AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-4135-2017Global height-resolved methane retrievals from the Infrared Atmospheric
Sounding Interferometer (IASI) on MetOpSiddansRichardrichard.siddans@stfc.ac.ukKnappettDianeKerridgeBrianWaterfallAlisonHurleyJaneLatterBarryhttps://orcid.org/0000-0001-5101-9316BoeschHartmutParkerRoberthttps://orcid.org/0000-0002-0801-0831STFC Rutherford Appleton Laboratory, Chilton, UKNational Centre for Earth Observation, Leicester, UKEarth Observation Science, University of Leicester, Leicester, UKRichard Siddans (richard.siddans@stfc.ac.uk)6November20171011413541646September201625August201723August20179November2016This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/4135/2017/amt-10-4135-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/4135/2017/amt-10-4135-2017.pdf
This paper describes the global height-resolved methane
(CH4) retrieval scheme for the Infrared Atmospheric
Sounding Interferometer (IASI) on MetOp, developed at the Rutherford
Appleton Laboratory (RAL). The scheme precisely fits measured
spectra in the 7.9 micron region to allow information to be
retrieved on two independent layers centred in the upper and lower
troposphere. It also uses nitrous oxide (N2O) spectral
features in the same spectral interval to directly retrieve
effective cloud parameters to mitigate errors in retrieved methane
due to residual cloud and other geophysical variables. The scheme
has been applied to analyse IASI measurements between 2007 and
2015. Results are compared to model fields from the MACC greenhouse
gas inversion and independent measurements from satellite (GOSAT),
airborne (HIPPO) and ground (TCCON) sensors. The estimated error on
methane mixing ratio in the lower- and upper-tropospheric layers
ranges from 20 to 100 and from 30 to 40 ppbv, respectively, and error
on the derived column-average ranges from
20 to 40 ppbv. Vertical sensitivity extends through the lower
troposphere, though it decreases near to the surface. Systematic
differences with the other datasets are typically <10ppbv regionally and <5ppbv globally. In the
Southern Hemisphere, a bias of around 20 ppbv is found with
respect to MACC, which is not explained by vertical sensitivity or
found in comparison of IASI to TCCON. Comparisons to HIPPO and MACC
support the assertion that two layers can be independently retrieved
and provide confirmation that the estimated random errors on the
column- and layer-averaged amounts are realistic. The data have been
made publically available via the Centre for Environmental Data Analysis (CEDA) data archive (Siddans, 2016).
Introduction
Methane (CH4) is one of the most important long-lived
greenhouse gases (GHG) in the atmosphere. Its concentration in the
troposphere has increased by a factor of around 2.5 since
pre-industrial times, mainly as a result of human activity (IPCC,
2013). Currently, natural and anthropogenic sources have similar
global annual magnitudes. The largest contributions are from
fossil fuel extraction/use, ruminant livestock, decomposition of
waste, rice cultivation (all anthropogenic), wetland emissions,
geological sources, termites (natural) and biomass burning (both
anthropogenic and natural). Current emission estimates are
subject to considerable uncertainties (IPCC, 2013; Kirschke,
2013), and prediction into the future is even more
uncertain. Large amounts of methane are stored in Arctic
permafrost and clathrates on the ocean floor; methane release
from these stores, as a result of warming, would lead to a strong
positive feedback on climate.
The atmospheric abundance of methane is determined by surface
emissions balanced by chemical sinks, predominantly via reaction
with hydroxyl radical. Between 1999 and 2006 there was little
growth in the annually averaged global methane concentration, but
since then a significant increase of around
5 ppbvyear-1 has been observed at surface level
(Rigby, 2008; Sussman, 2012). The reasons for this recent
behaviour are not yet clear (Nisbet, 2014).
Global observations from nadir-viewing satellite sensors can now
provide information to complement and extend that available from
surface in situ and remote-sensing measurements to improve our
knowledge of the processes controlling the atmospheric
distribution of methane and to monitor its variability on
a decadal scale. Methane can be measured remotely using bands in
the shortwave infrared (SWIR) and thermal infrared
(TIR). Observations in the SWIR, such as those from
SCIAMACHY on Envisat (Buchwitz, 2005; Frankenberg, 2010) and
GOSAT TANSO Fourier transform spectrometer (FTS) (Butz, 2010; Parker, 2011 and Yoshida, 2013),
provide information on column-averaged methane with a vertical
sensitivity, in cloud-free conditions over most land surfaces,
which is close to uniform throughout the atmospheric column. Such
measurements rely on surface-reflected sunlight, so observations
are limited to daytime and predominantly over land (ocean
reflectance being too low except in sun-glint geometry). TIR
measurements of methane are available from spectrometers
including Aura-TES (Worden, 2012), Aqua-AIRS (Xiong, 2008) and
MetOp IASI (Razavi, 2009; Crevoisier, 2009, 2013; Xiong,
2013). These complement the SWIR observations with regard to both
vertical sensitivity and geographical–temporal coverage. Because
TIR spectral signatures depend on thermal contrast between the
atmosphere and surface, sensitivity tends to be strong in the mid
to upper troposphere and relatively low near the surface. TIR
measurements are made over both land and sea and during day and
night. The spatial sampling of IASI is much greater than that of
GOSAT (the only currently operating SWIR methane sensor following
the loss of Envisat in 2012): IASI provides 1.3 million soundings
per day (with 12 km diameter footprint at nadir), over
a sufficiently wide swath to provide approximately even sampling
of all longitudes (Clerbaux, 2009). IASI is
currently flying on both MetOp-A (since 2006) and B (since
2012), effectively doubling the spatial sampling. EUMETSAT plans
MetOp-C to take over from MetOp-A around 2018, to be followed by IASI
Next Generation on the MetOp second-generation series from
2022 to 2040, yielding a self-consistent global dataset from 2007
onwards. In contrast, GOSAT typically provides of order 1000
soundings per day (10.5 km diameter footprint) over
a relatively narrow swath about the 14 ground tracks (Kuze, 2016;
Crisp, 2012). The potential for TIR and SWIR observations to be
used together to infer near-surface methane concentrations is
studied in Worden et al. (2015). Once in orbit, the Sentinel-5
Precursor (S5P), and later Sentinel 5, will offer SWIR methane
measurements with much improved spatial sampling compared to
earlier SWIR sensors.
This paper describes the global height-resolved methane retrieval
scheme developed at the Rutherford Appleton Laboratory (RAL). Key
aspects of the scheme include
retrieval of two independent pieces of information on the methane profile in
the upper and lower troposphere by precisely fitting measured spectra in
the 1232–1288 cm-1 interval;
use of nitrous oxide (N2O) spectral features in the same interval,
coupled to modelling of the N2O distributions (as described in Sect. 2.4), to estimate effective cloud parameters, which mitigate errors in
retrieved methane due to residual cloud and other geophysical variables
affecting radiative transfer. While existing schemes (Razavi, 2009; Worden,
2012) also exploit the fact that N2O retrievals from the same spectral
range as CH4 are affected similarly by residual cloud contamination,
temperature and other errors, their approaches involve jointly retrieving
N2O with other fit parameters (including methane and cloud). In the RAL
scheme the N2O profile at every location is fixed. Information from
IASI measurements at spectral locations of N2O lines is thereby used
for co-retrieval of cloud (and other) parameters and not N2O.
A neural-network scheme developed by Crevioissier (2009) has been
applied to measurements in 10 IASI channels between 1301 and
1305 cm-1 (together with co-located microwave
observations for temperature and humidity sounding). Those
channels were chosen to minimise sensitivity to N2O.
Methane is retrieved at tropical latitudes in a single layer, with
sensitivity to the mid-troposphere and above (peak sensitivity
∼ 230 hPa). IASI methane data produced by that scheme
have been adopted for the EC's MACC project (Massert, 2014)
and for ESA's CCI project (Buchwitz, 2015). By more extensively
exploiting IASI measurements in the 7.8–8 micron range and
modelling radiative transfer online, the RAL scheme reported here
provides information extending into the lower troposphere and with
near-global coverage.
Data processing schemeOptimal estimation
The scheme is based on the optimal estimation method (Rodgers, 2000), which solves an otherwise under-constrained
inverse problem by introducing prior information. This method
finds the optimal state vector x (which
contains the
parameters we wish to retrieve) by minimising a cost function:
χ2=(y-F(x))TSy-1(y-F(x))+(a-x)TSa-1(a-x),
where y is a vector containing each IASI spectral
brightness temperature (BT) measurement used by the retrieval;
Sy is a covariance matrix describing the
errors on the measurements; F(x) is
the forward model (FM), which predicts measurements
given x; and Sa is the a priori
covariance matrix, which describes the assumed errors in the
a priori estimate of the state, a. As the FM is
non-linear with respect to perturbations in methane and other
elements of the state vector, the solution state needs to be found
iteratively. In this case we adopt the well-known
Levenberg–Marquardt method (summarised in Press, 1995), assuming
convergence to have occurred when the change in cost-function
value is smaller than 1.
Measurements
IASI (Blumstein, 2004) provides spectra at 0.5 cm-1
apodized resolution, sampled every 0.25 cm-1, from 625
to 2760 cm-1. Spectra are measured with four detectors,
each with a circular field of view on the ground (at nadir) of
approximately 12 km diameter, arranged in a 2×2 grid within a 50×50km2 field of regard
(FOR). IASI scans to provide 30 FORs (120 individual spectra)
evenly distributed across a 2200 km wide swath. Our
retrieval scheme uses measurements between 1232.25 and
1288.00 cm-1, chosen following the work of Razavi
(2009) to (a) minimise errors caused by neglecting line mixing in
the forward model and (b) include channels with relatively clear
transmission to the ground, to help constrain co-retrieved cloud
parameters and surface temperature. Channels between
1245–1246.75 and 1267–1270 cm-1 are
omitted to avoid problematic spectral features (in the former
range attributed to line-mixing effects).
The noise on individual IASI spectra is particularly low in this
spectral range (Hilton, 2012), with noise-equivalent brightness
temperature (NEBT) around 0.07 K (for a reference scene
temperature of 280 K), corresponding to a noise-equivalent
spectral radiance (NESR) of
5.8 nW/(cm2 sr cm-1). Early retrievals from this range revealed
a significant contribution from scene photon noise, so we adopt
the following in-house model of the estimated error in each
channel:
Δynoise=h+oI‾,
where parameters o and h are constants derived empirically from
an analysis of the random component of fit residuals in this range
(from an early version of the retrieval scheme) and I‾
is the mean spectral radiance over the complete IASI band. This
yields NESR values ranging from 3 to
10 nW/(cm2 sr cm-1)
over the typical range of band-averaged measured radiances.
Forward model
RTTOV version 10 (Matricardi, 2009) is the basis of the RAL
FM. RTTOV estimates radiances convolved with the IASI spectral
response function by use of spectrally averaged layer
transmittances, based on a fixed set of coefficients which weight
atmospheric-state-dependent predictors. The RTTOV v10 model is
sufficiently fast to enable global processing of the IASI mission
with modest computational resources. In order to allow
interference from the water vapour isotopologue HDO to be modelled
adequately, this is handled as an independent variable to the
major isotopologue H216O. We have derived
coefficients specifically for this spectral range, by running the
line-by-line Reference Forward Model (RFM) (Dudhia, 2016) using
HITRAN 2008 (Rothman, 2009) spectroscopic line data for the same
set of atmospheric profiles and predictors used in Matricardi
(2009), with the exception that predictors of the type used for CO
are used instead to predict HDO transmittances. The accuracy of
the RTTOV model with the new HDO coefficients is tested by
comparing radiances from RTTOV to those calculated directly with
the RFM for an independent set of atmospheric profiles. The mean
and standard deviation (SD) of these differences are generally
found to be <0.05K, though SDs can exceed
0.2 K in a few spectral channels particularly where water
vapour transmittances are comparable to those of another variable
gas. This source of FM error is accounted for in the
retrieval by adding the RTTOV–RFM variances to the diagonals of
the IASI measurement error covariance matrix
(Sy)
Spectral correlations in the
RTTOV error are neglected. In practise this is adequate, as
error correlations are found to be weak for the few channels in
which this error is large in comparison to the noise.
.
The ECMWF ERA-Interim (Dee, 2011) reanalysis is used to define the
atmospheric temperature profile and the surface pressure
appropriate for each IASI scene. Reanalysis fields are provided at
6-hourly intervals, approximately 0.7∘ horizontal
resolution, on 60 vertical levels. These fields are linearly
interpolated to the IASI location and time. Profiles of methane
and water vapour (including the isotopologue HDO) are defined by
the retrieval state vector (see below), as is the surface (skin)
temperature.
Over sea, the RTTOV sea surface emissivity model is used. Over
land, the retrieval scheme currently uses the University of
Wisconsin surface emissivity database (Seeman, 2008). Principal
components of an ensemble of measured land surface spectral
emissivity spectra are used in conjunction with 0.1∘
latitude–longitude gridded monthly data from MODIS to generate
global maps of surface spectral emissivity.
It is found that fits to measured spectra based on this
FM
We refer here to fits by the FM implemented in
a manner identical to that described in this paper, with the
exception that points 1–3 above were not applied,
i.e. ERA-Interim temperature profiles and University of Wisconsin
emissivities are used and methane and other state vector
elements are retrieved as described in Sect. 2.5.
lead to
residuals (i.e. differences between observed and modelled spectra)
which are small (<0.5K RMS) but still significant
compared to the IASI NEBT (see Sect. 2.2). Column-averaged mixing
ratios derived from retrieved methane profiles are positively
biased compared to independent measurements by approximately
4 %, with a systematic height-dependent structure in the
profile
The bias on column-averaged mixing ratio was
estimated from comparisons of a version of the retrieval, which
did not apply points 1–3, to TCCON and GOSAT. These
were carried out in an identical fashion to that described in
Sects. 5.3 and 5.4 below. Errors in profile shape were inferred
on the assumption that the tropospheric mixing ratio should (on
average) be independent of height in Southern Hemisphere
mid-latitudes.
. A comparable bias has been found in TES
retrievals, which exploit the same spectral range (Worden, 2012;
Alvaredo, 2015), although with a different height dependence and
in MIPAS stratospheric retrievals in limb geometry as well (Von
Clarmann, 2009). These similarities suggest a possible common
underlying cause, which could likely be related to spectroscopic
line parameters. To perform a preliminary assessment, retrievals
have been carried out with the RFM or LBLRTM line-by-line
radiative transfer model in place of RTTOV and adopting either
HITRAN 2008 or 2012 line data. LBLRTM includes an approximate
treatment of line mixing (Alvaredo, 2013). Since none of these
tests led to a significant reduction in the positive bias with
respect to independent (from the Total Carbon Column Observing Network, TCCON) data, an in-depth investigation of
spectroscopic line parameters can be inferred to be desirable to
reconcile satellite methane retrievals in the relevant TIR and
SWIR bands. For expediency, an empirical approach has been adopted
to account for the identified bias, on the assumption that it is
inherent to RTTOV as implemented in our IASI retrieval scheme:
When input to RTTOV, all methane mixing ratios are multiplied by a factor of 1.04
compared to the values in the state vector (i.e. the FM assumes 4 % more
methane than is ultimately reported as the retrieved value).
Spectral mean residual patterns derived from applying the
retrieval scheme to 1 day of IASI data over cloud-free ocean in
the latitude range of 75 to 15∘ S are adopted to represent
the mean forward model error and its scan-angle dependence. In this
version of the scheme, rather than retrieve a height-resolved
profile, the methane profile shape is specified, using a similar
approach to that adopted to define N2O, and a single scale
factor is retrieved. The resulting mean residual spectrum from the
nadir view, r0, has negligible correlation
with the spectral weighting function for the altitude-independent
scaling of the methane profile. The scan-angle-dependent component
of the FM error is estimated as r1=rE – r0, where
rE is the mean residual from both outer edges
of the swath.
To retrieve global methane, a version of the FM which accounts for the
spectral mean residual by adjusting the measurement vector asy=yRTTOV+f0r0+f1r1,where yRTTOV is the radiance vector predicted by
RTTOV and factors f0 and f1 are co-retrieved with
methane and other parameters in the state vector, on
a scene-by-scene basis.
It is noted that spatial variations in retrieved values of f0
suggest that the mean residual features are at least partly related
to the water vapour distribution.
Modelling N2O
A distinguishing feature of the RAL scheme is the approach
developed to specify the N2O vertical profile at each
IASI measurement location for use in the FM. N2O is
modelled so that its spectral features can be exploited to
co-retrieve two effective cloud parameters and thereby
mitigate related errors on the retrieved methane. In the
troposphere, N2O exhibits variations with latitude
and season smaller than ∼ 0.5 % (IPCC, 2013);
approximately an order of magnitude smaller than those of
methane. The annual growth rate of N2O since IASI
became operational in 2007 (around 0.23 %year-1)
has been much more consistent than that of methane in recent
decades. Mixing ratios of both N2O and methane
decrease with height in the stratosphere. In the case of
N2O, the decrease commences at lower altitude and is
steeper than for methane
In the case of N2O,
the decrease with height is due to UV photolysis whereas for
methane (CH4) it is due to reaction with
O(1D).
. It is therefore particularly important to
accurately model the stratospheric N2O profile
pertaining to individual IASI observations. The lifetime of
N2O in the stratosphere is such that spatial and
day-to-day variability is controlled by dynamics and can
therefore be modelled accurately by exploiting the strong
correlation, on potential temperature surfaces, between
N2O and potential vorticity. This is implemented in
the RAL scheme using the seasonal N2O climatology
derived from the ACE-FTS solar occultation sensor (Jones, 2012),
which is expressed as a function of equivalent latitude (Lary,
1995) and pressure. This is filled below the tropopause with
a fixed mixing ratio of 322 ppbv, a representative
value for the global mean at the beginning of 2009. The zonal
mean field is linearly interpolated to the day of year of
a given IASI observation. The N2O vertical profile at
the locations of individual IASI observations is estimated
using the local equivalent latitude, derived from potential
vorticity and potential temperature given by the ERA-Interim
reanalysis. The long-term, monotonic growth rate in
N2O is modelled by scaling each derived profile by
the factor: f=1+0.0023d, where d is the number of
elapsed days since the beginning of 2009.
Selection of IASI observations for processing
The BT difference between the IASI
observation in a window channel (950 cm-1) and that
simulated on the basis of ERA-Interim, assuming clear-sky
conditions, is used to screen out scenes which are strongly
affected by cloud. If this difference (observation – simulation)
is outside the range of -5 to 15 K, the scene is not
processed. Furthermore, only scenes having a BT larger than
240 K in the same channel are currently processed, since
(a) the retrieval information content is significantly degraded
over very cold surfaces (see below) and (b) convergence is often
found to be slow in these conditions, leading to
a disproportionate use of computational effort.
The IASI MetOp-A orbits from 29 May 2007 to 17 November 2015 have
all been processed with this approach, selecting from the four
pixels in each FOR the IASI pixel with the warmest BT
at 950 cm-1.
State vector and a priori constraint
The state vector for the retrieval scheme consists of 34 elements,
as follows:
Methane mixing ratio (in ppmv, relative to dry air) defined on
12 fixed pressure levels corresponding to z* values of 0, 6, 12,
16, 20, 24, 28, 32, 36, 40, 50 and 60 km, where z* is
a simple transformation of pressure, p (in hPa), to approximate
geometric altitude (km):z*=16(3-log10p).The prior state is defined to be a fixed value of
1.75 ppmv in the troposphere, broadly representative of
Southern Hemisphere mid-latitudes in 2009, and an annual average
height–latitude cross section in the stratosphere. The zonal mean
is calculated by averaging into 5∘ latitude bins 2 years
(September 2008–2010) of output from the TOMCAT chemical transport
model run into which ACE-FTS observations of long-lived
stratospheric tracers have been assimilated (Chipperfield,
2002). A priori error statistics are estimated as the
root-mean-square combination of the SD of the model methane field
about its zonal mean and 10 % of the a priori value
itself. The model SDs in the troposphere are around 5 %, so
a priori errors in the troposphere are set to 175 ppbv
(i.e. 10 % of the prior value), increasing (in
fractional terms) above the tropopause up to peak values of around
50 %. The prior state and errors are interpolated in
latitude to the location of a given IASI observation. To help
regularise the retrieval, off-diagonal elements are defined,
assuming a Gaussian function in the vertical with full-width
half-maximum 6 km (in z∗ units). Here we make
a deliberate choice to define a simple and relatively weak
constraint to emphasise the vertically resolved information in the
IASI measurements. A tighter constraint, which could be justified
based on climatological variability, is not necessary to regularise
the retrieval and would increase the bias towards the prior
(already evident towards high latitudes from the evaluation
reported below).
Natural logarithm of the water vapour (H2O) mixing
ratio (in ppmv) defined on 16 fixed pressure levels corresponding
to z* values of 0, 1, 2, 3, 4, 5, 6, 8, 10, 12, 16, 20, 30, 40,
50 and 60 km. The a priori profiles for water vapour are taken
from ECMWF analysis, interpolated (linearly) onto these pressure
levels. The a priori error covariance is intended to represent
(conservatively) errors in the analysis and those associated with
interpolation to the time and location of an individual IASI
observation:Sa:H2O=Sbg+Sdt+Sdx,where Sbg is the ECMWF model background
error covariance matrix taken from (Collard, 2007);
Sdt is the covariance of differences
(considering all profiles in a given day) between ECMWF profiles at
one of the 6-hourly analysis times and those at the next analysis
time; and Sdx is the analogous
covariance of differences between neighbouring spatial grid
points. This results in a covariance matrix with relatively large
diagonal elements (corresponding to SDs of up to 60 % peaking
in the mid-troposphere) but sufficient vertical correlation to
regularise the retrieval.
Scale factor for HDO, with a priori value of 1 and assumed prior error of 1. This defines
the HDO profile assumed in the FM as follows:rHDO(p)=fHDOfstdrH2O(p),where fHDO is the retrieved factor, fstd
is the (fixed) ratio of HDO : H216O assumed by HITRAN
(3.107×10-4) and rH2O(p) is the (retrieved)
water vapour main isotope mixing ratio profile.
Natural logarithm of the (effective) cloud fraction and the
associated cloud pressure (hPa). Cloud is modelled in RTTOV as
a black body at the given atmospheric level, occupying a given
geometric fraction of the scene. The a priori cloud fraction is
assumed to be 0.01 (before converting to log) and the a priori
error is assumed to be 10 (which, given the log representation, is
a fractional error of 1000 % with respect to the prior value
of 0.01). The a priori cloud pressure is 500 hPa, with
error 500 hPa. The logarithm is used as it is found to
lead to more stable convergence (non-physical states with negative
cloud fraction can otherwise arise during the cost-function
minimisation).
Surface temperature, with a priori value taken from ECMWF
analysis and assumed a priori error of 5 K.
The two scale factors for systematic residuals. A priori
values are 1 for f0 and 0 for f1. Both are assigned an
a priori error of 1. Results from this retrieval scheme are
compared below to an earlier version of the scheme, which is
identical except that (i) the two cloud parameters are
not retrieved (scenes passing the initial cloud test are
assumed cloud free) and (ii) the N2O profile is jointly
retrieved, rather than being modelled. The N2O prior
state and errors are defined in the same way as methane, except
that the profiles are scaled, by the same factor at all
altitudes, to give a peak mixing ratio in the troposphere of
0.319 ppmv.
Error analysis and retrieval characterisation
The error covariance of the solution from an optimal estimation
retrieval is given by
Sx=(Sa-1+KTSy-1K)-1,
where K is the weighting function matrix which contains
the derivatives of the FM with respect to each element of the
(solution) state vector. The square roots of the diagonal elements
of this matrix are referred to as the estimated SD (ESD)
of each element of the state vector.
The transformation from the retrieved methane mixing ratio profile,
x, to the dry-air column-averaged mole fraction, c, is
expressed as a matrix operation c=Mx, where M
contains the weights required to perform the linear operations to
(i) interpolate the profile defined on the state vector grid to the
finer grid used in the radiative transfer model, (ii) integrate to
give the total column amount and (iii) normalise by the total column of
air. The ESD of the column average is then given by
Δc=MSxMT.
The sensitivity of the retrieval to perturbations in the
measurement is given by the gain matrix,
G=SxKTSy-1.
The sensitivity of the retrieval to perturbations in the true
state vector is characterised by the averaging kernel,
A=GK.
Sx can be divided into two terms:
Sx=Sn+Ss,
where Sn=GSyGT describes the uncertainty due
to measurement errors (characterised by Sy)
and
Ss=(I-A)Sa(I-A)T
describes the smoothing error, i.e. departure from the true state
caused by the tendency of the retrieval towards the imposed
a priori constraint. As discussed in von Clarrman (2013), the
smoothing error only applies to the profile as represented on the
(rather coarse) retrieval grid. A consequence of this is that the
ESD of the column average from Eq. (8) does not fully capture
errors arising from the insensitivity of the retrieval to fine-scale perturbations in the vertical profile. Since methane is
well mixed in the troposphere, this issue will often be of little
consequence, although there will be a tendency to underestimate
errors on column average (as well as the column average itself) in
the vicinity of strong sources where relatively high methane
concentrations are present close to the ground. Vertical smoothing
errors will be generally overestimated as the prior
uncertainty on methane exceeds its natural variability. However,
the ESD computed in this way corresponds well to the RMS
difference between individual IASI measurements and independent
TCCON data (see below).
Issues relating to the sensitivity of the retrieval to vertical structure
and smoothing errors can be addressed using the fine-scale averaging kernel:
Af=GKf.
The weighting function Kf is distinguished
from K in that derivatives may be computed with respect
to perturbations on a finer grid than that used for the state
vector. The averaging kernel for the layer average is
Acf=MAf.
The trace of the averaging kernel A, evaluated using
the weighting functions with respect to the retrieved state
(A=GK), gives the degrees of freedom for
signal (DOFS), which indicates the number of independent pieces of
information which can be recovered from the retrieval.
Averaging kernels for the methane profile at each retrieval level
(solid) and for the column average (dashed). Panel (a) shows the result for
NEBT = 0.5 K, and panel (b) for NEBT = 0.1 K. DOFS (the trace of the profile
averaging kernel matrix) for each case is indicated above the panel. The
averaging kernel for the column-averaged mixing ratio is also shown by the
dashed line. This kernel is normalised so that it shows the derivative of
the column-averaged mixing ratio with respect to perturbation in methane in
fine layers, as a function of altitude (for an ideal retrieval this function
should be 1, independent of altitude). Values of the column-averaged kernel
shown here are divided by 10 to fit on the same scale as those for retrieval
levels.
Estimated errors for the methane profile (in panel) and column average
(indicated in caption).
Figure 1 shows averaging kernels for
methane, typical of mid-latitude, for nadir-observing
conditions. The surface temperature is assumed equal to the
temperature of the lowest atmospheric layer. The panel on the left
shows results assuming an NEBT at 280 K of 0.5 K,
while that on the right shows results for 0.1 K
(commensurate with the actual IASI noise). Fitting close to this
level is shown to be very important, adding 0.9 DOFS and
increasing sensitivity to lower-tropospheric methane.
The gain matrix can also be used to estimate the impact of errors
not characterised in the measurement or prior covariance,
e.g. given the covariance of errors in the temperature profile
ST, the resulting covariance of errors in
the retrieved state is
Sx:T=GKTST(GKT)T,
where KT contains the derivatives of the FM
with respect to temperature (defined on the same arbitrarily fine
vertical grid as ST).
Figure 2 shows estimated error contributions to the retrieved
methane profile and derived column averages (for the same
assumptions as the right-hand panel in Fig. 1). The plot shows ESD,
noise and smoothing errors. It also shows three estimates of
temperature error, applying Eq. (11) to three different
temperature error covariance matrices, each expressed on the model
levels of the ECMWF analysis used: (i) “background”,
the ECMWF forecast background error covariance matrix from Collard
(2007); (ii) “sampling”, an estimate of the error
associated with interpolating ECMWF temperature profiles to the
times and locations of IASI soundings, determined in the same way
as for water vapour (terms Sdt+Sdx in Eq. 5); (iii)
IASI, an error covariance matrix estimated for results
from the (version 6) EUMETSAT operational IASI temperature
retrieval. The plot shows the ESD for column-averaged mixing ratio
to be 28 ppbv and the error from modelling temperature to
be of comparable magnitude. The temperature-related errors are
relatively large compared to the natural variability of
methane. The extent to which such errors are reduced by spatial
and/or temporal averaging is determined by their spatial–temporal
correlations, which are difficult to simply characterise. This
paper focuses on 2.5∘ (or coarser) latitude–longitude
gridded, monthly mean IASI retrievals in expectation that the
temperature-related errors on finer scales have a large random
component which averages out. This is partly justified by (i) the
level of agreement which is anyway found in comparisons to
independent data (reported below); (ii) Simmons (2014), who reports
errors in monthly mean ERA-Interim air temperatures to be
considerably smaller than the background errors assumed here; and (iii)
sampling errors being, by definition, negligible on scales coarser
than the model grid. Temperature profiles retrieved from the same
IASI soundings as the methane retrieval would, in several respects,
be preferable to temperature fields from analyses or reanalyses,
particularly when considering results at fine spatial–temporal
scales; sampling errors no longer apply and, although errors would
still exhibit spatial–temporal correlations (e.g. via common
forward model error), their character would differ from those of
the analyses/reanalyses
A pre-retrieval of temperature
and humidity profiles and surface spectral emissivity is in
development.
.
Average in 7.5∘×5∘ latitude–longitude bins of the estimated
SD (ESD) on column- and layer-averaged mixing ratio as
a function of season (left to right) and for day (a,
c and e) and night-time
observing conditions (b,
d and f). JJA is June, July and August 2009;
SON is September, October and November 2009; DJF is December, January and
February 2009/10; MAM is March, April and May 2010. Panels (a and b) show
column-averaged ESD; panels (c–f) show the 0–6 and 6–12 km
layer-averaged ESDs, respectively.
Averaging kernels for methane column average (a), 0–6 km layer
average (b) and 6–12 km layer average (c), as
a function of latitude and season, for observations over sea, daytime land and
night-time land. Kernels are normalised as in Fig. 1.
Panel (a) shows the mean differences between IASI and TCCON
measurements (in 2009) as a function of the AVHRR cloud optical depth and
height for the retrieval in which N2O is fitted and cloud
neglected. The line plot above shows the mean difference over all cloud
heights, as a function of optical depth. Panel (b) shows the same
but for the new scheme with N2O defined and cloud parameters
retrieved (note the change in ranges shown). Panel (c) shows the
number of IASI retrievals in each bin, with the sub-panel above showing the
cumulative total number of points as a function of increasing optical depth.
Panels (d) and (e) are the same as panels (b) and
(c), respectively, after removing scenes with retrieved cloud
fraction larger than 0.2.
The sensitivity of the retrieval is dependent on meteorological
conditions (particularly the surface–atmospheric temperature
profile and thermal contrast between atmosphere and surface). This
is reflected in the geographical and seasonal variation in the
column-averaged and layer-averaged ESDs summarised, using retrieval
results in 2009 and 2010, in Fig. 3. The ESDs in the column average
and 0–6 km layer average
Layers are defined in
terms of z* pressure-altitude levels; 0 km is the
surface. Hence, 0–6 km means surface to 422 hPa;
6–12 km means 422–178 hPa.
show similar
distributions. They vary between day and night and also vary with
season over land due to varying surface–air temperature
contrast. Most precise retrievals occur over daytime land, except
over particularly cold (frozen/snow/ice covered) surfaces in
winter, where ESDs are larger than over sea at similar
latitude. The ESDs in the 6–12 km layer average are large
over Greenland and the Tibetan Plateau. Elsewhere, they are
relatively low and uniform in comparison to ESDs in the
0–6 km layer. Figure 4 illustrates the variation of the
averaging kernel for the column averaging mixing ratio. Averages
from IASI retrievals performed in January and July 2009 are shown
in 10∘ latitude bands. In the figure, the two months are
presented as winter and summer conditions, with
data for summer conditions taken from the January zonal mean in the
Southern Hemisphere and the July mean in the north (vice versa for
the winter). Diurnal variations in surface temperature and
air–ground thermal contrast cause the sensitivity of methane
retrievals over land to differ between daytime (descending node,
09:30 local solar time) and night-time (ascending node,
21:30) observations. Methane retrievals over land generally
show greater near-surface sensitivity in daytime than
night-time. Due to thermal inertia of the ocean, methane retrieval
performance is more uniform with respect to latitude, season and
time of day than it is over land. In summer, daytime sensitivity is
greater over land than sea; however, the situation is reversed in
the winter at mid-latitudes (when the sea is typically warmer than
the land). The kernels for 0–6 and 6–12 km layer averages
show clearly that two pieces of information, broadly corresponding
to those layers, are available in the tropics and mid-latitudes
under most conditions, though with variable sensitivity near to the
surface. Towards the poles, height-resolved information is much
reduced; the kernels for the two layers tend to converge (both
peaking above 5 km).
The methane retrieval scheme is influenced by the specified
N2O distribution. Simulations for cloud-free conditions
show that over a realistic range of N2O perturbations,
the methane retrieval responds linearly, such that a 1 % height-independent scaling of the N2O profile gives rise to
a similar magnitude error in retrieved column-averaged methane. This
arises because the co-retrieved cloud parameters accommodate the
spectral signature of the N2O error, and the retrieved
methane profile then accommodates the impact of the erroneous cloud
parameters on the methane spectral features. Although the retrieval
scheme specifies a fixed tropospheric value of N2O, the
NOAA flask record indicates there is a meridional gradient from
the most southerly to northerly sites of order ±0.5 %
(IPCC, 2013). This would be expected to give rise to similar
systematic errors in methane (±10ppbv). Given the
simple response of the retrieval to perturbing the assumed
N2O, it is possible to correct retrieved column-averaged
methane post hoc, given a better estimation of the N2O
height-resolved distribution than that assumed in the retrieval
(e.g. from a model), although such corrections have not been
carried out in the analysis reported here.
Performance in the presence of cloud
The performance of the retrieval scheme in the presence of cloud
depends on the ability of the two cloud parameters included in the
fit to accommodate the real effects of cloud vertical and
horizontal structure. The potential of the approach was initially
demonstrated via retrieval simulations in which cloud optical
properties and 3-D structure on finer scales than the IASI
field of view was explicitly represented in synthesising measured
spectra (Kerridge, 2012). In this paper, the impact of cloud on
methane retrievals is confirmed using real IASI flight
data. Differences between column-averaged methane retrieved from
IASI and those from the TCCON (see Sect. 5.4) are examined as a function of cloud height
and optical depth
In principle, we should account here
for the IASI vertical sensitivity by using averaging kernels, as
is done in the more detailed TCCON comparisons reported in
Sect. 5 below. However, this would not significantly affect the
findings reported in this section. As illustrated in Fig. 13, the impact of accounting for vertical sensitivity (on
average 10 ppbv) is small compared to the magnitude of
the cloud-related errors considered in this section.
. For this
purpose, effective cloud optical depth and height for each IASI
measurement were estimated by applying the Optimal Retrieval of
Aerosol and Cloud (ORAC) scheme (Poulsen, 2012) to co-located
visible–near-IR and thermal-IR images observed by AVHRR/3 on board
MetOp. The ORAC scheme was applied to AVHRR/3 radiances averaged
over each IASI field of view (given in the IASI Level 1 (L1)
files) to give an effective
Effective in the sense that
cloud is assumed to be horizontally homogenous across the IASI
field of view and is represented by a geometrically thin single
layer of either liquid or ice particles.
optical depth and
cloud height, together with phase and effective radius. Thick,
high cloud is excluded from this analysis by the pre-selection of
IASI measurements based on the BT difference test described
earlier. All IASI retrievals within 100 km and 1 h of all
available TCCON measurements in 2009 were selected. The difference
between all these IASI retrievals and the associated TCCON
measurements were averaged into bins of the AVHRR/3-derived cloud
optical depth and height (without attempting to correct for
differences in vertical sensitivity). Results are summarised in
Fig. 5 for both our original scheme (N2O retrieved,
cloud neglected) and the current scheme (N2O specified,
cloud parameters fitted). Cloud leads to a strong positive bias in
the methane retrieved by the original scheme. Figure 5 shows that,
even though independent, co-located AVHRR/3-derived cloud
information is potentially available
Cloud retrieval from
the ORAC scheme is available only during daytime.
, the
sensitivity of the original scheme to residual cloud is such that
a very stringent filter would have to be applied to avoid
significant error in IASI-retrieved column-averaged methane. In
practice, this would result in the vast majority of scenes having
to be excluded. However, the scheme which co-retrieves
two cloud parameters is shown to be much less sensitive to
residual cloud: for cloud effective heights below 5 km,
the methane retrieval has a bias of less than 30 ppbv. For
cloud effective optical depth below 1, it is insensitive to cloud
at all heights. The errors with respect to TCCON tend to be
negative, especially for low altitude cloud of effective optical
depth >3. As evident from the figure, co-retrieved cloud
fraction (from the methane retrieval scheme) can be used in
post-screening to remove methane retrievals which are likely to be
biased, such that the vast majority of screened retrievals have
a bias smaller than 5 ppbv. Results presented in the
evaluation section below are based on using retrievals with an
effective cloud fraction <0.2.
Evaluation of the retrievalsOverview
In this section we present comparisons of the height-resolved IASI
retrievals with independent sources. Ideally, retrievals would be
validated by comparing to accurate, co-located, measured profiles
of methane which sample spatial and temporal variability as
extensively as IASI and with comparable or finer resolution. Given
such information, retrieved column- and layer-averaged methane
mixing ratios could be compared to the corresponding independent
data both directly and also accounting for the influences of the
prior and vertical sensitivity of the IASI retrieval using
averaging kernels as follows:
ctxI=ca+At(xt-at),
where ca is the column average computed from the a priori
profile; At is the averaging kernel matrix
for the layer, computed from the retrieval gain matrix and
spectral weighting function matrix evaluated on the (finely
resolved) vertical grid of the independent data; xt is the independently measured mixing ratio
profile; and at is the a priori profile
interpolated to the vertical grid of the independent data. There
are, however, no directly measured datasets which provide
sufficient spatial and temporal coverage to enable validation of
the IASI retrievals globally over the duration of its mission. We
therefore compare the following four sources of correlative
data which each have complementary attributes:
The MACC-II GHG flux inversion reanalysis (Bergamaschi,
2013) provides global 3-D methane fields which span the period of our IASI
methane retrievals. It employs the TM5-4DVar flux inversion system driven by
meteorology from ECMWF's ERA-Interim reanalysis. Daily average methane
distributions are provided at a horizontal resolution of 6∘ longitude
by 4∘ latitude. Here we focus on the reanalysis dataset
(“v10-S1NOAA_ra”) which spans 2000–2012 and is based on
the assimilation of NOAA surface level flask measurements. Although the
reanalysis does not provide an unambiguous validation dataset (being based
on assimilation of data into a model), it remains valuable to compare to
IASI because (1) the reanalysis has been validated against independent
observations (see Bergamaschi, 2013) and also found to compare very well
with the GOSAT measurements also used here (Parker, 2015); and (2) it provides
complete global profile information such that comparisons with IASI column-
and layer-averaged mixing ratios can properly take into account vertical
sensitivity (using Eq. 12) and be performed consistently over the full
globe and most of the IASI mission duration. The dataset is particularly
valuable for testing that the retrievals realistically represent differences
in the spatial and seasonal behaviour of the upper- and lower-tropospheric
layer averages (an aspect not well covered by measurements due to limited
sampling). MACC GHG data are also used here to enable retrieval sensitivity
to be accounted for in comparisons with other datasets, as described below.
Aircraft in situ measurements of methane profiles from HIAPER
Pole-to-Pole Observations (HIPPO) which provide accurate measured
methane profiles to compare with vertically resolved satellite
retrievals, e.g. see Wecht (2012) and Alvaredo (2015). The main
limitation of this dataset is its spatial and temporal sampling:
HIPPO executed five campaigns, each consisting of numerous
individual flights, sampling over the US and Pacific between
January 2009 and September 2011 (Wofsy, 2012). Methane profiles were
measured in situ as the aircraft executed a series of ascent/descent
manoeuvres. Most profiles sampled only the middle–lower troposphere,
though on occasion the manoeuvres extended into the upper
troposphere. In order to make comparisons to column averages and
layer averages, and to apply averaging kernels, we extend the HIPPO
profiles by filling upwards with temporally and spatially
interpolated profiles from the MACC-II GHG reanalysis.
The TCCON of
ground-based FTSs (Wunch, 2011) provides
the most comprehensive set of ground-based column-averaged methane
measurements against which to validate satellite
retrievals. Although TCCON does not provide height-resolved
information, comparisons to it are particularly valuable for testing
the temporal stability of the IASI retrievals seasonally and over
the full mission.
GOSAT column-averaged methane retrievals produced by the
University of Leicester (Parker, 2015) from the GOSAT SWIR spectrometer, using the so-called “CO2
proxy” technique
In this approach, the ratio of
CH4 to CO2 column-averaged mixing ratios is
retrieved from a spectral interval around 1.6 microns containing
both methane and CO2 bands. The
CH4 : CO2 ratio is multiplied by the
CO2 column average from a model. The approach exploits
the fact that CO2 deviations from uniform mixing are
much smaller than those of methane and allows errors in
instrument characterisation and radiative transfer modelling to
largely cancel out.
. Version 6 data, as produced for the ESA
Greenhouse Gas CCI project (Buchwitz, 2015), are used here. These
comparisons generally support the findings from the MACC column-averaged comparisons. They are also included here so that users who
might consider joint assimilation of the two satellite datasets are
aware of the level of consistency between them.
Retrieved and independent data are co-located and their
differences binned in latitude and longitude for a given month or
season (details of the binning are given in the individual
sections below). In the comparisons reported here, IASI retrievals
which have co-retrieved cloud fraction >0.2 or unusually high
fit cost are excluded from the analysis.
In all cases we compare column averages. Where the independent
data allow (MACC and HIPPO), we also compare separately lower- and
upper-tropospheric layer-averaged methane from surface to
z*=6km and between z*=6 and 12 km. In each
case, comparisons are made both directly and taking into account
retrieval sensitivity using the averaging kernels. Where the
independent data are height resolved, this is done by applying
Eq. (12). In the case of GOSAT and TCCON (which only provide
column information) this cannot be done, so instead we estimate
the (smoothing) error in the IASI column average using the
co-located MACC profile, given by the difference
(cM-cMxI), where cM is the directly computed
MACC layer average and cMxI is the result of Eq. (12)
applied to the MACC profile. The smoothing error
(cM-cMxG) of the GOSAT–TCCON profile can be
similarly estimated, using the corresponding GOSAT–TCCON kernels
and prior profiles. We then estimate what GOSAT–TCCON would be
expected to observe, given the IASI measurement, accounting for
the respective smoothing errors
Because the sensitivity
of GOSAT (and TCCON) varies relatively little with height
compared to IASI, there is very little difference in practice
between results using cMxG in this equation or the
uncorrected MACC-II GHG column average, cM.
:
cI : G=cI+(cM-cMxI)-(cM-cMxG)=cI+cMxG-cMxI.
Assuming that the MACC profiles are sufficiently accurate that the
binned mean difference cMxG-cMxI is
accurate then differences between binned mean values of cI : G
and the corresponding result based on the retrieved GOSAT–TCCON
column (cG) should reveal systematic errors in one or another
retrieval rather than differences which might be expected based
on their different vertical sensitivity.
Comparisons of IASI and MACC-II GHG column-averaged mixing ratio.
Each column of the figure shows results for a different season; JJA is June, July and August 2009;
SON is September, October and November 2009; DJF is December, January and
February 2009/10; MAM is March, April and May 2010. Panels (a–c) show, respectively, results from MACC-II GHG,
IASI daytime retrievals and IASI night-time retrievals. Panels (d–g) show
differences between MACC-II GHG and IASI, separately for day and night, with
and without applying the IASI averaging kernels. MxI is used in the
panel titles as shorthand for “MACC-II GHG after applying IASI averaging
kernels”.
Comparisons of IASI and MACC-II GHG 0–6 km (z*) layer-averaged
mixing ratio. Daytime only results shown. Panels (a–c) show,
respectively, results from MACC-II GHG, IASI and MACC-II GHG with the IASI
averaging kernels applied (MxI). Panels (d, e) show differences
between MACC and IASI directly and after applying the IASI averaging
kernels.
Comparisons of IASI and MACC-II GHG 6–12 km (z*) layer-averaged
mixing ratio. Daytime only results shown. Panels (a–c) show,
respectively, results from MACC-II GHG, IASI and MACC-II GHG with the IASI
averaging kernels applied (MxI). MACC and MxI results have 0.01 ppmv
added, as indicated in the plot title. Panels (d, e) show differences
between MACC and IASI directly and after applying the IASI averaging kernels
(without any offset applied to MACC or MxI).
Comparison to MACC-II GHG flux inversion reanalysis
Comparisons with IASI are made by interpolating MACC-II GHG data
for a given day to the geographical locations of individual IASI
observations and averaging both into 2.5∘×2.5∘ longitude–latitude bins for individual
months. Column-averaged methane and layer averages between
pressure levels corresponding to z*=6 and 12 km are
considered.
Figure 6 shows seasonal mean distributions of the column averages
from IASI and MACC-II GHG based on data processed between
June 2009 and May 2010 and averaged into 7.5∘×5∘ longitude–latitude bins over 3-month
intervals
This grid size is chosen to be appropriate for
the comparisons to GOSAT as discussed in the following
section.
. IASI and MACC-II GHG exhibit broadly similar spatial
distributions (e.g. north–south gradient) and seasonal
variations (in particular features associated with emissions in
southern Asia). IASI daytime observations over land show
generally larger values than MACC-II GHG, particularly over
regions associated with surface emissions. In the direct
comparison (MACC-II GHG - IASI), differences are seen to be
comparatively large at high northern latitudes. These differences
are much reduced in comparisons after the averaging kernels are
applied (labelled “MxI – IASI” in the figure), indicating
prior influence on the retrieval accounts for most of the
discrepancy. IASI tends to be biased low at high northern
latitudes because the a priori tropospheric mixing ratio is
biased low at these latitudes and, due to low surface
temperature, IASI spectra are less sensitive to methane
variations than they are at lower latitudes. Such discrepancies
are less apparent at high southern latitudes where the a priori
tropospheric mixing ratio is in reasonable agreement with MACC-II
GHG. Use of the averaging kernels also reduces differences
between MACC-II GHG and IASI over source regions in
Asia. However, IASI is lower than MACC-II GHG (by up to
40 ppbv) over Arabia/Iran and the Sahara. It is also
consistently lower (by 10–20 ppbv) over some regions of
the tropical oceans, especially in March–May. In contrast,
from September to February it is consistently higher in southern
mid-latitudes by ∼20ppbv. This could be partly
explained by a low bias of MACC-II GHG against TCCON in the
Southern Hemisphere reported in Alexe (2015). In comparisons to
TCCON and GOSAT, IASI is found to be biased high (see Sects. 5.3
and 5.4), though by a considerably smaller amount.
The regions used for comparisons with MACC and the locations of
TCCON sites.
There are regions over the tropical oceans in which IASI is lower
than MACC-II GHG by ∼20ppbv, especially in
March–May. Similar patterns appear also in IASI–GOSAT
comparisons. A low bias in IASI data could plausibly be explained
by persistent low cloud cover (see Sect. 4 above); however, the
geographical pattern of the bias is not fully consistent with
this explanation (a similar bias is not present in other regions
of known persistent low cloud cover). The bias could also be
explained by the presence of slightly higher than expected
tropospheric N2O mixing ratio in these areas, which
might be supported by HIPPO observations (Kort, 2011).
Figures 7 and 8 compare the 0–6 and 6–12 km layer
averages from IASI with MACC-II GHG. The IASI global
distributions are seen to capture well methane source regions
(e.g. South-East Asia, Amazon, central Africa) which feature prominently
in the MACC-II GHG distribution, although the amplitudes of such
regional enhancements retrieved by IASI are lower than MACC-II
GHG, consistent with mixing ratios in source regions being
highest nearest to the surface where IASI sensitivity is
lowest. After applying IASI averaging kernels to MACC-II GHG,
however, agreement is much improved, with differences comparable
to those found for column-averaged mixing ratios. IASI observes
the Australian methane source to be larger than MACC-II GHG,
which could perhaps explain why methane values in Southern
Hemisphere mid-latitudes are generally higher for IASI than for
MACC-II GHG, particularly in summer. Methane distributions in the
6–12 km layer are markedly different to those in the
0–6 km layer for both IASI and MACC-II GHG. The pattern
of land surface sources is reflected only weakly and the
asymmetry between Northern and Southern hemispheres is markedly
reduced. The most prominent feature in Northern Hemisphere summer
and autumn is seen to be outflow from the South-East Asian
monsoon, which is captured quite consistently by IASI and MACC-II
GHG. In direct comparisons, MACC values are generally
10–20 ppbv lower than IASI. When averaging kernels are
applied, which capture the influence on the retrieved
6–12 km layer average from the atmosphere both above and
below, agreement is generally improved. However, a discrepancy is
then revealed at high northern latitude, particularly in spring
when MACC-II GHG values are ∼20ppbv larger than
IASI values. These differences must be related to differences in
representing the stratospheric vertical profile and its
contribution to the 6–12 km layer average in either
IASI or MACC-II GHG
For MACC-II GHG, stratospheric 3-D
structure is that specified by TM5 whereas, for IASI, it is an
annual average zonal-mean cross section from TOMCAT, from which
there will be systematic, seasonally dependent departures.
.
Time series of monthly, regionally averaged column-averaged
methane from IASI and MACC. IASI measurements are shown in black; values
directly from MACC are in red; MACC results after applying the IASI
averaging kernel (AK) are shown in green. The caption in each panel
indicates the correlation between IASI and MACC (r), the mean difference
IASI-MACC (m) in ppbv and the SD in the difference
(s) in ppbv. Two values are given in each case, first for the direct
IASI–MACC comparison and then after applying the kernels. Dashed vertical lines
mark the beginning and end of each year.
As previous figure but for 0–6 km layer-averaged methane.
As previous figure but for 6–12 km layer-averaged methane.
Seasonal mean distributions of column-averaged methane in 2009–2010
from GOSAT (a) and IASI daytime observations, sampled on a daily basis
like GOSAT (b). Panel (c) shows differences between the
GOSAT and the IASI daytime means. Panel (d) shows differences after
correcting IASI for differences in the IASI/GOSAT vertical sensitivity using
MACC.
Comparison of IASI and TCCON monthly column-averaged methane
mixing ratio from 2008 to 2014. The black line shows IASI and red points show
TCCON. The grey line shows the IASI result corrected to account for the IASI
smoothing error using MACC (see text). Histograms indicate the SD in the IASI and TCCON monthly mean (refer to right-hand axis).
Figures in the caption indicate the correlation coefficient (r), mean
difference (m, in ppbv) and SD in the difference (s,
in ppbv). Two values are given for each quantity (separated by a slash),
corresponding to the black (IASI direct) and grey (IASI corrected) curves,
respectively.
Scatter plots comparing TCCON and IASI column-averaged methane
mixing ratio in 2013. Each point compares the mean values for each month,
with different colours indicating different (groups of) TCCON stations as in
the caption. Error bars show the SDs in daily mean values
within each month. Panels show (from a to c): IASI a priori vs. TCCON;
IASI vs. TCCON; IASI after correction for smoothing error using MACC.
Comparison of IASI and HIPPO for flight campaign 5 (between 9 August and 9 September 2011). Panel (a) shows the flight track. Actual
measurement locations are indicated with black dots; associated coloured
triangles indicate the profile index, as shown on the x axis of panels on
the right (b–e) (colours under the axis correspond to colours used in the map).
Panel (b) shows the cross section as measured by HIPPO, after
binning and filling (upwards) using the MACC GHG reanalysis. The solid black
line in this plot shows the latitude of each profile (refer to y axis on the
right); the dashed black line shows the maximum (z*) altitude of the HIPPO
measurement, above which profiles are filled with MACC. Gaps (filled with
white) between the coloured regions divide data from different flights (on
different days). Panels (c–e) compare IASI and HIPPO column- and layer-averaged mixing ratios. The mean of matched IASI retrievals is shown in
black. The dashed lines show the mean ±SD of the
matched retrievals. Grey shows the IASI a priori; red shows the HIPPO
result; green shows HIPPO after applying the IASI averaging kernel.
Summary of the differences between IASI and HIPPO for all five
HIPPO flight campaigns. Results are presented averaged into 10∘ latitude
bins. As indicated in the legend, solid lines show the mean difference
between IASI and HIPPO (with (green) and without (red) IASI averaging
kernels being applied to HIPPO data). Corresponding dashed lines show the
standard deviation of the individual IASI/HIPPO matches about the mean
difference. Black dashed lines show the mean of the IASI estimated SD (ESD) on individual soundings. Figures in the top left of each
panel show the mean difference (m) over all matches and the
standard deviation of the individual matches about the mean with and without
application of IASI averaging kernels to HIPPO data.
The evolution in time of these comparisons has been studied by
averaging the monthly-binned IASI and MACC values from
2.5∘×2.5∘ latitude–longitude intervals
into the regions illustrated in Fig. 9. Over land, these regions
correspond to those used in the TRANSCOM model intercomparison
exercise (from Fraser, 2013, based on Gurney, 2002). Time series of the column averages in these regions are
shown in Fig. 10. Each panel compares IASI with MACC-II GHG
(direct and after applying the averaging kernels). The caption in
each panel gives the correlation coefficient (r), mean
difference (m) and SD of the differences (s) between IASI
and MACC-II GHG. Panels are organised in order of average
latitude from north to south. IASI and MACC-II GHG clearly agree
well on the secular trend in methane over the 8-year period of
2007–2015, and seasonal cycles also agree well. As noted
previously, averaging kernels have most impact at high northern
latitudes, significantly increasing correlation coefficients in
that case. After applying the averaging kernels, correlation
coefficients are all ≥0.85. Mean differences are
≤10ppbv except in southern mid-latitudes, where IASI
values are ∼20ppbv higher than MACC-II GHG. SDs of
the monthly, regional mean values are typically ∼3ppbv (although these are somewhat larger at high
northern latitudes). This is an order of magnitude lower than the
typical ESD of ∼30ppbv for an individual IASI
sounding, but large in comparison to the standard error in the
mean (since many thousands of individual IASI soundings
contribute to each monthly regional mean).
Figures 11 and 12 show the corresponding time series for the 0–6
and 6–12 kmz* layer averages. In the 0–6 km
layer, the impact of vertical sensitivity is more pronounced,
with IASI typically underestimating the true layer average,
particularly at high northern latitudes and also in temperate
Eurasia and other land regions where near-surface methane
concentrations are particularly high. Much of these discrepancies
are explained by the averaging kernels, leaving correlations
between 0.7 (boreal Eurasia, where IASI sees more pronounces
seasonal variations) and 0.94 (temperate North America/mid-latitudinal
Atlantic). In many locations, the seasonal patterns at
6–12 km are quite different from those of the total and
0–6 km layer average. In particular, tropical regions
have relatively weak seasonal cycles in this upper layer and this
is captured by IASI in accordance with MACC-II GHG. In temperate
Eurasia (affected strongly by the monsoon outflow) and
mid-latitude Pacific, the opposite is true (stronger seasonal
cycle in upper layer). In the North Atlantic, there is
a pronounced phase shift in the seasonal cycle exhibited by the
two layers, consistently seen in IASI and MACC (with or without
kernels). These differences in seasonal cycles, together with the
level of consistency between IASI and MACC, support the assertion
that IASI is providing vertically resolved information. As with
the column average, correlation coefficients are ∼0.9 after
applying averaging kernels. The Arctic region shows the largest
differences, with a quite distinct seasonal cycle evident in IASI
compared to MACC (whether or not averaging kernels are
applied). Throughout the tropics and southern latitudes, IASI
tends to be 10–20 ppbv higher than MACC (after applying
averaging kernels) in the upper layer. Biases in the lower layer
are typically much smaller, except over Australia.
Comparisons to GOSAT retrievals
The approach described in Sect. 5.2 for MACC-II GHG is adapted
here to account for the sparse spatial sampling of GOSAT,
especially over the ocean where retrievals only exist in sun-glint
geometry. The starting point is to obtain 2.5∘×2.5∘ longitude–latitude daily binned fields from GOSAT
and IASI (daytime only retrievals); monthly mean fields are then
accumulated from these daily files. In the IASI case, geographical
sampling for each given day is restricted to only those grid cells
for which GOSAT data also exist. The resulting monthly means are
then further averaged to 7.5∘×5∘
longitude–latitude resolution over 3-month intervals.
A 7.5∘ longitude resolution was chosen to be just
sufficient to eliminate gaps that would otherwise appear between
GOSAT orbit tracks in the resulting plots. The number of GOSAT or
IASI retrievals in each of these bins is sufficiently large that
the estimated random error on the mean value (based on the
estimated error on individual retrievals) is well below
1 ppbv. According to Parker (2015), GOSAT retrievals have
a bias (vs. TCCON) of around 4.8 ppbv.
Results are illustrated in Fig. 13. Differences in direct
comparisons between GOSAT and IASI are seen to be broadly similar
to those found between MACC-II GHG and IASI (Fig. 6), which is
consistent with the agreement found between the same GOSAT and
MACC-II GHG datasets in Parker (2015). Adjusting for the
different vertical sensitivities of IASI and GOSAT (using MACC-II
GHG) is seen to account for most of the systematic difference
between the two at high northern latitudes in winter and
spring. The IASI values adjusted for vertical sensitivity are seen
to be lower by 10–20 ppbv over Arabia, North Africa and
over tropical oceans. At southern mid-latitudes IASI is higher (by
∼ 10 ppbv) than GOSAT. This bias is about a factor of 2
smaller than that also seen in this region in the comparison to
MACC-II GHG (from September to February), implying that MACC-II GHG
is negatively biased with respect to both IASI and GOSAT in this
region.
Comparison to TCCON
Here we compare IASI to all available TCCON data from version
“GGG2014”. This includes data from the following sites:
Armstrong Flight Research Center, Edwards (Iraci, 2014a), Ascension
Island (Feist, 2014), Białystok (Deutscher, 2014), Bremen (Notholt,
2014), California Institute of Technology (Wennberg, 2014c), Darwin
(Griffith, 2014a), Eureka (Strong, 2014), Four Corners (Dubey,
2014b), Garmisch (Sussmann, 2014), Indianapolis (Iraci, 2014b),
Izana (Blumenstock, 2014), Jet Propulsion Laboratory (Wennberg,
2014a, e), Karlsruhe (Hase, 2014), Lamont (Wennberg, 2014d), Lauder
(Sherlock, 2014a, b), Manaus (Dubey, 2014a), Orleans (Warneke,
2014), Paris (Te, 2014), Park Falls (Wennberg, 2014b), Réunion
Island (De Maziere, 2014), Rikubetsu (Morino, 2014b), Saga (Shiomi,
2014), Sodankylä (Kivi, 2014), Tsukuba (Morino, 2014a) and
Wollongong (Griffith, 2014b). For some stations, additional
temporal coverage was given in the earlier GGG2012 release, and we
exploit this here, using GGG2012 in periods when this is available
but GGG2014 is not. Data for Ny-Ålesund were taken from the 2012
release. Locations of the TCCON stations used are indicated in
Fig. 9.
IASI column-average data within 200 km of each TCCON
station are averaged over a month for the days on which there are
TCCON observations. Monthly averages are presented if there are at
least 100 IASI individual observations and 10 TCCON
observations contributing to each mean. Typically there are several
thousand IASI observations contributing to each monthly mean, for
which a standard error in the mean of ≤1ppbv would be
expected from typical ESDs on individual retrieved column averages.
Since TCCON does not provide profile information, we cannot
directly account for the effect of the IASI vertical sensitivity
but do so indirectly using MACC-II GHG as a transfer standard, as
described in Sect. 5.1. In order to extend this correction beyond
the end of 2012, we use the MACC-II GHG delayed mode analysis
(“v10_an”), which is available from 2013 until mid-2014. This
assimilates GOSAT retrievals (from the RemoTec proxy scheme of
Schepers, 2012), in addition to the NOAA surface flask
observations.
Time series of comparisons to TCCON stations are shown in
Fig. 14. In the absence of any TCCON measurements in a month, the
IASI time series is completed using the IASI monthly mean
considering all days in the month (these points are shown for
context/continuity in the plots but do not contribute to any
derived statistics). As indicated in the panel headings, results
from some stations (seen to sample broadly similar methane
variations) are grouped together to make the time series in each
panel more complete and to enable results to be presented in a more
compact form.
The caption in each panel gives the correlation coefficient
(r), mean difference (m) and SD of individual TCCON–IASI
differences (s); two values are given for each quantity
(separated by a slash), corresponding to the direct comparison of
TCCON to IASI and the comparison to IASI after adjusting for
vertical sensitivity (via MACC-II GHG profiles). Correlation
coefficients before correction are 0.76–0.91 and generally improve
after the adjustment. Correlation is only 0.65 (0.73 after
correction) at Izana. Some of this degradation may be related to
the high altitude of the station (2300 m) and/or its
specific location (on Tenerife), which is subject to peculiar
localised meteorological conditions and is in an area often
affected by desert dust.
As might be expected, the correction generally raises the IASI
values (as the tropospheric prior value is usually systematically
low). The correction tends to have more impact in the north,
although agreement with TCCON is not always improved. The
correction usually reduces the SD in the difference between IASI
and TCCON. After correction, the mean difference and SD are
≤10ppbv in most cases, with the notable exception of
the Four Corners site. Here IASI captures the seasonal cycle well
but underestimates TCCON by ∼25ppbv, even after
correction. This anomaly is presumably explained by the fact that
the Four Corners TCCON site is located close to an extremely strong
methane source associated with coal mining and related activities
(Kort, 2014), and so it samples localised methane variability
represented neither by IASI data sampled over a circular region of radius
200 km nor by the MACC-II GHG data used in the adjustment.
Each panel in Fig. 14 also shows (as histograms along the x axis,
referring to the right-hand y axis) the SDs of IASI and TCCON
data in monthly bins. Grey bars show the SDs of all individual IASI
retrievals in each monthly bin. These are generally higher than
those for TCCON (shown in light red), reflecting the significant
contribution of random errors to individual IASI retrievals. These
SDs are comparable to the ESD of individual retrievals, as computed
by the optimal estimation retrieval scheme (illustrated in
Fig. 3). The histogram indicated by the thin black line, shows for
each given month the SD of the set of the dailymean IASI values. Each daily mean is the average of all
IASI observations within 200 km of the TCCON site. This
quantity generally agrees rather well with the TCCON SD, reflecting
the day-to-day variation of methane within each month as seen by
the two sensors. Again, the exception here is Four Corners, where
TCCON has a higher SD than IASI, supporting the hypothesis that
this station samples methane variability which is substantial on
a local scale.
Figure 15 summarises the TCCON comparisons for 2013 in a compact
form, showing the individual monthly samples from TCCON and IASI as
individual points, colour-coded by the site (or grouping of
sites). Four Corners is excluded from this plot because of the
sampling issue identified above. Error bars show the SDs of
daily mean values in each monthly bin. The top-left panel plots
TCCON against the retrieval prior, whose variation arises
from meridional structure in the annual mean stratospheric
distribution. The absence of any discernible correlation between
prior and TCCON values clearly demonstrates that agreement
between the retrieval and TCCON to be wholly dependent upon
information provided by the IASI measurements. It also illustrates
why IASI retrievals tend to be biased low at high northern
latitudes, where measurement sensitivity is lowest and the
prior contribution is therefore most influential. The top
centre panel shows the direct IASI–TCCON scatter, which indicates
a correlation coefficient of 0.92 and that IASI is systematically
lower by ∼11ppbv. The top right panel shows the
scatter between TCCON and the adjusted IASI retrieval, in which the
correlation coefficient is increased to 0.95 and the negative IASI
bias is reduced to ∼1.5ppbv. The SD in the monthly
IASI–TCCON differences is ∼10ppbv.
Comparison to HIPPO airborne observations
We co-locate IASI and HIPPO data by selecting, for each HIPPO
profile, all IASI observations within 200 km and 6 h
(taking the mean latitude, longitude and time of the aircraft
measurement). We then compare each HIPPO profile to the mean of
the associated IASI observations. We also compute the SD of
individual IASI soundings. The number of IASI samples in each mean
profile is variable (typically in the range 1–20) due to cloud
occurrence and latitude (more samples are found at high latitude
where IASI's 2200 km swaths overlap). Figure 16 shows the
comparison for HIPPO campaign 5, which is particularly interesting
for our purposes as variations in the upper- and lower-tropospheric
layers (represented here by 0–6 and 6–12 km layer
averages) are quite distinct. These differences are very poorly
captured by the IASI prior but relatively well captured by
the retrieval; illustrating that IASI can effectively resolve two
independent layers centred in the lower and upper
troposphere. There is a particularly pronounced meridional
structure in the 6–12 km layer between profile index
30–40, which is captured by IASI and clearly present in both
HIPPO and the part of the field which has been infilled with
MACC-II GHG data.
Figure 17 summarises (in a similar format analogous to figures of
Alvaredo, 2015) the level of agreement between IASI and HIPPO,
binning into 10∘ latitude intervals, the differences
between the individual IASI and HIPPO matches, considering all five
campaigns. The figure shows the mean difference (HIPPO–IASI) in
each bin (with and without applying IASI averaging kernels),
together with the SD about the mean of the differences between the
individual IASI profiles and the HIPPO profile associated to
them. Each HIPPO profile is only used once in accumulating the
statistics: SDs are computed first for all IASI profiles which
match individual HIPPO profiles, then these are accumulated over
all HIPPO profiles in a given latitude bin. For comparison, the
mean ESD of the IASI observations is also shown. The number of
samples in each bin is typical several hundred, so the standard
error in the mean value is almost always <1ppbv
(whether estimated from the predicted retrieval error or the SD in
the individual profile differences). Differences larger than this
are therefore indicative of statistically significant bias between
IASI and HIPPO. Assuming HIPPO to be correct, biases are most
likely either from IASI retrieval error or the representativity of
the HIPPO sample of the spatial area sampled by the selected IASI
observations. The following points are evident. (1) After applying
averaging kernels, the mean bias is -1.5ppbv (column
average), 3.7 ppbv (0–6 km layer average) and
0.24 ppbv (6–12 km layer average). As previously
noted, averaging kernels have most impact in the Northern
Hemisphere (where the prior methane is biased particularly
low). (2) The SD in the difference is similar whether or not
averaging kernels are applied and is almost always slightly
smaller (21–41 ppbv, depending on layer) than the
reported ESD (27–47 ppbv) (summaries of the SDs are given
for each layer separately in the caption in each panel of
Fig. 17). This is not surprising as the ESD includes significant
smoothing error, which will not manifest entirely as (quasi)
random variability in the agreement between IASI and HIPPO. (3)
There is some latitude dependence in the bias, particularly in the
lower-tropospheric layer (still mainly within ±20ppbv). In particular, IASI seems to underestimate in
the tropics. This behaviour is similar to that found between IASI
and GOSAT column averages over the tropical oceans, as noted in
Sect. 5.3.
Summary and conclusions
This paper reports a retrieval scheme for IASI developed at the
RAL, which, by fitting measured
brightness temperature spectra in the 1232–1288 cm-1
interval to RMS precision of <0.1K, is capable of
extracting information on two independent layers centred in the
upper and lower troposphere. Sensitivity near the surface depends
upon thermal contrast with the surface and therefore the
temperature structure of the lower atmosphere and surface
conditions. A particular feature of this scheme is to model and
fix N2O profiles so that measurement of its absorption
features in the same spectral band as methane provide information
to co-retrieved effective cloud fraction and height, substantially
improving methane retrieval precision by mitigating the effects of
residual cloud. The use of the RTTOV10 forward model makes the
scheme sufficiently fast on the JASMIN computer infrastructure at
RAL to also run in near-real time. The complete IASI
MetOp-A record 2007–2015 has been processed, and this 8-year
global dataset is publically available from the CEDA
(Siddans, 2016).
The dataset has been extensively assessed by comparison to
independent results from the MACC-II reanalysis based on
GHG inversion and to correlative observations from the
GOSAT satellite instrument, the TCCON surface network and
pole-to-pole height–latitude transects from the HIPPO
campaigns. Taken together, these comparisons indicate quasi-random
errors of ∼20–40 ppbv on individual IASI-retrieved
column averages, in line with the ESD provided with
each retrieval. Random errors on the estimated upper-tropospheric
layer average are ∼30–40 ppbv; those on the lower-tropospheric layer average are more variable with observing
conditions (particularly land–atmosphere temperature contrast), in
the range of 20–100 ppbv. The estimated random errors in the
layer averages are supported by the comparisons to HIPPO. After
spatial and temporal averaging, and accounting for the vertical
sensitivity of the IASI column average, systematic differences
with the other datasets are typically <10ppbv
regionally and <5ppbv globally. A systematic bias
compared to MACC of around 20 ppbv is found in the
Southern Hemisphere, which may be associated with underestimated
emissions from Australia in MACC (such a bias is not observed in
comparisons to TCCON).
The IASI retrieval is shown to capture the secular increase from
2007 to 2015 and seasonal variations of methane, yielding regional
time series with correlations of typically 0.8–0.9 with respect
to the MACC-II GHG inversion, in which surface fluxes were
estimated from assimilation of NOAA flask
measurements
After 2013, GOSAT column average data were
assimilated in addition to NOAA flask data.
. Furthermore, lower-
and upper-tropospheric layer averages have been shown to reproduce
much of the seasonal variations predicted by MACC and the extent
to which seasonal cycles differ in the two layers, supporting the
assertion that IASI can recover two independent pieces of
information, in the tropics and mid-latitudes. At high latitudes,
over particularly cold surfaces, the sensitivity near the ground
is reduced, and the height-resolved information becomes
limited. The ability to resolve two layers is also supported by
comparisons with HIPPO, and is fully in line with the formal
estimate of 2 degrees of freedom for the retrieved vertical
profile (under most observing conditions away from the poles).
It is important to take into account the sensitivity of the IASI
retrievals, as characterised by the averaging kernels, and this is
particularly the case at high latitudes and when considering the
lower-tropospheric layer. If not taken into account, IASI column
averages appear negatively biased at high northern latitudes due
to the influence of the prior and systematically low
prior value in the troposphere.
This paper focuses on evaluation of the retrieval on relatively
coarse spatial and temporal scales. IASI has a spatial resolution
of 12 km, as determined by the fields of view of its
individual detectors, and therefore has potential for resolving
structure on that scale, including discrete emission sources. We
note here that the current RAL scheme is limited at fine spatial
scales by errors in representation of surface and atmospheric
temperature (particularly in mountainous areas) and surface
emissivity. These are expected to be mitigated significantly by
using temperature and humidity profiles and surface spectral
emissivity jointly pre-retrieved from IASI/MHS/AMSU
A new
version of the methane retrieval scheme which works on this
basis is at an advanced stage of development.
, which is being
investigated in depth at present.
Though it does not clearly emerge from the comparisons shown here,
we note that users of the data should be cautious when using IASI
methane retrievals in the presence of elevated sulphate aerosol,
which is known to have significant spectral dependence in the
1232–1288 cm-1 range used by this retrieval (Boer,
2013; Clarisse, 2013). Anomalies in methane retrievals have been
noted which may be related to the eruptions of Sarychev in 2009
and Calbuco in 2015.
The scheme makes empirical corrections for discrepancies apparent
between line-by-line modelled spectra and IASI observations. These
discrepancies do not seem to be resolved by updates in HITRAN
2010 or the line-mixing approach used by LBLRTM. Further work is
therefore needed to better understand the spectroscopy of methane
and/or spectral interferents in this range.
IASI also measures methane in the 3.7 micron spectral
region. During the daytime, measurements in this range include
a large surface-reflected solar contribution which contains
information on near-surface methane, which is complementary to that
obtained from the 7.9 micron band. The potential to practically
exploit this additional sensitivity is currently being
investigated in depth.
This paper demonstrates that through IASI on MetOp-A and B, as
currently in orbit, and C, due for launch in 2018/19, to be
followed by IASI-NG on the MetOp-SG series 2020-40, the global,
height-resolved methane distribution can be monitored over several
decades to investigate variability caused by natural and
anthropogenic emissions and composition–climate interactions.
The IASI methane data described in this paper
are publically available from the UK Centre for Environmental
Data Analysis (CEDA) data archive (Siddans, 2016). Access to meteorological reanalysis data from ECMWF and IASI Level-1
data, along with JASMIN computer infrastructure, has also been
provided by the CEDA.
TCCON data were obtained from the TCCON Data Archive, hosted by the
Carbon Dioxide Information Analysis Center (CDIAC)
(http://www.tccon.caltech.edu/).
HIPPO data were obtained from Carbon Dioxide Information Analysis
Center (CDIAC) HIPPO Data Archive (http://hippo.ornl.gov/).
MACC-II GHG inversion data were obtained from the ECMWF
(http://apps.ecmwf.int/datasets/data/macc-ghg-inversions/).
GOSAT data used here were provided by the ESA GHG CCI project
(http://www.esa-ghg-cci.org).
The authors declare that they have no conflict of
interest.
Acknowledgements
This work has been funded by the UK Natural Environment Research
Council (NERC) through the National Centre for Earth Observation
(NCEO) and the strategic programme “Greenhouse Gases UK and Global
Emissions” (GAUGE). Work has also been partly funded by a EUMETSAT
study (contract no. EUM/CO/14/4600001315/RM).
Robert Parker and Hartmut Boesch are funded by NCEO and ESA GHG-CCI; Robert Parker
is also funded by an ESA Living Planet Fellowship. We thank the
Japanese Aerospace Exploration Agency, National Institute for
Environmental Studies and the Ministry of Environment for the GOSAT
data and their continuous support as part of the Joint Research
Agreement. This research used the ALICE High Performance Computing
Facility at the University of Leicester.
Edited by: John Worden Reviewed by: two anonymous
referees
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