AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-2315-2016Consistency and quality assessment of the Metop-A/IASI and Metop-B/IASI
operational trace gas products (O3, CO, N2O, CH4, and CO2) in the subtropical North AtlanticGarcíaOmaira ElenaSepúlvedaEliezerSchneiderMatthiashttps://orcid.org/0000-0001-8452-0035HaseFrankAugustThomashttps://orcid.org/0000-0002-2995-4778BlumenstockThomasKühlSvenMunroRosemaryGómez-PeláezÁngel Jesúshttps://orcid.org/0000-0003-4881-2975HultbergTimRedondasAlbertohttps://orcid.org/0000-0002-4826-6823BarthlottSabinehttps://orcid.org/0000-0003-0258-9421WiegeleAndreasGonzálezYennySanromáEstherhttps://orcid.org/0000-0001-8859-7937Izaña Atmospheric Research Centre (IARC), Agencia Estatal de Meteorología (AEMET), Santa Cruz de Tenerife, SpainInstitute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyEuropean Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Darmstadt, GermanyOmaira Elena García (ogarciar@aemet.es)25May2016952315233310November201521December201510May201612May2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/2315/2016/amt-9-2315-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/2315/2016/amt-9-2315-2016.pdf
This paper presents the tools and methodology for performing a
routine comprehensive monitoring of consistency and quality of IASI (Infrared
Atmospheric Sounding Interferometer) trace gas Level 2 (L2) products
(O3, CO, N2O, CH4, and CO2) generated
at EUMETSAT (European Organisation for the Exploitation of Meteorological
Satellites) using ground-based observations at the Izaña Atmospheric
Observatory (IZO, Tenerife). As a demonstration the period 2010–2014 was
analysed, covering the version 5 of the IASI L2 processor. Firstly, we assess
the consistency between the total column (TC) observations from the IASI
sensors on board the EUMETSAT Metop-A and Metop-B meteorological satellites
(IASI-A and IASI-B respectively) in the subtropical North Atlantic region
during the first 2 years of IASI-B operations (2012–2014). By analysing
different timescales, we probe the daily and annual consistency of the
variability observed by IASI-A and IASI-B and thereby assess the
suitability of IASI-B for continuation of the IASI-A time series. The
continuous intercomparison of both IASI sensors also offers important
diagnostics for identifying inconsistencies between the data records and for
documenting their temporal stability. Once the consistency of IASI sensors is
documented we estimate the overall accuracy of all the IASI trace gas TC
products by comparing to coincident ground-based Fourier transform infrared
spectrometer (FTS) measurements performed at IZO from 2010 to 2014. The IASI
L2 products reproduce the ground-based FTS observations well at the longest
temporal scales, i.e. annual cycles and long-term trends for all the trace
gases considered (Pearson correlation coefficient, R, larger than 0.95 and
0.75 for long-term trends and annual cycles respectively) with the exception
of CO2. For CO2 acceptable agreement is only achieved for
long-term trends (R∼0.70). The differences observed between IASI and FTS
observations can be in part attributed to the different vertical
sensitivities of the two remote sensing instruments and also to the degree of
maturity of the IASI products: O3 and CO are pre-operational,
while N2O, CH4, and CO2 are, for the period covered by
this study, aspirational products only and are not considered mature.
Regarding shorter timescales (single or daily measurements), only the
O3 product seems to show good sensitivity to actual atmospheric
variations (R∼0.80), while the CO product is only moderately
sensitive (R∼0.50). For the remainder of the trace gases, further
improvements would be required to capture the day-to-day real atmospheric
variability.
Introduction
Continuous, consistent, and high-quality long-term monitoring of the
composition of the atmosphere is fundamental for addressing the challenges of
climate research. In this context space-based remote sensing observations are
of particular importance, since they are unique in providing a global
coverage. Among the current space-based remote sensing instruments, IASI
Infrared Atmospheric Sounding Interferometer, has
special relevance since it combines high quality (very good signal-to-noise
ratio and high spectral resolution), good horizontal resolution (12 km at
nadir), global coverage, and long-term data availability. Its mission is
guaranteed until 2022 through the meteorological satellites Metop, the space
component of the EUMETSAT (European Organisation for the Exploitation of
Meteorological Satellites) Polar System (EPS) programme: the first sensor
(IASI-A) was launched in October 2006 on board Metop-A, the second (IASI-B)
was launched in September 2012 on board Metop-B, and the third (IASI-C) is
expected to be launched in October 2018 aboard Metop-C. A successor to IASI,
IASI-NG , with improved spectral resolution and
radiometric performance is under development as part of the EPS-SG (Second
Generation) programme and will continue the mission after Metop-C and extend
the data record by 2 decades. All these features make the IASI missions
very promising for monitoring atmospheric composition in the long term as a
key instrument for the EUMETSAT Earth observation programme
e.g..
However, for correct scientific use of these long-term observational records,
an assessment of the consistency of the atmospheric observations from the
IASI sensors currently in orbit, as well as a documentation of their quality
is required. To date there has been no comprehensive consistency and
validation study for all the trace gas products disseminated by EUMETSAT as
IASI Level 2 (L2) products. Such activities have been mostly performed in the
context of short campaigns or have been focused on specific atmospheric
parameters e.g.. By such
campaigns alone it is not possible to extensively evaluate the quality of the
different IASI atmospheric products as well as the potential of IASI for
long-term climate studies. In order to address these two critical tasks, high-quality ground-based reference data sets are needed.
Technical specifications of the IASI and IZO ground-based FTS
instrument.
IASIGround-based FTSInstrumentFourier transform spectrometerFourier transform spectrometerSpectral range (cm-1)645–2760∼ 740–9000Apodized spectral resolution (cm-1)0.50.005Type of observationThermal emission of Earth–atmosphereSolar absorptionField of view (FOV)50 km (3.33∘) at nadir0.2∘with four simultaneous pixels of 12 km(FOV centred on solar disc)Frequency of observationtwice per daycontinues observations during∼ 10:30 and 21:30 UTC2/3 days per week (weather permits)Duration of observation8 s (30×4 pixels)∼ 6–8 minData availability2007–present1999–present
While there are several techniques for measuring total column (TC) amounts of
atmospheric trace gases such as water vapour or ozone that can be used as a
validation reference (e.g. radiosondes, UV–VIS spectrometers), there is
currently only one technique that routinely estimates all the atmospheric
trace gases retrieved operationally from IASI measurements (ozone,
O3, carbon monoxide, CO, nitrous oxide, N2O, methane,
CH4, and carbon dioxide, CO2): the ground-based Fourier
transform spectrometers (FTSs). FTS instruments record very high-resolution
infrared solar absorption spectra and use a similar measurement approach as
IASI. By evaluating these solar spectra, the FTS systems can provide TC amounts and volume mixing ratio (VMR) profiles of many different
atmospheric trace gases with high precision. Within the NDACC (Network for
Detection of Atmospheric Composition Change, www.acom.ucar.edu/irwg/)
such FTS experiments are operated at about 25 sites distributed worldwide.
Since the 1990s, when these instruments began to be used for atmospheric
composition monitoring, there have been continuous efforts to assure and even
further improve the high quality of the FTS data products: monitoring the
instrument line shape e.g., monitoring and
improving the accuracy of the solar trackers e.g., and
developing and intercomparing sophisticated retrieval algorithms
e.g.. The good quality of these long-term ground-based FTS
data sets has been extensively documented by theoretical and empirical
validation studies
e.g..
In this context this work intends to demonstrate monitoring capabilities for
the IASI L2 atmospheric composition products, to help identifying potential
areas for retrieval algorithm improvement, and to support the related
ongoing and future development activities. For this purpose, it is firstly
analysed the consistency between the IASI-A and IASI-B L2 atmospheric trace
gas products provided by the IASI mission (TC amounts of O3,
CO, N2O, CH4, and CO2) in the subtropical
North Atlantic region after the 2 first years in operation of IASI-B
(2012–2014). Secondly, the documentation of the overall IASI quality is
addressed by using the high-precision FTS observations that have been carried
out at the Izaña Atmospheric Observatory (IZO) since 1999. Due to its
strategic location, IZO is affected by background free troposphere air masses
with very distinct history and regions of origin (Atlantic Ocean, Europe,
North and Central Africa, etc; see , and references
therein), making IZO a unique place for documenting the quality of IASI
atmospheric products for different scenarios. To address all these tasks,
this paper is structured as follows: Sect. 2 presents the EUMETSAT Metop/IASI
mission describing the IASI sensor as well as the IASI L2 atmospheric trace
gas products routinely disseminated by EUMETSAT, while Sect. 3 introduces the
ground-based FTS products, including the FTS activities at IZO and the
retrieval strategies used for obtaining the different FTS products. Section 4
presents the strategy developed for comparing IASI-A, IASI-B, and FTS
observations and Sects. 5 and 6 address the consistency and intercomparison
study at different timescales (single measurements, daily, annual, and
long-term trends). Finally, Sect. 7 summarises the main results and
conclusions of this work.
EUMETSAT/IASI missionIASI sensor
The IASI remote sensing instruments are nadir-viewing atmospheric sounders
based on FTSs and developed by CNES (Centre
National d'Etudes Spatiales, www.cnes.fr) in cooperation with EUMETSAT.
They are on board the EUMETSAT Metop meteorological satellites, which operate
in a polar, Sun-synchronous, low-Earth orbit since 2006 (Metop-A was launched
in October 2006, Metop-B was launched in September 2012, and the Metop-C
launch is scheduled for October 2018). The Metop-A and Metop-B currently
operate in a co-planar orbit, 174∘ out of phase. The IASI sensors
were designed with the main goal of retrieving operational meteorological
soundings (temperature and humidity) with high vertical resolution and
accuracy for weather forecast use, as well as for monitoring atmospheric
composition (O3, CO, N2O, CH4, and CO2)
at a global scale. Additionally, they provide land and sea surface
temperature, surface emissivity, and cloud parameters . To do
so, IASI records thermal infrared emission spectra of the Earth–atmosphere
system in the 645–2760 cm-1 region (apodized spectral resolution of
0.5 cm-1) with a surface swath width of about 2200 km twice per day.
Table provides the main IASI technical specifications and more
information about these instruments can be found in and
(and references therein).
IASI operational trace gas products
Since 2008, when the first operational IASI data were delivered by the
EUMETCast system (www.eumetsat.int), different versions of the IASI L2
Product Processing Facility (PPF) have been used to produce the EUMETSAT IASI
L2 trace gas products: version 4 (V4) between June 2008 and September 2010,
version 5 (V5) between September 2010 and September 2014, and version 6 (V6)
from September 2014 onwards. Here we focus on the IASI V5 products, for which
the longest IASI-A and coincident IASI-A and IASI-B time series are available
(September 2010–September 2014 and December 2012–September 2014
respectively). The main characteristics of the IASI L2 V5 products are
described below and summarised in Table .
Description of the IASI L2 V5 trace gas products: spectral regions
used for the retrievals, type of inversion algorithm (ANNs: artificial neural
networks; OEM: optimal estimation method), status of the different
products (Pre-Op: pre-operational; Aspi: aspirational), and target
uncertainty within the IASI mission.
IASI L2 V5 introduces significant improvements in the retrieval of the
atmospheric trace gas products as well as cloud products and cloud detection
in contrast to the previous version, V4. Now, under cloud-free conditions,
the O3 profiles are simultaneously retrieved, together with the
humidity and temperature profiles and the surface temperature, from the IASI
measured radiances using an optimal estimation method
. This approach uses a global a priori with a single unique
covariance matrix, computed from a collection of ECMWF (European Centre for
Medium-Range Weather Forecast) analysis records, independent on seasonal or
latitudinal variations. Therefore, all observed atmospheric variability comes
from the measurements rather than the a priori information .
The TC amounts of the other molecules (CO, N2O, CH4,
and CO2) are retrieved using an inversion algorithm based on
artificial neural networks (ANNs). The feasibility of retrieving those
quantities with ANNs from IASI measurements was first studied prior to launch
and this formed the basis for the IASI L2 processors until
revision V4. The method was refined in V5 specifically for CO with an
updated channel selection and the addition of new predictors adding important
information about the surface characteristics and the viewing geometry
. The ANNs were trained with simulated radiances using the
RTTOV model and the atmospheric composition
profiles from the MOZART model . N2O, CH4,
and CO2 benefit from the same improvements in the ANNs design
introduced for CO in V5, but they have not been specifically optimised
and validated. They are only distributed as aspirational products while
scientific development is ongoing and research products from the wider
community can be made operational.
The cloud screening strategy is key for the space-based atmospheric trace gas
retrievals. For IASI L2 products, the cloud detection relies on five
different cloud detection tests, based on the combined information of the
measured IASI L1C spectra and of other remote sensors flying with IASI
(AVHRR, AMSU, and ATOVS) as well as of comparison to synthetic radiances. An
IASI pixel is flagged cloudy if at least one of the cloud tests detects the
presence of clouds. For more details, refer to and the
Products User Guide (EUM/OPSEPS/MAN/04/0033, EUMETSAT).
Ground-based FTS programme at the Izaña atmospheric observatory and FTS products
The IZO, run by the Izaña Atmospheric
Research Centre (IARC, http://izana.aemet.es) belonging to the Spanish
Meteorological Agency (AEMET), is a subtropical high-mountain observatory on
Tenerife (Canary Islands, 28.3∘ N, 16.5∘ W, and
2373 m a.s.l.; Fig. ), and only 350 km away from the African
continent. It is usually well above the level of a strong subtropical
temperature inversion layer, which acts as a natural barrier for local
pollution. This fact, together with the quasi-permanent subsidence regime
typical of the subtropical region, makes the air surrounding the observatory
representative of the background free troposphere (particularly at
night-time) and references therein. These
conditions are only significantly modified by episodes of mineral desert dust
exports during summertime, when Saharan dust long-range transports within the
so-called Saharan Air Layer () are quite common
e.g.and references therein. During wintertime, the
Saharan events rarely reach the IZO altitude, since they are confined below
2 km of altitude and references therein.
Site map indicating the location of the Izaña Atmospheric
Observatory (IZO, marked by a black star) in the Canary Islands and the
collocation box used for comparison (dashed lines), i.e. ±1∘
latitude/longitude centred at IZO location. The grid lines divide the area
into boxes of 0.25∘. Coloured filled circles correspond to IASI-A
ozone total column observations on 31 January 2012.
Description of the FTS retrieval setups used in this work. The
profile retrieval is done by using a Tikhonov–Phillips slope constraint
(TP1), while the scale retrieval corresponds to a scale to the a priori VMR
profile. A summary of the error estimation is also provided: theoretical
total systematic (SY) and statistical (ST) errors for the FTS total column
products (median, 1σ×10-1, being σ the standard
deviation of the error distributions between 2010 and 2014). XN2O,
XCH4, and XCO2 correspond to the total column-averaged dry air
mole fractions of N2O, CH4, and CO2 respectively.
O3CON2OCH4CO2Spectral range (cm-1)1001–10142057–21602481–25412611–29432620–2630Inversion methodTP1TP1TP1TP1ScaleTemp. retrievalYesNoYesNoNoSpectral range (cm-1)962–970–2610–2627––Interfering speciesH2O, CO2, C2H4H2O, CO2, O3H2O, CO2, O3H2O, HDO, CO2, O3H2O, CH4N2O, OCSCH4N2O, NO2, HCl, OCSTheoretical SY (%)2.0, 0.12.1, 0.42.1, 0.12.3, 0.13.5, 0.4Theoretical ST (%)0.4, 0.30.5, 0.10.2, 0.30.3, 0.30.6, 1.4Experimental ST (%)0.4–0.7–∼ 0.4 in XN2O∼ 1 in XCH40.4 in XCO2References(, )
Within the IZO's atmospheric research activities, the ground-based FTS
measurements started in 1999 and continue until the present with two Bruker
IFS spectrometers (an IFS 120M from 1999 to 2005 and an IFS 120/5HR from 2005
until present). These activities have contributed to the international
networks NDACC and TCCON (Total Carbon Column Observing Network,
www.tccon.caltech.edu) since 1999 and 2007 respectively. The FTS
experiment at IZO records highly resolved infrared solar absorption spectra
from 740 to 9000 cm-1. However, in order to be consistent with the
spectral range covered by IASI, for this study we only work with those
measured in the middle infrared spectral region, i.e. between
740 and 4250 cm-1 (corresponding to the standard NDACC measurements).
These solar spectra are acquired at an apodized spectral resolution of
0.005 cm-1. Table lists the main FTS characteristics and
highlights the main differences and similarities between IASI and these
ground-based FTS instruments. For further details about the FTS instrument at
IZO refer to and references
therein.
The high-resolution FTS solar absorption spectra allow an observation of the
pressure broadening effect and thus the retrieval of trace gas VMR profiles.
In this work the target gas VMR profiles are retrieved using the algorithm
PROFFIT PROFile FIT; which follows the formalism given by
. Then, TC amounts are computed by integrating the retrieved
VMR profiles from the FTS altitude (2373 m a.s.l. for IZO) to the top of
the atmosphere. For all the target gases considered we use a nearly identical
retrieval strategy, which is summarised in Table . The target gas VMR
profiles are retrieved using specific micro-windows, also taking into account
the absorption signatures of those trace gases interfering with the target
gas (see Table ). For O3 and N2O, the retrieval has
also been refined by including a simultaneous temperature fit, which
significantly reduces the error . For this
purpose, we add additional micro-windows containing well-isolated CO2
lines.
In order to reduce the interference error due to water vapour (the main
interfering gas), we firstly perform a pre-fit of H2O and in a second
step simultaneously perform the target gas profile retrieval and a scale
retrieval of all the interfering species considered. For all the target
gases, the VMR profiles are retrieved on a logarithmic scale using an ad hoc
Tikhonov–Phillips slope constraint (TP1 constraint), with the exception of
CO2, which is scaled to the a priori profile on a linear scale. The a
priori profiles are taken from WACCM (Whole Atmosphere Community Climate
Model-version 6, http://waccm.acd.ucar.edu) provided by NCAR (National
Center for Atmospheric Research; J. Hannigan, private communication, 2014),
averaged between 2008 and 2014 (period of IASI data). As a priori temperature we
use the NCEP (National Centers for Environmental Prediction) 12:00 UTC daily
temperature profiles. Note that only the a priori temperature profiles are
updated daily. For all the target gases the a priori information is always
kept constant; i.e. it does not vary on a daily or seasonal basis.
Therefore, similarly to IASI, all the observed variability directly comes
from the measured FTS spectra. Regarding spectroscopy, the spectroscopic line
parameters are taken from HITRAN 2008 database including
2009 and 2012 updates (www.cfa.harvard.edu/hitran) for all the gases
except for CH4. For CH4 we use a preliminary line list
provided by D. Dubravica and F. Hase, obtained within a current project of
the Deutsche Forschungsgemeinschaft, IUP Bremen, DLR Oberpfaffenhofen, and
KIT, which has demonstrated lower spectroscopic residuals than the HITRAN
2008 linelist .
The FTS spectra are only recorded when the line of sight between the
instrument and the Sun is cloud free. However, to avoid possible
contamination of thin clouds, the FTS observations are, in a second step,
filtered according to co-located global solar radiation observations taken at
IZO in the framework of the Baseline Solar Radiation Network (BSRN,
http://bsrn.awi.de). By using a cloud detection method on the
coincident solar radiation measurements (based on , and adapted
for IZO by ), the cloud-free periods in the FTS records
are easily identified. Once the FTS retrievals are computed, they are
filtered in a third step according to (i) the number of iterations at which
the convergence is reached and (ii) the residues of the simulated–measured
spectrum comparison. This final step ensures that unstable or imprecise FTS
retrievals can be considered (which could likely be introduced by remaining
thin clouds).
It is important to remark that the FTS products used here contain further
refinements over the standard NDACC approaches (NDACC/Infrared Working Group,
IRWG, www.acom.ucar.edu/irwg/) and, thus, they do not correspond to the
FTS products publicly available at the NDACC archive. Refer to the references
given in Table for further details about the specific retrieval
strategies used in this work.
Theoretically, the error of the different FTS products can be estimated by
following the formalism detailed by where three types of
error can be distinguished: the smoothing error associated with the limited
vertical sensitivity of the FTS instruments, the errors due to uncertainties
in the input/model parameters (instrumental characteristics, spectroscopy
data, etc), and the measurement noise. Using the error estimation as
provided by PROFFIT and assuming the error sources and values listed in
Appendix , where the theoretical error estimation of the FTS
products is detailed, the total statistical errors for the FTS TC products
range from 0.2 to 0.6 %, while the systematic error is between 2 and 4 %
(Table ).
At IZO, other different high-quality measurement techniques for monitoring
atmospheric trace gases are available . By using those data,
a continuous empirical documentation of the quality and long-term consistency
of our FTS products has been carried out since the FTS instrument was
installed at IZO in 1999. The FTS precision obtained from these experimental
studies for the trace gases considered here is also listed in Table ,
showing a rather good agreement between theoretical and experimental errors.
Comparison strategyTemporal decomposition
The consistency and quality assessment of IASI-A and IASI-B products is
addressed at different timescales: single measurements, daily, annual, and
long-term trends. This temporal decomposition provides an added value for
validating trace gases with a rather small variability, such as N2O
or CO2. For such gases the uncertainty is often larger than the
day-to-day concentration variations and thus a validation at longer temporal
scales is more meaningful than a validation limited to a comparison of
individual measurements. Moreover, this analysis allows us to quickly detect
instrumental issues or inconsistencies. For this purpose, we follow the
procedure proposed by (and references therein), explained
in detail in the following. Firstly, for analysing the time series on
different timescales the measured TC time series of each target
gas ([TC]gas) is fitted to a time series model, which considers a
mean [TC]gas value and [TC]gas variations on two
different timescales (see Eq. 1): a linear trend and intra-annual
variations.
F(t)=fo+ftrendt+∑i=1paicosωit+bisinωit,
where t is measured in years, fo is a baseline constant, and
ftrend the linear trend in change per year. The annual cycle is
modelled in terms of a Fourier series where ai and bi are the
parameters of the Fourier series to be determined and ωi=2πi/T
with T=365.25 days. Once the model fit is computed, the seasonal variations
are obtained by subtracting the fitted linear trends from the measured time
series. The averaged annual cycle is then computed by averaging these
de-trended time series on a monthly basis. It represents the de-trended
multi-annual seasonal cycle of the target gas. In addition to the seasonal
timescale we look on measurement-to-measurement and long-term timescales.
For the separation into these two timescales we use the aforementioned time
series model. The measurement-to-measurement timescale signal is calculated
as the difference between the measured time series and the modelled time
series (whereby all fitted timescales are considered: mean value, linear
trend, and seasonal cycle). The so-calculated de-trended and de-seasonalised
time series represents the very short-term variations, corresponding to the
variations among individual observations. Finally, in order to calculate the
long-term timescale signal (annual means) we reconstructed a time series
that only considers the fit results obtained for the mean [TC]gas
and the seasonal cycle. Then, by subtracting it from the measured time
series, we get a de-seasonalised time series, for which we then calculate the
annual mean values. Note that for IASI-A and IASI-B consistency study, the
bias between both IASI sensors is also calculated. To do so, we directly
compare the measured TCs of all the trace gases and compute the
median difference of this difference time series.
This temporal decomposition has been done on a logarithmic scale, i.e. our
measured time series correspond to the logarithm of the measured TCs of all the trace gases. This approach has two clear advantages in the
subsequent IASI–FTS comparison: (i) the [TC]gas variations on this
scale can be interpreted as variations relative to the reference mean values
(Δln[TC]gas≈Δ[TC]gas/[TC]gas) and we thereby directly
compare the anomalies observed by both remote sensing instruments, and
(ii) the relative differences between IASI and FTS observations can directly
be computed as the subtraction of the corresponding variability on the
different timescales (note that the temporal decomposition produces values
very close to zero, thereby computing the standard relative differences
provides very extreme values in some cases).
Summary of the temporal and spatial collocation criteria adopted for
each trace gas. Also shown are the typical degree of freedom for signal
(DOFS) for IASI and FTS products, and the number of coincident observations
between IASI and FTS (N). For IASI, the expected DOFS for O3 and
CO are taken from the Products User Guide
(EUM/OPSEPS/MAN/04/0033, EUMETSAT), while for CH4, N2O, and
CO2 those are obtained from and
. The FTS DOFS are calculated from the corresponding
retrievals between 2010 and 2014 (median ±1σ). Note that daily
means 24 h means.
Row averaging kernels for O3 and CH4 as observed by
IASI-A and FTS instruments, expressed on logarithm scale, for typical
measurement conditions at IZO. US is upper stratosphere, MS is middle
stratosphere, UTLS is upper troposphere and lower stratosphere, and T is
troposphere. Also shown are the total degree of freedom for signal (DOFS),
the vertical profile of the cumulative DOFS, as well as the a priori VMR
profiles used for the FTS retrievals.
Collocation criteria
IASI and ground-based FTS instruments are sensing areas of different size and
the acquisition times are generally not exactly simultaneous. In addition,
both instruments have different vertical sensitivities. All these features
have to be taken into account in the definition of the comparison strategy
that, on the one hand, ensures the representativeness of the reference FTS
data and, on the other hand, accounts for the spatial and temporal variability
of the trace gases considered.
The collocation criteria selected are the result of a compromise between the
spatial and temporal variability of each trace gas and the uncertainties and
spatial range covered by the FTS observations and therefore can vary from gas
to gas. Appendix describes in detail the methodology followed to
define the optimal coincidence criteria adopted for each trace gas,
summarised in Table . In summary, we consider all the IASI
observations within the box ±1∘ latitude/longitude centred at the
IZO location (Fig. ) and pair those IASI and FTS observations
taken within ±1 h for O3 and CO, and we pair daily median
observations corresponding to the same day for the rest of trace gases. As
previously mentioned, we consider all the TC observations disseminated in V5:
September 2010–September 2014 for IASI-A and December 2012–September 2014
for IASI-B. From this data set we only work with the best quality IASI L2 V5
measurements: over sea, cloud free, and with the highest level of quality and
completeness of the IASI retrieval as indicated in the Products User
Guide
(EUM/OPSEPS/MAN/04/0033, EUMETSAT).
For the consistency study between both IASI sensors (IASI-A and IASI-B) the
2∘ square has been divided in boxes of ±0.25∘. Moreover,
we distinguish between daily morning (10:00–11:00 UTC) and daily evening
(22:00–23:00 UTC) TC observation overpasses in order to carefully analyse a
possible bias for each overpass. Then, we compare TC amounts from each
overpass and sensor for the same day. Note that for this study the IASI
observations are paired without forcing temporal coincidence with the FTS
data.
Vertical sensitivity
The vertical sensitivity of a remote sensing spectrometer depends, among
other things, on the geometry of observations, the target gas considered as
well as the specific characteristics of each instrument (e.g. the signal to
noise ratio, spectral resolution). Hence, the responses of IASI and
FTS to real atmospheric variability are significantly different. This fact
can be observed in Fig. , where the rows of the IASI and FTS
averaging kernels (A) are displayed for O3 and
CH4. The IASI A are not operationally disseminated in V5;
therefore, to illustrate its vertical sensitivity, we have taken the O3A from the EUMETSAT/IASI L2 version 6 products (EPS Product
Validation Report: IASI L2 PPF v6, EUM/TSS/REP/14/776443 v4C, EUMETSAT),
while the IASI CH4A have been obtained in the framework
of the European project MUSICA . Note that the rows of
A describe the altitude regions that mainly contribute to the
retrieved VMR profile and therefore these kernels can be used to identify the
independent layers without significant overlap with other layers. Indeed, the
trace of A (so-called the degrees of freedom for signal, DOFS) is
a measure of the number of independent layers retrieved from the remote
sensing measurements. Also, Fig. includes the vertical profiles of
the cumulative DOFS, calculated from the top of the atmosphere to surface for
IASI and inversely for FTS as well as the a priori VMR used for the FTS
retrievals in order to compare with the vertical sensitivity of the two
remote sensing instruments.
Figure illustrates two relevant facts that have to be taken into
account when comparing IASI and FTS observations. First, IASI has a lower
number of DOFS than FTS and the maximum sensitivity within these detected
layers is located at different altitudes. Therefore, the TC amounts
observed by both instruments could differently reflect the atmospheric
composition variability. For example, when retrieving CH4, IASI only
detects one CH4 layer (DOFS ∼ 1), located in the upper
troposphere/tropopause region (∼ 12–14 km), while the FTS system
detects two independent CH4 partial columns (DOFS ∼ 2.5),
corresponding to the troposphere and the stratosphere. Similar conclusions
might be derived for N2O and COsee, for example,
Fig. 2 of. For O3, the difference between the
vertical sensitivities is not so significant and IASI is expected to be
sensitive to the maximum O3 concentrations in the Chapman layer and
to the tropopause/upper troposphere regions (DOFS ∼ 2.5). The expected
IASI DOFS and the obtained FTS DOFS for all the trace gases are also listed
in Table . The second important fact is that IASI has a weak
sensitivity in the lower troposphere for the trace gases considered in this
study, leading to the variability of the partial columns missed by FTS below
2373 m a.s.l. (IZO altitude) not being crucial for the IASI–FTS
comparison.
Consistency between IASI-A and IASI-B observations
In order to probe the continuity provided by IASI-B as well as the
consistency of each individual IASI sensor, it is indispensable to first
analyse the temporal stability of their observations. For this purpose, we
examine possible drifts and discontinuities in the times series of the
differences between the de-seasonalised variability from IASI-A and IASI-B
averaged on a weekly basis. The drift is defined as the linear trend in the
differences, while the change points (changes in the weekly median of the
difference time series) are analysed by using a robust rank order
change-point test . By using these tools, we
observe that the time series of the differences between the observations from
the morning and evening overpasses for each IASI sensor as well as between
the observations from the two sensors for each overpass are homogenous for
all the trace gases considered (i.e. no change points were detected at
95 % confidence level). Moreover, all the difference time series reveal
no significant drifts at 95 % confidence level. Figure
shows the time series of O3 TC as observed by IASI-A and IASI-B and
the corresponding differences.
This temporal stability study is complemented by analysing whether the
distributions of the IASI-A and IASI-B observations could be statistically
considered equivalent. To do so, we have used the Friedman non-parametric
test, which detects differences in the distributions of related variables by
checking the null hypothesis that multiple dependent samples come from the
same statistical population . By applying this test on
the observed short-term variability time series from the two IASI sensors for
each overpass, which can be considered as four related samples for each trace
gas, we only observe significant differences for the O3 distributions
between evening and morning overpasses (at 95 % confidence level).
Nonetheless, these discrepancies disappear when comparing the observations
for the same overpass; therefore, the IASI sensors distinguish the O3
intra-day concentration variations. Indeed, for this trace gas the agreement
between both sensors for the same overpass is significantly better than
between the two overpasses for each sensor, as observed in Fig. ,
which displays the scatter plots between the O3 de-trended and
de-seasonalised variability as observed by the two IASI sensors for each
overpass.
Time series of O3 TC amounts (in 1×1022 molec m-2) from the IASI-A and IASI-B overpasses (morning, M,
and evening, E) (upper panel) and of the differences between the
corresponding de-seasonalised variability (in %) for the morning and
evening overpasses (middle and bottom panels). The solid lines represent the
difference time series averaged on a weekly basis. The arrows distinguish the
IASI-B commission mode (from 19 December 2012 to 18 June 2013) and the IASI-B
operational mode (from 19 June 2013 onwards).
Scatter plots of the de-trended and de-seasonalised variability (in
%) from the IASI-A and IASI-B overpasses (morning and evening) for
O3. The legend shows the Pearson correlation coefficient, R, and
the colour bar indicates the number of coincident data per bin. The dashed
lines represent the diagonals (x=y).
Pearson correlation coefficient (upper panel) between the
observations from IASI-A and IASI-B overpasses (morning, M, and evening, E)
for all the trace gases considered at different timescales: single
measurements (de-trended and de-seasonalised variability, Det + Des) and
annual cycle (AC). Middle panel shows the same, but for the standard
deviation (in %) of the corresponding differences, while the bottom panel
displays the median bias (in %). The number of coincident observations is
675.
Summary of the IASI-A and FTS comparison for O3:
(a, b) time series of the TC amounts (in 1×1022 molec m-2) and the de-trended variability (in %)
respectively; (c) averaged annual cycle; (d, e) time series of the de-trended+de-seasonalised variability (in
%), and the difference between coincident de-trended+de-seasonalised
variability from IASI-A and FTS (in %) respectively; and
(f) scatter plot of the de-trended + de-seasonalised
variability. For (c) and (f) the Pearson correlation
coefficient is included in the legends and for (e) the standard
deviation of the differences. The number of coincident measurements is
2338.
Pearson correlation coefficient (upper panel) between IASI-A and
FTS observations for all the trace gases considered at different timescales:
single measurements (de-trended + de-seasonalised variability within
±1 h, Det + Des 1 h), daily (de-trended + de-seasonalised
variability within the same day, Det + Des Day), annual, and long-term
trend. The number of coincident data is 2338 for O3, 2003 for
CO, and 425 for N2O, CH4, and CO2. Bottom panel
shows the same, but for the standard deviation (in %) of the corresponding
differences. The solid black lines represent the day-to-day variability
calculated from the FTS observations at IZO between 2010 and
2014.
All of these findings suggest that, on the one hand, the observations from
each sensor are consistent with themselves and, on the other hand, both
sensors similarly reproduce the atmospheric composition variations. The
statistics for the IASI-A and IASI-B intercomparison are summarised in
Fig. (Pearson correlation coefficient and standard deviation of
the de-trended and de-seasonalised differences, and median bias). In summary,
we observe that both IASI sensors similarly reproduce the annual cycle of all
the trace gases considered (R>0.95 except for CO2, for which we
observe a poorer agreement, R∼0.70–0.85), while for the very short-term
concentration variations we find a large correlation for O3
(∼ 0.80) and moderate for the rest of trace gases (R∼0.30–0.60).
The scatter (1σ) of the differences among sensors and overpasses, on a
measurement-to-measurement basis, is less than 10 % for CO,
∼ 2 % for O3 and between 1 and 2 % for the rest of the
trace gases, while it decreases when comparing annual cycles: 2 % for
CO, less than 0.5 % for the rest of trace gases. Regarding the
biases between the IASI sensors (IASI-A–IASI-B), the values of
∼-3 % for CO and between 0.4 and 0.6 % for N2O and
CH4 are remarkable (bottom panel in Fig. ). For O3
and CO2 the bias is lower than 0.2 % in absolute value.
Comparison between IASI and ground-based FTS observations
This section presents the IASI–FTS comparison at different timescales:
single measurements, daily, annual, and long-term trends. An example of this
strategy is displayed in Fig. for O3, while the summary of
the intercomparison results for all the trace gases is shown in
Fig. (Pearson correlation coefficient and standard deviation of
the differences between IASI and FTS products). The consistency between both
IASI sensors has been documented in the previous section. Therefore, here we
only focus on IASI-A since it has the longest time series of measurements
(September 2010–September 2014 for V5) as well as to ensure a homogeneous
sampling during the whole period analysed.
As observed in Fig. , IASI reproduces the ground-based FTS
observations well at the longest temporal scales, i.e. annual cycles and
long-term trends. For the latter the correlation is larger than 0.95 for all
the trace gases with the exception of CO2 (R∼0.70), while on an
annual basis, the correlation is larger than 0.95 for O3 and
CO and between 0.75 and 0.85 for CH4 and N2O. The
discrepancies found for the annual cycles (amplitude and phase) can be
explained by the different sensitivity of IASI and ground-based FTS
instruments. As observed in Figs. c and , for O3
and CO the IASI and FTS annual cycles are completely in phase, but the
peak-to-peak amplitude is slightly different. For O3 the largest
differences are observed during spring–early summer due to the missing
sensitivity of IASI in the troposphere and in part in the tropopause region
(recall Fig. ), while for CO we observe that IASI tends to
overestimate the variability observed by the FTS. For CH4 and
N2O the results are very similar: we observe that IASI products
follow the annual shift of the tropopause altitude, where the maximum IASI
sensitivity is located, and while FTS also reflects that annual shift, it is
also sensitive to the tropospheric and stratospheric CH4 and
N2O variations. As a consequence, the annual cycles observed by both
remote sensors are slightly out of phase. For CO2 the annual cycles
are not correlated either in phase or in amplitude. This is likely due to
the algorithm used in the IASI L2 processor which has not been specifically
optimised for CO2, since the middle infrared FTS CO2 products
have successfully proven their reliability to monitor the CO2
concentrations . In particular, the IASI CO2
retrieval is solely based on the IASI measurements unlike in
, where collocated microwave measurements are exploited
together with IASI data to disentangle the temperature and the CO2 signals in
the thermal infrared spectra.
Multi-annual averaged annual cycle for CO, N2O,
CH4, and CO2 from IASI-A and FTS observations.
For the shortest-term variations we find poorer agreements, although the
correlation is significantly larger for O3 (∼ 0.80), but not
for the rest of trace gases (R∼ 0.10–0.30). When comparing daily
values for CO the agreement improves (R∼ 0.50), suggesting that
the IASI sensor could moderately capture the day-to-day concentration
variations but not the intra-day variability. The scatter (1σ) of the
differences between IASI and FTS observations, which can be used as a
conservative estimate of the IASI uncertainties, is less than
∼ 10 % for CO, ∼ 3 % for O3, and between
1 and 2 % for the rest of the trace gases. All of them are within the target
uncertainties of the IASI mission (recall Table ) and seem to be good
enough to capture the day-to-day variations for O3 and CO when
comparing to those observed by FTS records at IZO (values also included in
Fig. as a reference). The uncertainties reported here agree well
with previous validation studies of IASI L2 V5 operational products using
different measurement platforms. For example, errors below 5 % have been
reported for O3e.g., between
10 and 15 % for COe.g., and ∼ 2 % for
N2O and CH4. Note that IASI–FTS comparison
also confirms the results observed for the consistency study of IASI-A and
IASI-B sensors. The correlations and the scatter of the differences observed
for both analysis are very similar both at short-term and intra-annual timescales (recall Figs. and ) with the exception of
CO2. For this trace gas the consistency study reveals a moderate
agreement between both IASI sensors (correlations between 0.6 and 0.8 for the
annual cycles), but we do not document any agreement for the IASI–FTS
comparison (correlation less than 0.2). This is likely due to the degree of
maturity of the IASI CO2 products, as aforementioned. Therefore, the
continuous intercomparison of both IASI sensors could successfully be used
as a quality control for identifying inconsistencies or instrumental issues
in lack of reference ground-based observations.
The differences between the IASI and FTS short-term variability have been
analysed as a function of the different parameters, such as the relative
horizontal distance between IASI footprints and FTS location, or of the
different viewing geometry of the two remote sensing instruments, without
identifying significant patterns. As example, the study of viewing geometry
is displayed in Fig. , where we observe that the differences are
uncorrelated to the viewing geometry (IASI-A air mass ()
and difference between IASI-A and FTS AMS). Only the differences for
O3 are displayed as example. However, the same behaviour has been
observed for all the trace gases considered.
(a) Differences between the O3 de-trended and
de-seasonalised variability time series from IASI-A and FTS (in %) as a
function of the IASI-A air mass (AM) plotted as black squares with error bar
(median and standard deviation per bins of 0.1 of AM), and simultaneous 2-D
plot showing the difference between the IASI-A and FTS AMS. (b) Same
as (a) but versus the aerosol optical depth (AOD) at 500 nm,
recorded at AERONET SCO station during summer. Also, the differences between
IASI-A and IASI-B are included. The black and white squares represent the
median and standard deviation per bins of 0.025 of AOD for the
IASI-A–FTS and IASI-A–IASI-B differences respectively.
(c) Same as (b) but during winter.
In addition, the strategic location of IZO allows us to address the IASI–FTS
comparison for different atmospheric conditions. Figure also shows
the IASI–FTS differences as a function of the aerosol optical depth (AOD) records.
This aerosol parameter characterises the total extinction in the line of
sight due to atmospheric aerosols and, thus, it can be used as a tracer of
Saharan desert air masses in the subtropical North Atlantic region
e.g.. To identify these Saharan events, we have
considered the AOD observations performed at the Santa Cruz de Tenerife (SCO)
station from AERONET network . SCO, managed by AEMET, is also
located on Tenerife, but at sea level (≈ 50 m of altitude) and very
close to the sea. Therefore, the SCO AOD records can better quantify the
mineral dust outbreaks occurring both in winter (below 2 km of altitude) and
in summer (between 2 and 6 km of altitude). Note that although the winter
events rarely reach IZO, they will affect the IASI observations including the
surrounding ocean. As observed in Fig. , during summer, the
differences between IASI and FTS observations seem to be affected by the
range of the aerosol load, increasing (absolute value) as the corresponding
AOD values increase. This pattern is likely due to the different type of
observations: while the FTS measurements are performed from the ground in the
direct solar path, the IASI sensors record thermal emission of the
Earth–atmosphere from the space and could be more affected by aerosol
signatures (thermal emission and scattering processes) e.g.and
references therein. Indeed, when comparing the
observations from the two IASI sensors (also displayed in Fig. )
this difference disappears, which is expected given the very good spectral
and radiometric consistency of the two instruments. However, in winter, the
IASI–FTS differences seem to be independent on AOD. This fact is likely due
to the limited sensitivity of IASI to the boundary layer because of
decreasing thermal contrast between the surface and the atmosphere when
approaching the surface. In addition to the Saharan conditions, we have also
analysed the IASI–FTS comparison under polluted air masses likely coming
from North America or Europe and references therein by
using the intra-day CO concentration variations from the GAW (Global Atmospheric Watch) in situ
observations recorded at IZO as a tracer (see Appendix for details
about GAW programme at IZO). No significant patterns were observed (data
not shown), but further analysis and longer time series are needed to
extract more robust conclusions.
Until now, the IASI–FTS comparison has been addressed in terms of relative
variability, but a comparison of absolute TC amounts also provides us useful
information. Therefore, to roughly estimate possible biases between IASI-A
and FTS observations, the partial column amounts below IZO altitude, computed
from the WACCM climatological data, have been added to the FTS observations.
By using those data, we find that the IASI observations are consistently
lower than FTS observations for all the trace gases, with a median bias
(IASI–FTS) of ∼-6 % for O3 and CH4 (-6.4 and
-5.9 % respectively) and ∼-12 % for N2O and
CO2 (-12.4 and -12.1 % respectively), except for CO.
For CO we observe the contrary behaviour; i.e. the IASI sensor
overestimates the FTS observations by ∼ 15 % (15.2 %). These
discrepancies could be partly attributed to systematic IASI and FTS error
caused, for example, by the lower IASI sensitivity to the lower troposphere,
uncertainties in the IASI ANNs training procedure or in the spectroscopic
line parameters. As previously mentioned in Sect. 3, the errors in the
spectroscopic line parameters could explain between 2 and 4 % of the FTS
bias, according to our error estimation (see Appendix ). Indeed,
experimental intercomparisons between FTS and Brewer observations carried
out at IZO in the last years found that FTS systematically overestimates the
Brewer O3 TC amounts by ∼ 4 %
, which may be due to inconsistencies
in the ultraviolet and infrared spectroscopic parameters. This implies that
the IASI O3 observations should have less bias with respect to the
actual O3 concentrations (less than 2 %). However, for CO,
the bias obtained is likely introduced by the WACCM estimation since previous
studies comparing to other space-based CO products, like the MOPITT
sensor , or to dedicated IASI CO retrievals, like the
FORLI-CO algorithm , found biases lower than
7 % for regions with low background CO concentrations such as the
Atlantic Ocean.
Conclusions
This paper documents, for the first time, the uncertainty and the long-term
consistency of all EUMETSAT/IASI trace gas products at the same time and
using a unique measurement technique as reference, the ground-based FTS experiment.
Firstly, we show that the EUMETSAT/IASI trace gas observations, from both
Metop-A/IASI and Metop-B/IASI, are consistent; i.e. neither drifts in time
nor were biases found. Therefore, the observations from both remote sensing
instruments could be merged to obtain a unique IASI database. Secondly, we
focus on the IASI versus ground-based FTS measurement comparison. IASI
adequately captures the day-to-day TC variation, the annual cycle,
and the long-term trend for its operational products O3 and
CO, as compared to ground-based FTS measurements. Likewise, for
N2O and CH4 (trace gases with a rather small day-to-day
variability), IASI observations can successfully be used to describe their
seasonality and interannual trend. However, for CO2 an acceptable
agreement is only achieved at the long-term scale. For the latter three
gases, disseminated as aspirational products, improvements in the EUMETSAT
retrieval algorithms are currently being carried out to include mature
algorithms from the wider scientific community. FORLI-CO and
FORLI-O3 are also being included in the operational IASI L2 suite
. The same methodology can be applied to the upgraded
products to support their monitoring in the long term and in view of the
expectation of a continued data record from IASI on Metop-C.
This consistency and quality assessment has been carried out using the
ground-based FTS located at the IZO and, thus,
it is valid for the subtropical North Atlantic region under free troposphere
conditions. Although this quality documentation can be used as a benchmark
for studies that apply EUMETSAT/IASI trace gas products in climate research,
further comparison studies covering other regions might be desirable in order
to analyse the possible impact of latitude or other environments, such as
urban-industrial or biomass burning areas, on the IASI products accuracy.
Finally, this paper highlights the potential of ground-based FTS experiments
once again as an indispensable reference for validating the current
space-based observations as well as those anticipated from the next
generation of satellite sensors.
Theoretical error estimation of the FTS products
Theoretically, the error of the different FTS products can be estimated by
following the formalism detailed by , where the difference
between the retrieved state, x^, and the real state, x,
can be written as a linear combination of the a priori state, xa,
the real and estimated model parameters, b and b^
respectively and the measurement noise ϵ:
(x^-x)=(A-I)(x-xa)+GKb(b-b^)+Gϵ,
where G represents the gain matrix, Kb a sensitivity
matrix to model parameters, I the identify matrix, and
A the averaging kernel matrix. A relates the real
variability to the measured variability of the considered atmospheric state
and, thus, represents the way in which the remote sensing system smoothes the
real vertical profiles . Therefore, Eq. () defines
three types of error: the first term is the smoothing error associated with
the limited vertical sensitivity of the FTS instruments, the second one
represents the errors due to uncertainties in the input/model parameters
(instrumental characteristics, spectroscopy data), and the third one
corresponds to the measurement noise).
The theoretical error estimation strongly depends on the assumed
uncertainties. In our case, we consider the error sources and values listed
in Table for the input parameters, which are the leading error
sources affecting the different FTS products, identified from our experience
and the literature and references therein,
while the smoothing error is calculated as
(A-I)Sa (A-I)T, where
Sa matrix is the is the covariance matrix of the target gas.
Strictly, to estimate the smoothing error contribution, the covariance matrix
of a real ensemble of atmospheric states must be known .
However, due to lack of real observations of the vertical profiles of all the
trace gas considered at IZO, the Sa for each target gas is
assumed and calculated from the WACCM-V6 model estimates. WACCM is a global
chemistry model of well-recognised prestigious that has widely demonstrated
its ability to provide reliable estimations of the vertical profiles of trace
gases and their expected concentration variations
. Therefore, here the
Sa is calculated considering the variance of the corresponding
gas concentrations at each altitude from the WACCM-V6 climatological data and
a Gaussian distribution of strength 5 km for the inter-layer correlation.
Note that the total error values are calculated as the root sum squares of
all the error sources considered, where the contribution of each error source
has been split into statistical and systematic contributions. The exceptions
are the spectroscopic parameters and the measurement noise, which are
considered as purely systematic and statistical respectively. This error
estimation has been applied to the IZO FTS observations between 2010 and 2014
(period studied in the current work).
Error sources used for the theoretical error estimation for all the
FTS products (chann.: channeling; eff.: efficiency; err.: error; int.:
intensity; ν-scale: spectral position; S: intensity; γ:
pressure broadening parameter). The second column gives the assumed error
value and the third column the partitioning of this error between statistical
(ST) and systematic (SY) contributions .
Error sourceErrorST/SYBaseline (chann. and offset)0.1 and 0.1 %50/50Modulation eff. and phase err.1 % and 0.01 rad50/50Temperature profile2–5 K70/30Line of sight0.1∘90/10Solar lines (int. and ν-scale)1 % and 10-680/20Spectroscopy2 % for S and 5 % γ0/100
Total statistical (ST) and systematic (SY) errors (in %) as a
function of the solar zenith angle (SZA, in ∘) for FTS O3 and
CH4 measurements between 2010 and 2014. The black solid line
represents the limit value of SZA =75∘. Beyond this value the FTS
observations are discarded.
The FTS total errors (statistical and systematic) depend on the observing
geometry at which FTS observations are carried out. As illustrated in
Fig. , the larger theoretical errors are found at high solar
zenith angles (SZAs), mainly due to the fact that the FTS observations are
more sensitive to possible misalignments of the solar tracker at these SZAs.
Therefore, these data (SZA >75∘) are excluded from the study to
avoid unrealistic FTS retrievals in the FTS-IASI intercomparison, which
represent between 1 % for CO and 8 % for N2O,
CH4, and CO2. Considering the filtered FTS observations, the
total statistical errors (medians and ±1σ) are
0.40 ± 0.03 % for O3, 0.50 ± 0.01 % for
CO, 0.20 ± 0.03 % for N2O, 0.30 ± 0.03 %
for CH4, and 0.60 ± 0.14 % for CO2. The major error
sources for the tropospheric gases (N2O, CH4, CO2, and
CO) are the baseline uncertainties and the measurement noise, while
the uncertainties in the FTS's instrumental line shape (described by the
modulation efficiency and the phase error) dominate the statistical errors
for the stratospheric gas O3. For all the target gases, the
systematic error budget is lead by the spectroscopic errors, with median
values and ±1σ of 2.00 ± 0.01 % for O3,
2.10 ± 0.04 % for CO, 2.10 ± 0.01 % for
N2O, 2.35 ± 0.01 % for CH4, and
3.50 ± 0.04 % for CO2.
Collocation criteria between IASI and ground-based FTS observations
To define the temporal collocation, we first estimate the intra-day
concentration variations of the target gases and, then, analyse whether the
FTS system is good enough to detect this variability by comparing to the
respective FTS uncertainties. To do so and to be independent from the FTS
observations, we use the high-frequency and high-quality data routinely
measured by different in situ analyzers and Brewer spectrometers at IZO.
Ground-level in situ atmospheric continuous measurements of CO2
(since 1984), CH4 (since 1984), CO (since 2008), and
N2O (since 2007) have been routinely carried out at IZO as a
contribution of AEMET to the WMO GAW programme
. The high quality of these measurements has been externally
assessed by (1) periodic audits performed in 2004 (CH4), 2008
N2O;, 2009 CO, CH4, and
N2O;and references therein, and 2013–2014
(CO, CH4, CO2, and N2O, report in preparation)
by the World Calibration Centre for Surface Ozone, Carbon Monoxide, Methane
and Carbon Dioxide (WCC-Empa), and the World Calibration Centre for Nitrous
Oxide (WCC-N2O); (2) the participation in WMO Round Robin
intercomparisons (e.g. WMO Round Robin 5,
www.esrl.noaa.gov/gmd/ccgg/wmorr/wmorr_results.php); and (3) the
continuous comparison to simultaneous weekly discrete data
obtained by the NOAA analysis of weekly collected
flask samples, within the NOAA/ESRL/GMD CCGG cooperative air sampling network
(www.esrl.noaa.gov/gmd/ccgg/flask.php). The expected uncertainties in
these IZO continuous atmospheric measurements are ±0.1 ppm for
CO2, ±2 ppb for CH4, ±0.2 ppb for N2O, and
±2 ppb for CO. Refer to , and
for more details about the measurements and the techniques
used. Regarding O3, we use the daytime O3 TC observations
performed by Brewer spectrometers at IZO since 1991. Like the FTS measurements,
the Brewer O3 data are part of NDACC since 2001. Furthermore, since
2003 they are the Regional Brewer Calibration Center for Europe
(www.rbcc-e.org) of the WMO GAW. This guarantees the high quality of
their measurements better than 1 %;and references
therein.
Intra-day variation coefficient (VC) for O3 total column and
in situ CH4 (in %]) as observed by a Brewer spectrometer and a GAW
in situ GC-FID analyzer respectively between 2008 and 2013. The solid and
dashed black lines represent the median and ±1σ of the reference
intra-day VC respectively, and the dashed red lines represent the range of
theoretical and experimental FTS errors.
The intra-day concentration variations have been estimated through the
intra-day variation coefficient (VC), calculated as the daily standard
deviation divided by the daily mean of the corresponding observations,
considering daytime Brewer measurements for O3 and night-time GAW
hourly mole fractions means for the rest of the gases (20:00–08:00 UTC).
Note that the IZO in situ night-time data represent the background regional
signal of the free troposphere well, while during daytime thermally driven
up-slope flow from maritime boundary layer can reach the station and, thus,
the in situ data are not well suited for comparing to remote sensing
observations. By considering the available time series of these observations
since the IASI data are operationally disseminated, i.e. 2008–2013 (see
Fig. for CH4 and O3), we have estimated the
following typical intra-day VC (medians and ±1σ):
0.63 ± 0.52 % for O3, 2.90 ± 2.65 % for
CO, 0.08 ± 0.02 % for N2O, 0.29 ± 0.17 %
for CH4, and 0.05 ± 0.07 % for CO2. When comparing
experimental and theoretical FTS uncertainties (recall Table ) to
these values, we observe that for CO and O3 the intra-day VC
is larger or much larger than the FTS uncertainty and, therefore, the
individual FTS measurements (or hourly medians) should be considered to
ensure optimal temporal collocation. Likewise, for CH4, N2O,
and CO2, daily medians of the FTS observations can be taken without
losing information or affecting the validation results (FTS uncertainty is
larger than or comparable to the typical intra-day VC). Note that the
remaining concentration variations within the defined temporal window have to
be considered when performing the IASI–FTS intercomparison. For CO
the hourly VC, considering only night-time observations, is estimated to be
0.98 ± 1.43 %, while for O3 it is 0.31 ± 0.37 %.
Horizontal distance covered by the FTS observations (in km) versus
the solar zenith angle (SZA; in ∘), at which they are taken for all
the target gases. The dashed lines represent the maximal horizontal
distances.
As for the temporal collocation, the spatial coincidence criteria have to
take into account the spatial concentration variations of each trace gas and
the maximal horizontal distance covered by the FTS observations. Since the
FTS measurements are performed in the direct solar path, the horizontal
projection of the air masses probed by the FTS can be easily calculated from
the actual solar observing geometry and the effective altitude of the
vertical column observed by the FTS. The latter has been defined as the
altitude at which 95 % of the corresponding TC amount is
observed and, thus, varies from gas to gas. These effective altitudes have
been determined by using the WACCM-V6 climatological data and are
∼ 40 km for O3 and ∼ 20 km for the rest of gases,
resulting in a maximal horizontal distance of ∼ 150 km for O3
and ∼ 80 km for the rest of gases (see Fig. ).
Regarding the spatial concentration variations of each trace gas, we should
consider that IZO is far away from the target gas sources/sinks and embedded
in the free troposphere, thereby usually affected by long-range
transports of aged and well-mixed air masses e.g.and references
therein. In addition, the latitudinal/longitudinal gradients of
the trace gases considered here are rather smooth at oceanic subtropical
latitudes. Indeed, the latitudinal relative difference of CO2 between
the Equator and 60∘ N in 2012 was smaller than 2.5 %
, leading to a mean CO2 gradient smaller than 0.04 %
per degree of latitude. For CH4 and CO, the latitudinal
relative difference between the means of the latitudinal bands 0–30 and
30–60∘ N in 2012 was smaller than 3.8 and 40 % respectively
. This implies mean CH4 and CO gradients smaller
than 0.13 and 1.3 % per degree of latitude respectively. In the previous
three gradient estimations, we have also taken into account the seasonal
cycles, which depend on latitude (i.e. we are not simply considering the
annual mean latitudinal gradients). For N2O, the latitudinal relative
difference between 20 and 40∘ N is smaller than 0.32 %
and the seasonal cycle is insignificant as can be seen
in . This implies a mean N2O gradient smaller than
0.016 % per degree of latitude. For O3 a gradient of 0.92 %
per degree of latitude could be expected at the IZO latitude (value obtained
from the ozone observations in 2012 of the space-based Ozone Monitoring
Instrument (OMI); ). Therefore, assuming constant
latitudinal gradients within the box ±1∘ latitude/longitude
centred at IZO, the spatial concentration variations inside the box (defined
in an equivalent way as the temporal intra-day VC) are expected to be
0.53 % for O3, 0.75 % for CO, 0.01 % for
N2O, 0.08 % for CH4, and 0.023 % for CO2
(where we have taken into account that the standard deviation, in a segment
of length 2∘, of a linear function with slope Gr per degree, is equal
to Gr /3). These spatial VC are similar (for O3 and
CO) or much smaller (for the rest of trace gases) than the statistical
uncertainties of the FTS (recall Table ). Therefore, no significant
concentration variations might be expected within the actual area probed by
the FTS observations and, indeed, a slightly wider range than this can be
applied for collocating IASI measurements without affecting the validation
results. Thus, we define a validation box of ±1∘ centred at IZO
location (i.e. ±∼110 km at IZO latitude) for all the trace gases.
Previous studies at IZO latitudes found no significant impact of the spatial
co-location criteria (50–100 km) on the differences between IASI and FTS
TCs for N2O, CH4, or CO.
Acknowledgements
The research leading to these results has received funding from the
Ministerio de Economía y Competitividad from Spain for the project
CGL2012-37505 (NOVIA project) and from EUMETSAT under the Fellowship
Programme (VALIASI project). Furthermore, M. Schneider, S. Barthlott,
A. Wiegele, and Y. González are supported by the European Research Council
under FP7/(2007–2013)/ERC grant agreement no. 256961 (MUSICA project).
Edited by: A. Kokhanovsky
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