurements of GLORIA are overlayed in green and yellow for
chemistry and dynamics mode, respectively. The tangent points of processed
profiles are shown in green, where the tangent point vertically closest to
12 km is highlighted in yellow. Isolines of potential vorticity are
shown in red. Wind speeds are shown as contour surfaces in blue shades. The
meteorological data is taken from the ECMWF model state of
26 September 2012, 12:00 UTC.
A measured and a simulated dynamics mode spectrum averaged over
row 57 (tangent altitude 12.78 km) of the detector taken at
14:40:12 UTC and 14.45 km aircraft altitude. The spectrum is split
at 1090 cm-1 with the lower panels showing higher wavenumbers.
The lines shows the full spectral resolution of the spectra while the
markers show the values of the ISWs used in the retrieval. The extent of the
ISWs is overlayed as grey bars. The difference plots also contain the target
(dots) and threshold (dashes) values for noise. The vertical red lines separate regions that employ different aerosol/extinction profiles. This simulation was
performed using the band model. The large discrepancy in the lower panels is
caused by N2O and CH4, which are currently not retrieval
targets.
Total error and major error sources for the four primary targets
temperature (a), H2O (b),
O3 (c), and HNO3 (d)
averaged over the profiles of the flight of 26 September 2012. A “spec”
prefix notes the error induced by spectral uncertainty of line intensities.
Diagnostic quantities for the four primary targets averaged over the
profiles of the flight of 26 September 2012: measurement
contribution (a), vertical resolution (b),
horizontal resolution (c), and horizontal displacement (d).
Horizontal resolution and
displacement have been computed using the band model; displacement is
measured relative to the tangent point location. The resolution is defined
as the “full width at half max” of the corresponding row of the averaging
kernel matrix.
Cross-sections of retrieved quantities for the flight of 26 September 2012:
temperature (a), H2O (b),
O3 (c), and HNO3 (d).
In addition, selected isentropes are shown as dotted grey lines, ECMWF
potential vorticity (interpolated to the time of measurement) isolines of 2
and 4 PVU are shown as thick grey lines. The thermal tropopause is
derived from retrieved temperature profiles and is shown as thick grey dots.
Depicted retrieved values are limited below by clouds and above by the
flight altitude.
The averaging kernels have been diagnosed to provide measurement contribution
and vertical resolution (Fig. ). Due to the nature of
the regularisation employed, the measurement contribution is very close to 1
over the full altitude range, implying that the retrieval results are not biased
in absolute value by the regularisation. The vertical resolution is consistently
better than 500 m and as low as 250 m close to the aircraft for the trace
gases and on the order of 1 km for temperature. The vertical resolution
of temperature will likely increase, if the CO2 Q-branch can be measured
to higher precision in the future. The vertical resolution seems to improve
again for the lowest altitudes. This is technically correct, but misleading as
the shape of the averaging kernels takes on a rather broad base and also partly
negative values at lower altitudes. The last two panels of
Fig. show the horizontal resolution and
displacement. The horizontal resolution along the line of sight of retrieved
trace gases is on the order of 100 km. The small horizontal
displacement for the trace gases asserts that indeed the trace gas VMRs close to the
tangent point (the reference point for the displacement) are being retrieved.
But the 2-D averaging kernels of temperature are shown to be biased towards the
instrument location, presumably because the ISWs used to determine the
temperature are not optically thin. This discrepancy is another error source
introduced by the assumption of horizontal homogeneity. However, we expect that
the effect is mitigated by the application of ECMWF temperature gradients.
The retrieval results for the primary targets are collected in
Fig. . Shown is a highly variable structure consisting of many
small scale filaments. Anomalies in O3 and HNO3 are mostly well
correlated with each other and anti-correlated with H2O anomalies.
This is expected due to the typical chemical composition of stratospheric air (dry,
O3 and HNO3 rich air) and tropospheric air (wet and deprived of
O3 and HNO3) and makes the observed filamentary structure
plausible. From the given figures, one can directly identify air masses, which
were recently mixed from the troposphere into the stratosphere like the filament
of comparatively wet air at 12 km around 10:00 UTC.
Validation
The best opportunity for validation is offered by data acquired from other
instruments carried aboard HALO. Satellite data are not as useful here, as
the given altitude region is usually only coarsely resolved (if at all) and
profiles are spatially and temporally difficult to align. In the given synoptic
situation with strong horizontal temperature gradients, also comparing to
radiosonde data is difficult due to the sparsity of radiosonde ascents. This
leaves the in situ instruments as the best source for validation. Due to the crowded
airspace over Oberpfaffenhofen, it was not possible to directly measure with
GLORIA the ascent or descent profiles acquired by the in situ instruments.
There are measurements of the dive over Norway available, but this situation was
selected specifically for its large horizontal variability, implying a large
sampling uncertainty due to the averaging nature of 1-D retrievals. The dive
will prove valuable for the characterisation of 3-D tomographic
retrievals, though, as it is at the centre of a tomographic hexagonal flight
pattern.
Comparing the retrieved temperatures (in fact 125 m below flight level
to mitigate
the effect of the top column) against the in situ measurements at flight level
is illustrated in Fig. . The temperature in
Fig. a follows closely the measurements, which for
this flight agrees also well with ECMWF. Temperatures seem to follow the lower
bound of the in situ envelope, which might indicate a low bias (the mean
difference is
-0.48 K, see Table ); the most likely
explanation on the GLORIA side for such a bias would be an imperfection in the
calibration of the instrument gain. The correlation of all retrieved
values at flight levels for the three processed flights is shown in
Fig. b. The agreement is within expectation for all flights.
Comparison of retrieved temperature 125 m below flight level
and temperature measured by the HALO BAHAMAS system. The error bars of
retrieved values use the total error (accuracy plus precision).
(a) shows the values over time for the flight of 26 September 2012; in
addition the a priori information employed with assumed uncertainty and
flight altitude is given. (b) shows the correlation for the
three currently processed flights; the Pearson correlation coefficient for
the flights is given in the legend. See also Table .
Comparison of retrieved H2O 125 m below flight level
and H2O measured by FISH. See also
Fig. and Table .
Comparison of retrieved O3 125 m below flight level
and O3 measured by Fairo. See also
Fig. and Table .
Comparison of retrieved HNO3 125 m below flight level
and HNO3 measured by AIMS. See also
Fig. and Table . On
23 September 2012, AIMS has been operated in the water vapour mode;
therefore no HNO3 data are available for that flight.
Water vapour agrees within error bars to the FISH measurements as shown in
Fig. a. There seems to be a high bias on the order
of 1 ppm (roughly 20 %) after 09:00, which is according to simulations
employing fixed ECMWF temperatures related to the low bias in temperature in the
same time frame. Another known systematic error source is the use of a standard
Voigt line-shape for simulation. and suggest
that improved results can be achieved using a speed-dependent Voigt profile.
The mean difference for these flights is
in the same order but of opposite sign, indicating no consistent systematic
problem.
The
correlation for all processed flights in Fig. b
shows good agreement. The correlation for the Antarctic flight is lower than for the other flights as the air was very dry and no VMRs above 6 ppm were measured see also.
O3 values vary to a much larger degree along the flight path than
temperature or H2O. Figure a shows that the
retrieval results follow the in situ measurements within given error bars with
only few exceptions. These are most likely caused by differences in the measured
air masses. Figure b shows that the correlation is
worse than for temperature and H2O, which may be due to the higher
variability of O3 on small spatial scales.
Comparison of retrieved targets 125 m below flight level
and quantities measured by in situ instruments (BAHAMAS, Fairo, FISH, and
AIMS). Shown are the mean difference and the SD. The last
column shows the Pearson correlation coefficient for the linear correlation
between retrieved and in situ measured values.
target
flight
count
bias
stddev
corr
temperature (K)
13 Sep 2012
256
-0.82
0.64
1.00
23 Sep 2012
238
-0.68
0.51
0.98
26 Sep 2012
234
-0.48
0.78
0.91
H2O (ppmv)
13 Sep 2012
230
-0.15
0.93
0.59
23 Sep 2012
222
-0.30
2.27
0.92
26 Sep 2012
195
0.41
1.08
0.81
O3 (ppbv)
13 Sep 2012
181
-0.02
0.14
0.66
23 Sep 2012
158
-0.02
0.09
0.56
26 Sep 2012
140
0.06
0.11
0.69
HNO3 (ppbv)
13 Sep 2012
193
-0.08
0.93
0.58
23 Sep 2012
– not available –
26 Sep 2012
159
0.05
0.40
0.71
Further, a comparison for HNO3 is given in
Fig. a. Similarly to O3, the HNO3
observations cover a large dynamic range. The retrieval results follow the
observed dynamic structures closely, even though there are intervals of
systematic deviations to the in situ measurements on the order of ±40 %.
These deviations may be caused by horizontal gradients of trace gas VMRs along
the line of sight or – to a lesser extent – also vertical gradients. The
correlation for the Antarctic flight in Fig. b is
lower due to a small number of consecutive samples where AIMS
measured approximately twice as much HNO3 as GLORIA. In the same
profiles, the O3 VMR detected by the in situ instrument Fairo
is higher
than that derived from GLORIA measurements, hence a very likely explanation is
that simply different air masses were measured in the Antarctic polar
stratosphere.
The correlation between in situ measurements and GLORIA retrieval results is
astonishingly low compared to the visual agreement. It is obvious that the
GLORIA limb sounder does not measure the radiance emitted at the location of the
aircraft, but rather the radiance emitted by an elongated volume around the tangent point. It
was found for previous aircraft campaigns that the limb-sounder measurements
often lead or lagged behind the in situ measurements as filaments were slanted toward the flight path and were therefore measured earlier or later by
the limb sounder than by the in situ instrument e.g.. It is
plausible that most anomalies in measured trace gases form elongated filaments that
are not fully orthogonal to the flight path.
To estimate the effect of a lag on the correlation, the auto-correlation of
in situ data at GLORIA temporal resolution was determined and a time lag of only
300 s reduces the correlation from 1 to about 0.75 (whereby temperature
was less affected and H2O more). This corresponds to a distance of about
50 km for typical speeds of HALO, which corresponds roughly to twice the
horizontal distance between aircraft and the centre of maximum retrieval
sensitivity at flight level (see above). The sampling of different air masses
may thereby reduce the correlation by up to ≈0.25. Tomographic
retrievals are not subject to such an effect and should deliver more consistent
results.