IASI on Metop-A Operational Level 2 retrievals after five years in orbit

<strong>IASI</strong> on Metop-A: Operational Level 2 retrievals
after five years in orbit
Thomas August n , Dieter Klaes, Peter Schlüssel, Tim Hultberg, Marc Crapeau,
Arlindo Arriaga, Anne O’Carroll, Dorothée Coppens, Rose Munro, Xavier Calbet
EUMETSAT, Eumetsat Allee 1, D-64295 Darmstadt, Germany
article info
Available online 9 March 2012
Keywords:
Hyperspectral infrared sounding
<strong>IASI</strong>
Metop
Operational retrievals
1. Introduction
1.1. The EUMETSAT Polar System
abstract
The EUMETSAT Polar System (EPS) is EUMETSAT’s
contribution to the Initial Joint Polar System (IJPS). The
IJPS provides observations for operational meteorology
and climate monitoring from both the mid morning orbit,
under the responsibility of EUMETSAT, and the afternoon
orbit, under the responsibility of NOAA.
The Metop satellite series is the space component of
the EUMETSAT Polar System (EPS). Metop-A, the first in a
series of three spacecraft, was launched in October 2006.
Since May 2007 it has provided data continuously from
the eight meteorological instruments on board. The three
n
Corresponding author. Tel.: þ49 6151 807 5650;
fax: þ49 6151 807 8380.
E-mail address: thomas.august@eumetsat.int (T. August).
Contents lists available at SciVerse ScienceDirect
Journal of Quantitative Spectroscopy &
Radiative Transfer
0022-4073/$ - see front matter & 2012 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jqsrt.2012.02.028
Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
journal homepage: www.elsevier.com/locate/jqsrt
Geophysical parameters from the <strong>IASI</strong> instrument on Metop-A are essential products
provided from EUMETSAT’s Central Facility in near real time. They include vertical
profiles of temperature and humidity, related cloud information, surface emissivity and
temperature, and atmospheric composition parameters (CO, ozone and several other
trace gases). As compared to previous operational processor versions, the latest
processor version 5 delivers significant improvements in retrieval performance for
most major products. These include improvements to cloud properties products, cloud
detection (with a positive impact on the knowledge of the sea surface temperature,
SST), the temperature profile (especially in the mid and upper troposphere), and ozone
and carbon monoxide total columns.
This paper provides a comprehensive summary of the processing algorithms, the
latest scientific developments, and the related validation studies and activities. It
concludes with a discussion of the future outlook.
& 2012 Elsevier Ltd. All rights reserved.
Metop satellites (with launches of Metop-B and -C
planned in 2012 and 2017, respectively) will provide a
continuous service from the mid-morning orbit (9:30
Local Solar Time equator crossing time, descending node)
for at least 15 years. More details on the EPS/Metop
system can be found in [1].
The most innovative and one of the key instruments on
Metop is the Infrared Atmospheric Sounding Interferometer
(<strong>IASI</strong>). Three instruments were developed for Metop
by CNES (Centre National d’Etudes Spatiales) in cooperation
with EUMETSAT. They are built to provide temperature,
moisture with unprecedented accuracy and
resolution and additionally to provide information for the
monitoring of atmospheric trace gases. <strong>IASI</strong> is a Michelson
interferometer measuring in the infrared. It measures 8461
spectral samples between 3.62 and 15.5 mm with a resolution
of 0.5 cm 1 after apodisation. <strong>IASI</strong> scans across-track
in 30 successive elementary fields of view (EFOV), each
composed of 4 instantaneous fields of view (IFOV). The
EFOVs span a 748.331 range, symmetric with respect to

the Nadir, in steps of 3.331. The swath width on ground is
approximately 2200 km, which provides global Earth coverage
twice per day. The IFOV is a disc of 12 km diameter
at sub-satellite point. The dimensions increase to 39 km
and 20 km at the swath edge in the across- and along-track
directions, respectively. More details about the <strong>IASI</strong> instrument
are available in [2,3].
Operational products from EPS/Metop are generated in
the EPS Core Ground Segment, located at EUMETSAT headquarters
in Darmstadt, Germany, and also in eight decentralised
Satellite Application Facilities (SAF), hosted by
EUMETSAT Member States. More details on the EPS products
and applications are discussed in [1] and, more
especially, <strong>IASI</strong> applications are discussed by Hilton et al. [4].
1.2. The operational <strong>IASI</strong> Level 2 products
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1341
Development activities for the centrally processed <strong>IASI</strong>
L2 products [5] took place at EUMETSAT and were supported
by science expert groups and additionally through
dedicated internal and external studies. For <strong>IASI</strong> in particular,
as a highly innovative instrument, considerable
scientific support was required. For this purpose and to
foster scientific development for <strong>IASI</strong> the <strong>IASI</strong> Sounding
Science Working Group (ISSWG) was established. A Science
Plan [6] emerged which was used as a guideline for the
product processing development and for the group’s activities.
There was a strong heritage from the Atmospheric
Infrared Sounder (AIRS) embarked on NASA’s Aqua satellite
in 2002, which paved the way for the use of hyperspectral
data in meteorology and climatology [7]. The<strong>IASI</strong>Level2
processing development targeted the generation of temperature
and humidity profile information, the associated
surface information and the retrieval of some trace gas
species: CO, O 3,CH 4,N 2OandCO 2 from the beginning of
Metop operations. The vertical temperature and watervapour
profiles are currently represented and distributed
on a 90-level grid extending between 0.005 and 1050 hPa.
The independence of the retrieval algorithms from Numerical
Weather Prediction (NWP) was a strict requirement
from the beginning. A large number of scientific studies
both internal and external to the ISSWG, supported the
evolution of the science plan and the processor development,
thereby enabling the operational production of <strong>IASI</strong>
L2 products by the end of the instrument’s commissioning.
As with the other EPS products, the <strong>IASI</strong> L2 processor has
been subject to post-launch developments to extend the
scope of and improve further the products. These are
referred to as ‘‘Day2’’ activities.
The version 5 (v5) of the processor was released on the
operational processing chain on 14 September 2010. In
the following we provide a full description of this new
processor version, the associated validation results, the
improvements with respect to version 4, and the latest
developments for each of the retrieved parameters after 5
years in operation. Each parameter is covered in one or
more dedicated algorithm description documents and
validation reports, which are cited in the text. They are
accessible on-line in the ‘‘Data & Products section’’ at
www.eumetsat.int/Home/Main/DataProducts/Resources/
index.htm (last accessed 17/01/2012) or alternatively on
request with the reference number.
2. The <strong>IASI</strong> Level 2 PPF version 5
The Level 2 <strong>IASI</strong> Product Processing Facility (PPF) has a
modular structure, represented in Fig. 1. The <strong>IASI</strong> measurements
and various auxiliary data (e.g. measurements from
other EPS instruments, NWP forecasts etc.) are first collocated
in a data pre-processing step, which also includes the
configuration of the retrieval algorithms with coefficients
and thresholds adapted to the time and location of the
acquisitions. The cloudiness within the <strong>IASI</strong> pixel is assessed
in a second step using a number of cloud detection methods.
In the presence of a cloud, the cloud properties – fractional
coverage, height and phase – are determined. The retrieval
steps follow with a series of statistical methods to estimate
the temperature and humidity profiles, the surface emissivity
as well as the sea and land surface temperature (SST and
LST, respectively), and some trace gas species. A physical
retrieval using the optimal estimation method (OEM) is
subsequently performed to refine the temperature and
ozone profiles. These successive processing steps are
detailed in the following sections.
2.1. Pre-processing
Besides the calibrated and apodised spectra stored in
the <strong>IASI</strong> L1c products, the L2 PPF ingests two types of
auxiliary data.
Fig. 1. Modular representation of the <strong>IASI</strong> Level 2 operational processing chain (version 5), operational since 14/09/2010.

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Table 1
Static atlases used in the <strong>IASI</strong> L2 PPF.
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
Data type Name Resolution
Land/sea mask – Quadtree a
Surface GTOPO 30 [10] 30 in. 30 in.
elevation
( 1 km)
Emissivity Global land surface
0.51 0.51
Atlas
climatology
Full spectrum
for <strong>IASI</strong> measurements [9] (via PCs)
Monthly means
a A quadtree is a tree data structure storing values only for homogeneous
regions rather than for every pixel. The spatial resolution is thus
varying and adapted here to the land/sea coastal information. The
dataset detailed description and usage can be found in the EUMETSAT
Technical Note ‘‘Description of Land/sea and Coastline Data Bases for
AAPP upgrade’’, EUM/EPS/SYS/TEN/00/018.
The first type are the static data: the auxiliary datasets
configuring the various algorithms in the <strong>IASI</strong> L2 PPF (e.g.
thresholds) and the static atlases (listed in Table 1) used
to compute the land/sea fractional coverage, the surface
elevation and the land surface emissivity within a given
field of view. In previous releases, the land surface
emissivity was derived from the International Geosphere/Biosphere
Programme (IGBP) land surface properties
database [8]. In the operational processor version 5
this is replaced by the recent land surface emissivity
monthly atlas based on <strong>IASI</strong> measurements developed
by Zhou et al. [9]. The emissivity is defined on a coarser
grid (0.51 0.51 longitude/latitude) but is provided at the
full <strong>IASI</strong> spectral resolution.
The second type of auxiliary information is data flowing
in near real time (NRT) to support the <strong>IASI</strong> L2
retrievals, such as NWP forecasts from the European
Centre for Medium-Range Weather Forecasts (ECMWF)
and data from other instruments on-board Metop:
namely the Advanced Very High Resolution Radiometer
(AVHRR), the Advanced Microwave Sounding Unit
(AMSU) and the (Microwave Humidity Sounder) MHS
L1B products [1]. The validity of these variable data and
of the <strong>IASI</strong> L1C spectra is checked before processing by
inspection of their quality flags when applicable, and by
comparison against predefined and configurable validity
bounds.
2.1.1. Auxiliary data and measurements collocation
When the spatial resolution of the auxiliary data is
higher than the <strong>IASI</strong> IFOV the information is averaged
within the IFOV and weighted with the <strong>IASI</strong> point spread
function (PSF) to account for non-homogeneities in the
detector’s response. This is the case with the digital
elevation model (DEM), the AVHRR cloud information
and the land/sea mask.
In contrast, when the spatial resolution is of the order
the <strong>IASI</strong> footprint size (of 12 km at nadir), as with the
numerical weather prediction (NWP) forecast fields (this
information is used in the cloud detection only), the
collocation is performed using a nearest neighbour
method or by bilinear interpolation between the four
nearest grid-points. For the atmospheric forecast parameters
provided on a vertical grid, such as the temperature,
humidity and ozone profiles, a vertical interpolation
is performed from the original grid to the constant
pressure grid used within the <strong>IASI</strong> L2 PPF prior to the
horizontal interpolation. Since 08/11/2011, the 3-h forecasts
(at 00, 03, 06, 09, 12, 15, 18, 21 UTC) from ECMWF
are ingested. Older L2 products were processed with the
6-h forecasts for the synoptic times 00, 06, 12 and 18 UTC.
The forecast parameters are then interpolated in time as a
first order approximation of the atmospheric state at the
actual sensing time.
Collocation of AMSU and MHS data has been already
implemented in preparation for the planned synergistic
use of microwave measurements with the <strong>IASI</strong> infrared
(IR) spectra within the cloud and atmospheric temperature
and humidity profiles retrievals.
2.1.2. Radiance noise filtering
The radiance noise filtering is the last step of the data
pre-processing sequence and was introduced with PPF v5.
It consists of filtering out part of the noise present in the
<strong>IASI</strong> L1C spectra using principal component analysis (PCA)
techniques. At <strong>IASI</strong> spectral resolution, the top of atmosphere
(TOA) radiances sampled in the 8461 channels are
spectrally correlated and do not represent as many
independent information. PCA theory states that these
radiances can be represented by a small number of
eigenvectors of their covariance matrix. A base of empirical
orthogonal functions (EOF) was therefore computed at
EUMETSAT from a large set of apodised <strong>IASI</strong> L1C spectra
corresponding to a representative collection of atmospheric
situations. We consider here the <strong>IASI</strong> measurements
as the sum of the TOA radiances and the
instrument noise. While the atmospheric signal subspace
lies in the most significant eigenvectors, the random
component (i.e. the noise) is equally distributed in all
eigenvectors. Hence, discarding the eigenvectors of higher
rank allows the TOA radiances to be reconstructed with
part of the noise removed [11]. To reconstruct and noisefilter
the <strong>IASI</strong> bands 1, 2 and 3, respectively, 90, 120 and
80 eigenvectors are currently used in operations. The
difference between the original and the reconstructed
radiance is called the residual. For rare atmospheric
situations – e.g. exceptional volcanic eruptions or wild
fires with unique plume compositions – it might happen
that some spectral features are not well represented by
the eigenvectors. These situations are usually of particular
interest for specific research studies on atmospheric
composition which might require the original spectra
while the operational purposes of the <strong>IASI</strong> L2 products
are better served with low-noise level (i.e. filtered)
radiances. The ability to represent the atmospheric signal
with these leading eigenvectors is routinely monitored.
The collection of spectra supporting the EOF has been
iteratively extended and the eigenvectors updated to
include rare spectra, e.g. from the Kasatochi eruption
(08/08/2008) [12,13] and from the Russian Fires (summer
2010) [14]. The reader is referred to the <strong>IASI</strong> principal
component compression product generation specification
(EUM/OPS-EPS/SPE/08/0199) and validation report

(EUM/OPS-EPS/REP/10/0148), accessible on-line at www.
eumetsat.int in the section Data & Products, for further
details and configuration description.
2.2. Cloud detection
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1343
Efficient cloud detection is essential for accurate L2
product generation because most of the subsequent
retrieval functions are tailored for cloud-free atmospheres.
Thus, undetected cloud contamination would
degrade the quality of the final <strong>IASI</strong> L2 products. The
cloud screening in previous <strong>IASI</strong> L2 PPF versions relied
solely on the NWP cloud test (see Section 2.3.1 below). In
the <strong>IASI</strong> L2 PPF v5 two additional cloud tests were added.
They are described in Sections 2.3.2 and 2.3.3. A more
recent detection technique based on artificial neural networks
(ANN) is discussed in Section 2.4.1. It was introduced
in October 2011 for monitoring purposes only and
will be used routinely starting in 2012.
2.2.1. NWP cloud test
The NWP cloud test consists of comparing the measured
radiances in a selection of channels from IR atmospheric
micro-windows with synthetic clear-sky TOA radiances calculatedforthesamescene.Theaprioriknowledgeofthe
atmospheric state and of the surface temperature and winds
comes from ECMWF forecasts. The surface emissivity is either
computed with an analytical model for ocean surfaces after
Masuda [15] and Watts [16] or, for continental surfaces,
retrieved from the static atlas described in Section 2.2. The
synthetic clear-sky radiances are then computed with a fast
radiative transfer model (FRTM). The FRTM was RT<strong>IASI</strong>-4 for
<strong>IASI</strong> L2 PPF version 5.0 (and previous versions) but was
replaced, with the release of version 5.2 in October 2011,
with the state-of-the-art RTTOV-10. Both versions result from
development of the FRTM implemented by Saunders et al.
[17,18]. The reader is referred to the NWP-SAF product
reports (www.research.metoffice.gov.uk/research/interproj/
nwpsaf/rtm/index.html) and to the ECMWF Technical Memo
425 (2003) for a specific description of the RTMs used in
operations.
In this cloud test, if the observed and the synthetic
radiances differ by more than a configurable threshold, then
the scene is declared cloudy. In the current operational
processor, this threshold is set to 1 K in brightness temperature.
The threshold was determined statistically with
<strong>IASI</strong> observations. It accounts for the errors in the prior state,
especially in the surface parameters. Studies are currently
ongoing to calibrate this test. They indicate that such a
configuration is more appropriate for sea than for land
surfaces, where uncertainties in the surface temperature
and emissivity sometimes translate into errors much larger
than 1 K in the computation of synthetic brightness temperatures.
As a consequence, it is believed that the residual
cloud contamination is higher for land surfaces than for
oceans. The choice of the IR micro-window channels is also
configurable. The channels 751 (832.5 cm 1 ) and 1023
(900.25 cm 1 ) are currently used in the PPF v5. On average,
approximately 75% of the IFOVs tested with this method are
declared cloudy, with regional variations.
2.2.2. AVHRR cloud fraction
The imager AVHRR, flying with <strong>IASI</strong>, provides measurements
in both visible and infrared bands, from which the
presence of a cloud is evaluated for each pixel. It has a
resolution of 1 km at Nadir. At this resolution, the pixels
are classified as either clear or cloudy in a binary fashion.
The determination of the cloud mask is performed in the
AVHRR L1 processing chain. The detection algorithms
involve various tests based on inter-channel brightness
differences in the infrared, reflectances in the visible and
near-infrared (NIR), and some NWP forecast data. They
are more exhaustively listed and described in Section
5.4.4 of the ‘‘EPS Ground Segment AVHRR L1 Product
Generation Specification’’, EUM/EPS/SYS/SPE/990004
(available on-line at: www.eumetsat.int).
The integrated AVHRR cloud fraction (CFR) in this
context is computed as the proportion of AVHRR cloudy
pixels within the <strong>IASI</strong> footprint, weighted by the <strong>IASI</strong>
point spread function. This integrated AVHRR CFR has
been routinely embedded in the <strong>IASI</strong> L1C products since
18 May 2010 following <strong>IASI</strong> L1 Day-2 developments. A
scene is declared cloudy if the amount of AVHRR cloudy
pixels within the <strong>IASI</strong> IFOV exceeds a configurable threshold,
set to 2% in the <strong>IASI</strong> L2 PPF version 5. On average,
approximately 78% of the IFOVs tested with this method
are declared cloudy. This AVHRR cloud test is systematically
used in conjunction with the NWP cloud test and
both methods agree in 90% of the cases. The use of
different cloud tests in combination is specifically
addressed in Section 2.2.5.
2.2.3. Optical thickness test
In this test, the atmospheric optical thickness is evaluated
using the principal components of the <strong>IASI</strong> spectra
as predictors in a linear regression. This retrieval belongs
to an algorithms suite developed in [19] for retrieval of
atmospheric and surface parameters in cloud-free as well
as in partially cloudy conditions. The resulting cloud
optical thickness triggers the choice of regression coefficients
tailored for the atmospheric and surface parameters
retrieval under either clear or partially cloudy
conditions. This test was also introduced with the <strong>IASI</strong> L2
PPF v5 and, with the current configuration, is the least
conservative of the three operational tests with a global
cloud detection rate between 40% and 45%.
2.2.4. Stand-alone ANN cloud test
This test is a novel cloud detection method based on
ANNs which was developed in the frame of an external
study. The principle and performance of this method are
summarised in this section. The full description of the
methodology is accessible in the EUMETSAT study reports
by Brockmann et al. (‘‘Technical Report for the study on
<strong>IASI</strong>/AVHRR Visual Scenes Analysis and Cloud Detection—
IAVISA, Issue 3.0, Revision 4’’, 21 November 2008, 85 pp.
and ‘‘<strong>IASI</strong>/AVHRR Cloud detection and Characterisation,
v1.1’’, 21 July 2010, 100 pp.).
The ANNs described here are multi-layer perceptrons
(MLP) with two or three hidden layers. They can be seen
as non-linear regression operators between a set of
predictors, stored in an input layer, and a predictand, in

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this case the required cloudiness estimate. The inputs are
composed of <strong>IASI</strong> radiances and AVHRR information. The
input <strong>IASI</strong> radiances are sampled at selected wavelengths
commonly used in cloud detection with IR data [20–22];
they are listed in Table 2. The AVHRR information consists
of the means and standard deviations of the collocated
radiance and reflectance clusters, which comprise the
AVHRR radiance analyses present in the <strong>IASI</strong> L1C products.
The inputs are propagated through the networks,
where successive linear combinations and convolutions
apply. The linear combinations involve different sets of
weights and offsets. The convolution (activation) function
is in this case the logistic function f(x)¼1/(1þe x), which
gives their non-linear properties to the nets. The weights
and offsets used to configure the networks are iteratively
adjusted during a so-called training phase, where pairs of
input vectors (<strong>IASI</strong> and AVHRR observations) and their
associated outputs (IFOV cloudiness) are successively
presented to the network. The training consists of
Table 2
List of <strong>IASI</strong> channels used in the ANN cloud detection.
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
Channels Wavenumber (cm 1 ) Channels Wavenumber (cm 1 )
142 680.250 2733 1328.00
546 781.250 3391 1492.50
751 832.500 3770 1587.25
754 833.250 3790 1592.25
1023 900.500 6390 2242.25
1059 909.500 6512 2272.75
1282 965.250 7391 2492.50
2019 1149.50 7547 2531.50
2616 1298.75 8232 2702.75
minimising the errors between the retrieved output and
the teaching target (here the cloudiness). This is achieved
by simple back-propagation of the error gradient [23].
To constitute the teaching patterns, the cloudiness in
24923 <strong>IASI</strong> IFOVs was assessed by visual inspection of the
collocated AVHRR images and classified into four qualitative
categories: clear-sky, low cloud fraction, high cloud
fraction and full cloud coverage. Together with the corresponding
<strong>IASI</strong> and AVHRR observations, they will be
referred to as the IAVISA database. These IFOVs were
randomly selected to provide a full Earth coverage (see
Fig. 2) and an equal representation of day/night, land/sea,
seasonal and geographical/climatological configurations.
Dedicated artificial neural networks were trained for each
of the day/night and land/sea combinations. The IAVISA
database was split into a training subset and a verification
subset (not used for training), to evaluate the generalisation
skill of the networks (i.e. ensure that they are not
too specific to the training data).
The performances of the NWP and AVHRR cloud tests
(see Sections 2.2.1 and 2.2.2) with these visually classified
scenes were assessed and compared to the performances
of the ANN cloud test. They are summarised in the
contingency Table 3. The global ability to correctly classify
clear-sky and the capacity to screen out the cloud contaminated
IFOVs are increased by approximately 25% with
the ANN method in comparison to the NWP test, with a
success rate exceeding 90%. The respective scores have
also been studied individually for different surface types
and day/night configurations. A more exhaustive performance
analysis can be found in the EUMETSAT validation
report EUM/MET/TEN/10/0343. The ANN test is usually
more stringent than the other tests, with better success
rates for the identification of cloud-free IFOVs.
Fig. 2. Distribution of the <strong>IASI</strong> IFOVs in the IAVISA database used for training the ANN cloud detection.

T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1345
2.2.5. Cloud tests combination and impact on <strong>IASI</strong> L2
product quality
Currently, all operational tests (NWP, AVHRR and
optical thickness) are systematically applied. An IFOV is
flagged cloudy if at least one of these tests detects the
presence of a cloud. While this conservative approach
increases the confidence that the remaining clear IFOVs
Table 3
Contingency table of the true (classified by visual inspection) vs. retrieved
cloudiness with the <strong>IASI</strong> NWP and ANN cloud tests. The numbers indicate
the size of the subsamples classified by the respective tests.
NWP test ANN test
Clear Cloudy Clear Cloudy
Visual classification Clear 4803 2456 6708 551
Cloudy 4074 13,590 1363 16,301
are indeed cloud-free it has the disadvantage of discarding
many pixels with false cloud detection from the
subsequent atmospheric parameters retrievals. Approximately
10–15% of the IFOVs are declared cloud-free with
the current NWP and AVHRR tests but only about 5–8%
would remain if the non-linear cloud test were simply
used in addition to the other two.
Fig. 3 shows the agreement/disagreement rate between
three cloud detection methods, computed for the period
19–24 March 2010. It illustrates that simply considering
the intersection of the cloud tests repeatedly reject IFOVs
in regions with a specific climatology or soil type. For
instance sea ice, continental snow cover and Polar Regions
are typical areas where the cloud detection, and especially
with the AVHRR and NWP tests, is less reliable. The
reflectance properties of such surfaces are similar to those
of clouds in the AVHRR visible channels and often confuse
the albedo test. Visual inspection confirmed that this test
Fig. 3. Average agreement rates between the ANN cloud detections and the NWP tests (left), and AVHRR tests (right) during the period 19–24 March
2010. In red: the methods agree, in green: cloud tests disagree with clear-sky as per ANN test only, in blue: cloud tests disagree with cloud-free as per
NWP test (top) or AVHRR (bottom) only. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of
this article.)

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T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
usually overestimates the presence of clouds. The NWP test
requires an accurate description of the atmospheric temperature
and humidity, which the forecast fields are less
able to provide at high latitudes, as well as an accurate
knowledge of the surface temperature and emissivity. At a
surface temperature of 270 K, the cloud detection threshold
of 1 K described in Section 2.2.1 corresponds to an error
smaller than 2% in the surface emissivity at the wavelengths
considered for this test. Measurements of natural
materials [24] however show that the natural variability of
the surface emissivity for water, ice and snow covers is as
high as 5% in this spectral region and depend on the ice and
snow type, density and moisture. This variability is not
necessarily captured by the static emissivity database.
Furthermore, the land/sea classification is so far still a
static process with the consequence that the IFOVs covered
by sea-ice are still treated as open oceans. The current
developments in this area involve the synergistic use of the
microwave measurements from the AMSU companion
instrument on-board Metop to distinguish plain water
and sea ice.
Similarly, clouds are systematically incorrectly reported
in the Ganges and Indus crop land valleys by the NWP test.
Conversely, the AVHRR and NWP tests statistically report
more cloud-free IFOVs in the inter-tropical belt over the
oceans than the ANN test. Visual inspections, as illustrated
by Fig. 4, confirm the actual presence of clouds reported by
the ANN classification when this method disagrees with
the other two. However, the non-linear test was not
trained to detect dust clouds, which the NWP and especially
the AVHRR tests usually capture more efficiently.
This can be seen in the sub-Saharan belt where the AVHRR
images confirm a large dust storm event which was not
detected by the ANN test but is correctly reported by the
AVHRR integrated cloud information.
The identification of clear-sky is important as the retrieval
algorithms defined in the <strong>IASI</strong> L2 science plan are
nominally tailored for cloud-free radiances. The impact of
cloud contamination, and therefore the ability to tolerate it,
does however vary with the atmospheric parameter in
question, the retrieval method and the required product
accuracy defined in the EPS End User Requirements Document
(reference EUM/EPS/MIS/REQ/93/001 available on-line
at: www.eumetsat.int). Clerbaux et al. [25], for instance,
retrieve CO concentrations from <strong>IASI</strong> measurements with
potential IFOV cloud coverage of up to 25% using a retrieval
scheme designed for clear radiances. While this is consistent
with the expected accuracy of 10% for the CO total column,
such high cloud fractions are not compatible with the
accuracy requirement of 0.4 K for clear-sky sea surface
temperature retrievals. In subsequent sections we illustrate
the impact of the three cloud tests discussed in this section
on the SST product yield and quality.
The reference SST product, from the Advanced Along
Track Scanning Radiometer (AATSR) instrument on-board
ENVISAT [26], the <strong>IASI</strong> L2 SST retrieval and the intercomparison
protocol are described in Section 2.5.3. The
inter-comparisons are performed during the period 19–24
March 2010 where <strong>IASI</strong> and AATSR acquisitions over the
same area were taken within 20 min of one another.
Fig. 5a–c shows maps of the SST departures (AATSR–<strong>IASI</strong>)
after cloud filtering with the AVHRR, the NWP and the
ANN test respectively. Fig. 5e–g shows the corresponding
distributions of the AATSR–<strong>IASI</strong> SST differences. All three
distributions have a main Gaussian mode with an asymmetric
tail containing less than 10% of the sample where
<strong>IASI</strong> SST are colder by up to 2 K than the AATSR SST. This
tail is the result of undetected cloud and dust contamination.
While the overall statistics are similar for all three
cloud-tests (positive bias and standard deviation of
approximately 0.47 K) the central mode is narrower with
the ANN than with the NWP and the AVHRR cloud
filtering (a standard deviation of 0.25 versus 0.30 K,
respectively). Furthermore, the visual inspection of
AVHRR images and MODIS aerosol optical depth products
[27] (as shown in Fig. 6 and available at: http://disc.sci.
gsfc.nasa.gov/giovanni/overview/index.html, last accessed
19/01/2012) confirmed the presence of dust aerosols off
the West African coast, in the Arabian Sea and off the East
Asian coast. The dust patterns correlate with the larger
<strong>IASI</strong> and AATSR SST discrepancies. The yield with the ANN
test is also 30% smaller. Outside these three dust contaminated
areas, identified using the MODIS aerosol
product, the AATSR–<strong>IASI</strong> SST departure after the ANN
cloud filtering is nearly Gaussian, with a bias and standard
deviation of þ0.25 and 0.22 K, respectively. This confirms
Fig. 4. Cloud detections in <strong>IASI</strong> IFOVs over central Pacific Ocean in March 2010 (black: clear, white: cloudy). From left to right: ANN cloud test, ‘NWP
cloud test’, AVHRR CFR test, over an AVHRR image composite (channels 3 and 4). The clouds show up in pink/red and the ocean surface in cyan. (For
interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1347
Fig. 5. Impact of the cloud tests on the <strong>IASI</strong> L2 SST yield and quality as compared to AATSR L2P SST.

1348
the superior skill of the ANN cloud test for clear-sky
identification (see Fig. 5d and h) over oceans.
Work is still ongoing in the area of dust detection and
on the combined use of the various cloud detection
schemes with the objective of bringing the ANN tests into
operations in 2012. The ultimate configuration will maximise
the quantity and the quality of the final <strong>IASI</strong> L2
retrievals available.
2.3. Cloud characterisation
If a cloud is detected in an <strong>IASI</strong> field of view, its
characterisation in terms of cloud microphysics, fractional
coverage and cloud top pressure (CTP) is performed. In
previous versions of the <strong>IASI</strong> L2 PPF, the cloud fraction and
cloud top height determined with AVHRR were combined
with the retrievals made from <strong>IASI</strong> measurements with the
CO2-slicing method (see Section 2.3.2 below). Because of
their different characteristics this was modified and only
the CO2-slicing retrievals are retained in the L2 products
from version 5.0. Assessment of cloudiness within each
AVHRR pixel is indeed binary: either the pixel is clear or it
is cloudy, regardless of the fractional cover within the pixel
and of the cloud optical depth, whereas the CO2-slicing
returns the effective cloud amount (ECA). The latter takes
into account the cloud transparency and is more consistent
with the radiative transfer in the atmosphere.
2.3.1. Cloud phase retrieval
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
Fig. 6. MODIS aerosol optical depth at 550 nm for the period 19–24 March 2010, http://disc.sci.gsfc.nasa.gov/giovanni/overview/index.html [27].
2.3.1.1. Methodology and algorithm description. The <strong>IASI</strong> L2
PPF contains a cloud-phase-determination method that is
based on a published algorithm described in the MODIS
ATBD on ‘‘Cloud top properties and cloud phase, v5’’ [81],
which is in turn based on the algorithm described in [28].
We describe here modifications to and validation of the
algorithm. It was further compared against a method
described in [29].
The methodology is based on the different spectral
emissivity of water and ice clouds in the spectral region
between 8 and 12 mm. In case of clouds, the slope of the
emission changes, so that the difference DT1¼Tb(8 mm)
Tb(11 mm) in comparison to a second difference DT2¼
Tb(11 mm) Tb(12 mm) varies depending on the cloud
phase. The corresponding test until <strong>IASI</strong> L2 PPF v4 reads:
If DT1 DT2Zt1 then the cloud phase is ice,
else if t14DT1 DT2Zt2 then the cloud phase
is mixed,
else the cloud phase is liquid. The thresholds t1 and t2 as
well as the exact wavelengths within the <strong>IASI</strong> spectrum
are configurable and can be optimised on the basis of
independent knowledge.
The algorithm implemented in the <strong>IASI</strong> L2 PPF V5.0
consists of that described above, plus an additional test. A
third threshold can be set to identify ice clouds based on
the fact that super-cooled water cannot exist at temperatures
below t3¼ 40 1C; i.e. at temperatures lower than
t3 the cloud phase must be ice. This threshold must be set
using a window channel near 11 mm, which is close to the
cloud-top temperature in case of opaque clouds. In the
case of semi-transparent clouds this test will only predict
ice if the cloud-top temperature is much lower than t3,
and therefore also provides correct results.
2.3.1.2. An alternative method. Another cloud-phase test,
published by Wei et al. [29], has been implemented as a
prototype for comparison with the algorithms described
in Section 2.3.1.1. This method was optimised for the use
with AIRS data. Ice clouds are detected
if Tb(900 cm 1 )o238 K
or Tb(1231 cm 1 ) Tb(900 cm 1 )40.5 K
or Tb(1231 cm 1 ) Tb(900 cm 1 )4 0.5 K and
Tb(900 cm 1)4258 K.

Otherwise, liquid phase is assumed. No provision is
made for mixed-phase clouds.
2.3.1.3. Reference data set and validation results. For the
validation of cloud-phase determination and for the
definition of new thresholds (training), a set of 672
globally distributed co-located <strong>IASI</strong> and AVHRR data has
been compiled. The data set consists of day-time scenes
only, allowing proper identification of ice clouds in the
multi-spectral AVHRR imagery, using measurements of
the reflected solar radiation in the visible and nearinfrared
spectral regions. In particular the use of the
1.6 mm channel allows proper identification of ice clouds
due to their low reflectivity in this channel as compared
to the short-wave channels. The visual inspection of RGB
pseudo-colour images from AVHRR channels 1, 2, and 3a
allows the discrimination of ice and liquid phases. Mixed
phases are not however easily identified.
The data set composed of 672 AVHRR scenes and
associated <strong>IASI</strong> samples has been collected and visually
interpreted with respect to cloud phase. It includes 371
samples with ice clouds, 228 samples with liquid phase,
and 73 samples with mixed phase. A separation into two
subsets allows for training and validation.
Using the original coefficients implemented in <strong>IASI</strong> L2
PPF V4.3.2, 79.0% of the cloud phases are determined
correctly. The method in [29] provides 78.6% correctly
determined cloud phases. Subsequently, the algorithm
has been amended as described in Section 2.3.1.1. The
spectral sample positions as well as the thresholds have
been modified to enhance the performance. The final
version of the tuned algorithm detects 84.5% of the cloud
phases correctly. Among the ice samples 97.3% are correctly
determined, while of the liquid samples 84.6% are
determined correctly. The algorithm does not have any
skill in detecting mixed-phase clouds, only 5.5% are
correctly identified, the majority of mixed-phase clouds
are reproduced as ice clouds. The reason for this is the
uncertain visual determination of mixed-phase clouds. In
practice, however, mixed phase clouds will always be
detected as either ice or liquid clouds depending on the
relative proportions.
The algorithm described in Section 2.3.1.1 requires the
following brightness temperatures and is configured with
the following thresholds:
DT1 ¼ Tbð1209:75cm 1 Þ2Tbð900:25cm 1 Þ
DT2 ¼ Tbð900:25cm 1 Þ2Tbð829:00cm 1 Þ
t1 ¼ 0:9K
t2 ¼ 1:15K
t3 ¼ 233:15K:
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1349
2.3.2. The CO 2-slicing method
The cloud top pressure (CTP), and subsequently the
cloud top temperature (CTT) obtained using a reference
temperature profile, together with the cloud fraction
(CFR) are additional parameters provided in the <strong>IASI</strong> level
2 cloud products. They are currently retrieved with the
CO2-slicing method in the operational processor. Validation
results are presented and discussed here.
The CO2-slicing method established by Menzel et al.
[30] and Smith and Frey [31] for high resolution interferometer
sounders was adapted to <strong>IASI</strong> after Arriaga, 2007
(EUMETSAT Technical Report: ‘‘CO2 Slicing Algorithm for
the <strong>IASI</strong> L2 Product Processing Facility’’, EUM/MET/REP/07/
0305). It was implemented in the first version of the <strong>IASI</strong> L2
PPF to routinely retrieve the cloud top height and equivalent
cloud amount within a <strong>IASI</strong> field of view. In this
implementation, the observations in CO 2-channels sensitive
to different pressure levels are associated with their
clear-sky synthetic radiances counterparts. The forward
computation is performed with RTTOV using as input the
atmospheric profile forecast from ECMWF. A reference
channel (currently at 796.75 cm 1 ) and 41 channels
selected in the CO 2-band between 707.5 and 756 cm 1 ,
configure the cloud-top retrieval. The equivalent cloud
amount is estimated using a window channel
(900.50 cm 1 ) and the retrieved CTP. When a low cloud
is suspected, the collocated forecast model temperature
profile is inspected for a potential inversion. If a temperature
inversion is found, the cloud top pressure is placed
below the base of the inversion. Retrievals are attempted
only if the contrast between observed and simulated clear
radiances is larger than the instrument noise. In addition,
the algorithm also returns a quality indicator flag and
retrievals with ECA lower than 10% are discarded as they
are usually associated with too large uncertainties.
2.3.2.1. Validation of the cloud fraction. The use of a simple
digital camera equipped with a fish-eye lens in support of
cloud detection and monitoring has developed in recent
years [32]. The validation of the retrieved cloud fraction as
compared to such whole sky imagery was performed in
2008 during the dedicated EPS validation campaign which
took place from June to September 2007 at the meteorological
station of Lindenberg, Germany. A Daylight VIS/NIR
Whole Sky Imager (WSI) manufactured at the University of
California San Diego has been operating in Lindenberg
since 2003. The instrument acquires images of the upper
hemisphere with a viewing angle of 1801 in different
spectral regions at time steps of 5–10 min between
sunrise and sunset. Calibrated and corrected basic
image data are stored. Automatic post processing
algorithms provide cloud cover and cloud distribution
of both optically thin and thick clouds. More details on
the instrument and on the results of measurements
including comparisons of WSI data with conventional
cloud observations are given by Feister and Shields [33].
Ground-based measurements within 10 min of the
overpass time were matched to the <strong>IASI</strong> IFOVs within 11
of Lindenberg. The associated cloud fractions are shown in
Fig. 7, where only scenes with one cloud layer are shown.
Horizontal error bars show the variability of the cloud
fraction within 10 min of the overpass time while the
vertical error bars show the spatial variability (within
11 of the Lindenberg site) as seen by <strong>IASI</strong> with the
CO2-slicing algorithm. The area encompassed by the WSI
does not exactly coincide in size and location with the
collocated <strong>IASI</strong> foot-prints, as illustrated by Fig. 8, which

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T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
Fig. 7. Comparison of cloud fraction as measured from the Lindenberg
Whole Sky Imager (WSI) and the one from the CO 2 slicing algorithm in
the <strong>IASI</strong> L2 PPF.
Fig. 8. Convective cells near Lindcolourenberg (red square) at overpass
20070620190941. The cloud cover from surface observations is 87%. The
retrieved cloud fraction within <strong>IASI</strong> IFOVs (ellipses) is overlayed on the
AVHRR 10.8 mm image. (For interpretation of the references to colour in
this figure legend, the reader is referred to the web version of this
article.)
explains the size of the errors bars and prevents a full
quantitative validation. The inter-comparison shows a
good general agreement between the two products and
visual inspections of the retrieved cloud coverage with
AVHRR images confirmed the quality of the <strong>IASI</strong> L2 ECA
(see an example in Fig. 9). The retrieved equivalent cloud
amount was recently validated against the visual cloudiness
classification in the IAVISA database (presented in
Section 2.2.4). It confirmed the general good quality of the
CO 2-slicing results but also demonstrated the limitations
of the method for low-contrast scenes, i.e. when the cloud
fraction is smaller than 20% and/or when the cloud top
temperature is too close to the surface temperature, as
Fig. 9. Retrieved cloud fraction within <strong>IASI</strong> IFOVs over a cold front in the
North-West Atlantic imaged at 0.6 mm with AVHRR.
happens with low-level clouds. For these configurations, a
complementary method has been developed for the <strong>IASI</strong>
L2 PPF with the ‘w 2 method’ (see Section 2.3.3 below).
Furthermore, the current implementation of the CO 2slicing
method cannot cope with multi-layer clouds
within the <strong>IASI</strong> field of view and the current developments
aim to use the AVHRR cloud information based on
the cluster analysis for these situations.
2.3.2.2. Validation of the cloud top pressure with Lindenberg
cloud radar. The retrieved cloud top pressure was also
validated in 2008 against ground-based cloud-radar
operated at the meteorological station of Lindenberg,
Germany, during an atmospheric sounding campaign
from June to September 2007 in support of the
validation of Metop products. It is acknowledged that
this validation work is so far limited to this specific site.
However, the campaign involved a large variety of cloud
types reported by an expert observer (Ac, Cb, Cc, Cb calvus,
Cg, Cs, Cu, Ci, Sc, St), alone or in combination, and a large
range of cloud elevations between 950 and 200 hPa. The
full analyses and validation results of the retrieved cloud
fraction and cloud top pressure are available in the
validation report by Arriaga et al. ‘‘EPS <strong>IASI</strong> L2 PPF
Validation Report: Cloud Top Pressure and Effective
Cloud Amount’’ (EUM/MET/REP/09/0698).
Cloud radars measure profiles of the intensity of
particle-backscattered signals and their Doppler shift to
derive information about particle size, concentration and
motion. The cloud radar used in this study is a vertically
pointing Doppler radar measuring at 35.5 GHz, manufactured
by Metek GmbH, Elmshorn, Germany, and is operated
continuously. The measurements are 10-s averaged
cloud reflectivity records, sampled every minute. Local
atmospheric soundings support the conversion of the
cloud top (base) height to pressure levels. The use of
the cloud radar at Lindenberg and the limitations of the
ground-based cloud detection and characterisation are
discussed by Hennemuth et al. [34]. In particular, some
optically relevant clouds with too small particles, such as
cumulus humilis and high cirrus, may fall below the radar
detection threshold.

During the validation campaign, a total of 161 overpasses
with valid <strong>IASI</strong> measurements and cloud product
retrievals were available within 50 km of the Lindenberg
meteorological station. For the reasons cited above, no
radar cloud measurements were available in 64 cases. 15
cases were further excluded because <strong>IASI</strong> and the groundbased
radar were sensing different cloud structures in
different layers, due to large scale cloud heterogeneities
(full details on the cloud heterogeneity evaluation and
handling in the validation report by Arriaga, 2009). A total
of 82 match-ups were retained for validation of the CTP
and are presented in Fig. 10. The correlation is high with a
global bias of 29.4 hPa (<strong>IASI</strong> products higher) and standard
deviation of 49.2 hPa. These statistics vary with the
cloud elevation and the details are presented in Table 4.
2.3.2.3. Inter-comparison with CALIOP cloud top pressure.
More recently, a validation of the cloud height assignment
from <strong>IASI</strong> dedicated to the Polar Regions was initiated in
order to support the development of the EPS Day-2 Polar
wind product. The <strong>IASI</strong> products were inter-compared
to the retrievals from the Cloud–Aerosol Lidar with
Fig. 10. <strong>IASI</strong> retrieved vs. ground-based radar measured cloud-top
pressure (hPa) over Lindenberg during the EPS Validation Campaign
from June to September 2007.
Table 4
Bias and standard deviation of the retrieved cloud top pressure (hPa).
CTP (hPa) Retrieved cloud top pressure
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1351
Radar Nb cases Bias Std
Above 400 37 þ57.1 46.6
400–700 26 þ19.1 37.4
Below 700 19 10.5 32.7
Orthogonal Polarisation (CALIOP) instrument. CALIOP
was launched on-board the Cloud–Aerosol Lidar and
Infrared Pathfinder Satellite Observations (CALIPSO)
satellite [35] in April 2006 as part of the NASA A-Train.
It is the first space-borne Lidar to provide long-term
atmospheric measurements, with unique cloud profiling
capabilities. CALIOP provides high resolution vertical
profiles of clouds and aerosols with a resolution of 30 m
from ground to 8 km and of 60 m between 8 and 20 km
[36]. The cloud products v3 at 1-km horizontal resolution
[37] are used in this study. They are available at NASA’s
on-line Earth Science Discovery Tool http://reverb.echo.
nasa.gov/ (last accessed 19/01/2012). Simultaneous
observations by <strong>IASI</strong> and CALIOP only occur at Polar
latitudes, due to the orbits of their respective spacecrafts:
09h30 descending node for Metop and 13h30 ascending
node for CALIPSO. Collocations in the period September to
December 2010 with a maximum time difference of
10 min and a maximum distance of 10 km between the
CALIOP pixel and the <strong>IASI</strong> IFOV centre are selected.
Additionally, only homogeneous single layer clouds, as
defined by CALIOP, are retained for comparison; i.e.
where the CTPs measured by CALIOP do not vary by
more than 50 hPa standard deviation within the <strong>IASI</strong> field
of view. A total of 4568 match-ups are considered which
are displayed in the scatter plot in Fig. 11. The colour
coding refers to the retrieved <strong>IASI</strong> L2 ECA. The few outliers
seen in the graph, with <strong>IASI</strong> CTP around 100 hPa, are
actually artefacts due to an algorithm initialisation issue,
which does not affect the other CTP retrievals. It was
corrected with revision 5.2 which became operational in
October 2011. The discrepancies between the two satellite
cloud height assignments are larger for the IFOVs with an
ECAs of 25% and below. For ECAs larger than 30% the
correlation is high (r 0.9). Two modes are observed,
spanning the lower and higher halves of the troposphere.
Between 1000 and 550 hPa CTPs from <strong>IASI</strong> show a small
bias of 15 hPa (<strong>IASI</strong> lower) and a standard deviation of
approximately 90 hPa. The dispersion is smaller (standard
deviation of around 60 hPa), with however a larger bias
(110 hPa, <strong>IASI</strong> higher) for clouds located above 550 hPa.
One should recall that the CO 2-slicing with IR measurements
characterises the effective radiative height of a
cloud while the Lidar locates a physical cloud boundary.
The two may differ depending on the cloud opacity and
this accounts for the biases observed between the two
cloud height assignments. Similar offsets were recently
reported by Kim et al. [38] comparing the cloud top heights
derived from CALIOP and those retrieved in the IR from
MODIS with the CO 2-slicing method. Inaccuracies in the
forecast atmospheric profiles supporting the CO2-slicing
retrieval are also source of errors [39].
2.3.3. The w 2 -method
This method consists of modelling the differences
between measured and simulated clear radiances with a
mono-layer cloud for channels selected in CO2 lines. For
implementation and theoretical performances see
EUMETSAT technical note ‘‘Assessment of the chi-square
method for cloud top pressure and equivalent cloud
amount retrievals with measurements from <strong>IASI</strong>’’ (EUM/

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<strong>IASI</strong> top cloud pressure [hPa]
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
100
200
300
400
500
600
700
800
900
1000
1000
<strong>IASI</strong> vs CALIOP TCP depending on the <strong>IASI</strong> CO2−slicing ECA
(scatter_caliop_iasi_eca, stdev

T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1353
– the skin surface temperature (for ocean and continental
surfaces)
– the surface emissivity at twelve configurable channels
– a coarse O3, profile in thick partial columns (0–6 km,
0–12 km, 0–16 km)
– the total column of CO, O3, CH4, N2O and CO2.
A brief overview of the retrieval algorithms is given
here while validation methodology and results of the
respective parameters is summarised in Sections 2.5–2.7.
2.4.1. The statistical retrievals
The retrieval of the full state vector listed above is only
attempted for cloud-free pixels. In case of a partial cloud
contamination, the geophysical retrieval is achieved with
EOF regression and limited to temperature and humidity.
The reader is referred to the work by Zhou [19] for the
complete description of the algorithm in cloudy conditions
and its performance. In clear-sky, a collection of
statistical methods is applied first, which consists of EOF
linear and ANN non-linear regressions. In the EOF
method, the principal component scores of the <strong>IASI</strong>
radiances are computed and are input to the linear
regression for determination of the sea surface temperature
(SST) [42], the ozone, temperature and humidity
profiles [19] as well as the land surface temperature and
emissivity [9].
Artificial neural networks are used to retrieve the total
column amount of CO, CH4, N2O and CO2. The algorithm
was adapted from the work of Hadji-Lazaro et al. [43] and
Turquety et al. [44] to account for the instrument viewing
angle, variable surface emissivity and interfering atmospheric
species. The ANNs ingest radiances in selected and
configurable absorption and baseline channels as well as a
coarse atmospheric temperature profile, the surface pressure
and the satellite zenith angle. Improvements were
sought also in the preparation of normalised and centred
inputs to optimise the learning skills and speed [23]. The
description of the updated algorithm which became
operational with the <strong>IASI</strong> L2 PPF version 5 on 14/09/
2010 is available on-line at: www.eumetsat.int (August T.,
2010, ‘‘An Improved Artificial Neural Network CO Retrieval
for <strong>IASI</strong> L2 Processor’’, EUM/MET/TEN/09/0232).
The geophysical parameters retrieved with the statistical
methods may constitute the final L2 product or serve
as a first guess for the optimal estimation retrieval
(Section 2.4.2). The choice is configurable individually
for each parameter and is also a function of the quality
of the respective statistic and iterative retrievals.
2.4.2. The retrieval using optimal estimation
The OEM is the final retrieval module in the <strong>IASI</strong> L2
processing chain and implements the standard optimal
estimation after Rodgers [45]. The core algorithm in the
PPF version 5 is similar to the algorithm in former
revisions [5]. The configuration and the state-vector
composition have changed in the PPF version 5.0; they
are presented here.
2.4.2.1. Atmospheric state vector and cost function
minimisation. In addition to the surface temperature (Ts)
and to the temperature (T) and humidity (q) profiles
which were simultaneously retrieved already in the
previous versions, the ozone profile (O3) has become an
active state vector parameter and is now simultaneously
retrieved within the OEM. The basis of the retrieval is the
minimisation of a cost function of the classical form:
j ¼ðx xaÞ T UB 1 Uðx xaÞþðFðxÞ yÞ T UR 1 UðFðxÞ yÞ ð2Þ
The first term of the sum represents a constraint on the
atmospheric state vector to be retrieved, x, in terms of an
a priori vector xa and its covariance matrix B. The second
term describes the constraint generated by weighting the
departure of the measurements (y) from simulated
radiances computed with the forward model F(x), taking
the retrieved state vector x as input, by the instrument
noise covariance matrix R. The state vector x is modified
in successive iterations in order to minimise J using the
Levenberg–Marquardt method. The minimisation is performed
in brightness temperature space and the convergence
criterion is a threshold on the norm of the gradient
of the cost function. For operational performance and
timeliness reasons, a maximum of 5 iterations is allowed
and the state vector x is accepted and stored into the final
L2 product if the residual F(x) y is smaller than a
configurable threshold.
Within the cost function, the vertical profiles to be
retrieved used to be expressed on a pressure grid in the
previous PPF versions. T, q and O3 are now represented by
their principal components in the EOF space of the atmospheric
profiles. In this approach 28, 18 and 9 PCs were
retained to represent the temperature (in K), the humidity
and ozone (both in ln(ppmv)) profiles, respectively. More
details on the algorithm description can be consulted online
at www.eumetsat.int in the ‘‘EPS <strong>IASI</strong> L2 Product
Generation Specification’’ (EUM/OPS-EPS/SPE/08/0199).
2.4.2.2. Radiative transfer model, channel selection, bias
correction and observation error covariance matrix. The
function F in (2) is the radiative transfer model RTTOV-
10 (presented in Section 2.2.1), in use since October 2011
with the introduction of the <strong>IASI</strong> L2 PPF v5.2 (RT<strong>IASI</strong>-4
was used in prior versions). The channel selection was
modified and 316 channels from the list established by
Collard [46] are used in the PPF v5 whereas the retrievals
were based on 222 channels in previous releases.
In the second term of the cost function (2), the
measurements are fitted with simulated radiances. It is
therefore essential to account beforehand for any systematic
differences between the measurements and the
forward computations, which may originate in instrument
errors or in inaccuracies in the radiative transfer
model. Global biases have been calculated from a set of
pairs of radiance vectors, one observed by <strong>IASI</strong> (referred to
as OBS), the other calculated from some measure of truth
(called CALC hereafter). The (OBS CALC) statistics were
computed on clear sky ocean FOVs, using collocated
ECMWF analysis profiles for the atmospheric state vector
and RTTOV as a forward model. The systematic departures
evaluated this way are used to tune the <strong>IASI</strong> measurements
before ingestion in this retrieval module.

1354
The measurement error covariance matrix R in (2) was
computed from these clear sky (OBS CALC) differences.
Thus, besides the instrument noise (reduced with the
noise filtering, see Section 2.2.2 above), it also contains
contributions from the model error. The full measurement
error covariance matrix is now exploited. This also allows
the correlations introduced by the noise filtering and the
correlations due to model errors to be captured.
2.4.2.3. Background a priori and covariance matrix, first
guess. Until version 4.3, the <strong>IASI</strong> L2 PPFs were
configured with a unique covariance matrix and nine
regional latitude-dependent a priori atmospheric state
vectors which were based on the climatology established
by Chevallier in 2001 from ECMWF analysis fields
(in Chevallier F., 2001. ‘‘Sampled Database of 60 Levels
Atmospheric Profiles from the ECMWF Analysis’’. Technical
Report, ECMWF EUMETSAT SAF programme Research
Report 4; ECMWF). In version 5.0, a global aprioriis used
with a unique covariance matrix, computed from a
collection of ECMWF analysis records covering all seasons.
The choice of a unique background rather than the coarse
latitude stratification was made to avoid geographical
discontinuities. With this configuration, regional variations
can also be explained by the measurements rather than by
the varying a priori.
The atmospheric parameters to be retrieved are initialised
with the statistical retrievals, discussed in Section
2.4.1 above. The geophysical state vector has also a static
part, i.e. one that is not modified in successive iterations.
For instance, the profiles of CO2,CO,N2O and CH4 are set to
the default trace gas profiles used in RTTOV-10 and kept
fixed during the iterations. Surface pressure and wind
vectors are obtained from ECMWF forecasts and the surface
emissivity is either computed following Masuda [15]
over oceans or taken from a monthly emissivity climatology
database over land (see Section 2.1).
2.5. The surface products
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
2.5.1. Land surface temperature
The land surface temperature (LST) and land surface
emissivity (LSE) entering the <strong>IASI</strong> L2 are currently taken
from the statistical retrievals (Section 2.4.1). As part of the
<strong>IASI</strong> L2 validation activities, the errors in the LST products
were assessed by inter-comparison with the LST products
generated at the Land Surface Analysis (LSA) SAF from the
Spinning Enhanced Visible and InfraRed Imager (SEVIRI)
measurements acquired from the Meteosat Second Generation
(MSG) satellites, available in the EUMETSAT Earth
Observation data portal (https://eoportal.eumetsat.int). This
validation study was therefore limited to the portion of the
Earth visible from the MSG geostationary orbit, namely
mainly Africa, Europe and the Eastern part of South America.
The short periodicity of the LSA LST products, repeated
every 15 min., however allowed very close temporal coincidences
between the two products which is essential as the
surface skin temperature diurnal variations can be as high
as 6 1C/hforsomesoiltypesindeserts[47]. SEVIRIdata
products from nominal mode having a spatial resolution of
approximately 3 km at the sub-satellite point were used.
The LSA-SAF product retrieved from SEVIRI measurements
is based on a generalised split-window algorithm
using two adjacent channels—IR10.8 and IR12.0 mm. It
was validated against MODIS LST products and in-situ LST
retrievals [48]. At night time, biases (standard deviation)
of around þ1.75 K (1.5 K) were found against the former
(LSA minus MODIS) and of 1.7 K (2 K) against the latter
(LSA products being colder than in-situ retrievals).
Each LSA LST comes with a quality flag indicating the
degree of confidence and the error associated with the
retrieval. For this study, we only retained the products with
‘‘above nominal’’ and ‘‘nominal’’ quality for both the
retrieved LST and surface emissivity. Departures (LSA SAF
minus <strong>IASI</strong>) were computed for each match-up where at
least four good LSA LST retrievals were found within the <strong>IASI</strong>
field of view. The matching SEVIRI points were averaged and
used only if their standard deviations remained lower than
5 K to avoid highly heterogeneous scenes. The inter-comparisons
were performed for day- and night-times separately
to discriminate the effect of the different Metop/
MSG–Sun–Surface geometry. At night-time the absence of
solar illumination allows a direct comparison of the LST
retrieved or modelled from different instruments. During
daytime, the comparison is affected by the different Sun–
surface–instrument geometries, as a result of shadows due
to orography or vegetation for example. For a given place
and time, the better the alignment between the instrument,
the Sun and the scene, the smaller is the observed shadow
fraction and the warmer the sensed LST. This effect is visible
in Fig. 13, where the LST–<strong>IASI</strong> mean difference varies by 2 K
with scan angle in daytime and remains constant to better
than 0.5 K at night time. The statistics are computed for the
overall SEVIRI domain with the exclusion of the Sahara,
where the PPF LST retrievals during daytime in particular
exhibit large variances. The African Sahara and the Arabian
Peninsula were then isolated and the statistics specifically
repeated for these particular soil types.
Fig. 14 illustrates the average and standard deviation of
(LSA–<strong>IASI</strong>) LST between the 19 and 24 March 2010 at night
time. The distributions of the departures during day and
night times, displayed in Fig. 15, are essentially Gaussians
(red shape), with the exception of few outliers. The correlation
between the two products is of 0.98 over non-desert
places and of 0.7 for the Sahara. The best agreements are
achieved at night, where the rms errors amount to about 2 K
with usual surfaces. Larger differences were found in
elevated regions and for bare arid soils in the Sahara and
the Arabic Peninsula with a particular mineralogy, especially
in the Rub’ Al Khali subregion. In addition, global comparisons
against the ECMWF LST analyses for the same period
were performed. The results are summarised in Table 5. Itis
referred to the dedicated validation report (August, 2010,
‘‘<strong>IASI</strong> L2 Surface Temperature: PPF v5 Validation Results’’,
EUM/MET/TEN/10/0188) for a more detailed analysis of the
inter-comparison between the <strong>IASI</strong> L2 LST and LSA LST
products as well as with the ECMWF analysis field.
2.5.2. Land surface emissivity
The land surface emissivity product is distributed in a
pre-operational mode in 12 sampled channels. It has so
far mainly been compared to the Global Infrared Land

T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1355
Fig. 13. LST (LSA–<strong>IASI</strong>) departures distribution vs. <strong>IASI</strong> scan position
(Nadir at step 15) in the 19–24 March 2010 period at night (top) and day
(bottom) time.
Surface Emissivity database [49] (see EUMETSAT report,
Hultberg, 2010, ‘‘Surface Emissivity within <strong>IASI</strong> L2 PPF
v5’’, EUM/OPS-EPS/TEN/10/0203). Validation studies are
ongoing to characterise it further. In future <strong>IASI</strong> L2
processor versions, the OEM will ingest these retrievals
as an input instead of the emissivity monthly climatology
introduced in Section 2.1. Retrieval of the land surface
emissivity simultaneously with the temperature and
humidity profiles is also planned, with a positive impact
expected in the lower troposphere.
2.5.3. Sea surface temperature
The sea surface temperature selected for the <strong>IASI</strong> L2
products is currently based on the EOF retrieval, where
PCs of the first two <strong>IASI</strong> bands are used for the linear
regression (EOF in Section 2.4.1). The validation of this
product involved inter-comparisons with other spaceborne
sensors and to in-situ measurements from drifting
buoys. They are summarised here.
2.5.3.1. Comparison to AATSR SST products. In an initial
assessment phase, the SST retrievals from the Advanced
Along-Track Scanning Radiometer (AATSR) were used as
reference products. The reader is referred to Llewellyn-
Jones et al., 2001 ‘‘AATSR: Global-change and surfacetemperature
measurements from ENVISAT’’ in the
European Space Agency (ESA) Bulettin 105, pp. 10–21
(available online at: http://www.esa.int/esapub/bulletin/
bullet105/bul105_1.pdf, last accessed 09/02/2012) for the
description of the instrument and of the mission
objectives. As with the <strong>IASI</strong> L2 PPF, the AATSR processor
is designed to retrieve the surface skin temperature. The
reference products in this study are the L2 ATS_NR_2P
generated in the frame of the Group for High-Resolution
SST (GHRSST) [50] (www.ghrsst.org, last accessed 23/01/
2012). They have a horizontal resolution of 1 km and were
validated against in-situ measurements (buoys and ships)
and airborne radiometers [26]. The retrieval algorithm
includes atmospheric corrections covering multi-spectral
and dual-angle view capability. The L1 measurements
underwent dedicated calibrations [51]. Illingworth et al.
[52] evaluated the <strong>IASI</strong> and AATSR radiance intercalibration
at 11 and 12 mm and concluded that the
brightness temperatures agreed within 0.3 K, with even
smaller differences of the order 0.1 K at 11 mm. The AATSR
SST products are characterised by a slight warm bias of
0.05 K at night (0.1 K for daytime) and by a typical
standard deviation of 0.25 K during night-time (0.35 K
during day) [26,53].
The inter-comparison was performed on the same 6day
period (19–24 March 2010) used for the LST. As for
the collocation of AATSR pixels to <strong>IASI</strong> IFOVs, only the
clear cases as identified in the <strong>IASI</strong> processing chain were
retained where at least 200 good AATSR pixels (according
to the L2 SST quality flags) could be found in a radius of
15 km around the <strong>IASI</strong> IFOV centre. Additionally, the
match-ups were rejected if the standard deviation of
AATSR SST over the <strong>IASI</strong> footprint exceeded 0.4 K in order
to analyse homogeneous scenes only and limit the impact
of the <strong>IASI</strong> point spread function in the inter-comparisons.
A total of approximately 60,000 match-ups were finally
considered in this study.
The <strong>IASI</strong> L2 SST has a global cold bias of 0.44 K and
a standard deviation of approximately 0.40 K. As seen
in Fig. 5, the distributions of AATSR–<strong>IASI</strong> SST have a
Gaussian main mode, containing at least 90% of the
samples and peaking at þ0.27 K. The standard deviation
of the fitted Gaussian is also smaller and drops down to
0.28 K at night-time, when AATSR products are expected
to be the most accurate. A small fraction (5–10%) of the
retrievals lies outside this main mode. The associated
departures are typically of 1–1.5 K (<strong>IASI</strong> colder) but can on
rare occasions be as high as a few Kelvin. This asymmetric
tail is mostly attributed to undetected clouds and aerosols
(dust clouds) contamination as discussed in Section 2.2.5,

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T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
Fig. 14. LSA–<strong>IASI</strong> LST mean (top) and standard deviation (bottom) in the 19–24 March 2010 period.
whichartificiallycoolstheretrievedSST.Thiseffectisshown
in Fig. 16, where the differences (AATSR <strong>IASI</strong>) in SST in the
Arabian Sea on 19/03/2010 correlate with higher dust load.
The (AATSR–<strong>IASI</strong>) SST statistics computed outside the geographic
areas with high aerosol optical depths, as identified
with MODIS products, have a mean of approximately þ0.3 K
and a standard deviation of the same order. The current
operational implementation does not however include such
an aerosol filtering. These figures, although still theoretical as
long as aerosols are not effectively detected, can be extrapolated
to the sea areas which are climatologically clean of
dust. Similar comparisons were repeated with ECMWF
analyses fields, which confirmed the slight cold bias in <strong>IASI</strong>
SST and the good precision assessed with AATSR. The
departures from AATSR and ECMWF SST were assessed as a
function of viewing geometry, which revealed a slight
angular variation of approximately 0.3 K amplitude in the
<strong>IASI</strong> SST bias from Nadir to the swath edge.
2.5.3.2. Single sensor error estimate and quality levels. To
promote the use of combined multiple SST datasets the SST
community has agreed on a specification for all global
satellite-derived level-2 SST products within the GHRSST
[50] (www.ghrsst.org, last accessed 23/01/2012). Highresolution
products are provided to the operational
oceanographic, meteorological and climate community on
a daily basis in a common netCDF format. The GHRSST
level-2 format is identified as ‘L2P’ and contains
observational error estimates called Single Sensor Error
Statistics (SSES). The good performances described in the
previous section motivated the creation of a demonstration
<strong>IASI</strong> SST L2P product as a contribution to the GHRSST, for
which trial-dissemination started on 24/03/2011. Six levels
of quality are defined for the <strong>IASI</strong> L2P SST from 0 to 5 with
2 being the first usable quality and 5, the best quality.
The <strong>IASI</strong> SST error has been further estimated by
comparison to other satellite SST products, namely from

T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1357
Fig. 15. LST (LSA–<strong>IASI</strong>) distributions at day (left) and night (right) times, for non-arid areas (top row) and Sahara (bottom row) in the 19–24 March 2010
period.
Table 5
Summary of the respective (LSA,ECMWF–<strong>IASI</strong>) LST departures for the 19–24 March 2010 period: bias, standard deviation and correlation coefficient. In
parentheses, the statistics of the Gaussian fitting the main mode.
the instruments AVHRR/Metop and AATSR, as well as with
drifting buoy in-situ measurements, in order to establish
SSES for <strong>IASI</strong>. A summary of the validation approach and
of the results is presented here (they are more exhaustively
discussed in EUMETSAT validation report from
O’Carroll A., 2010, ‘‘Validation of <strong>IASI</strong> L2Pcore Sea Surface
Temperature’’, EUM/MET/DOC/10/0472).
Day Night
Bias s r Bias s r
LSA–<strong>IASI</strong> No Sahara 0.6 ( 0.7) K 2.4 (1.8) K 0.97 1.6 ( 1.2) K 1.6 (1.0) K 0.99
Sahara 1.7 ( 1.3) K 4.6 (3.3) K 0.75 4.4 ( 2.9) K 5.6 (3.0) K 0.65
ECMWF–<strong>IASI</strong> No mountains, no Poles, no Sahara 4.0 ( 3.2) K 3.6 (3.1) K 0.97 1.9 ( 0.9) K 2.9 (1.7) K 0.99
Sahara 5.7 ( 5.4) K 5.7 (5.2) K 0.66 3.5 ( 2.3) K 5.0 (2.6) K 0.73
Poles (Antarctica, Arctic) 2.9 ( 1.8) K 3.3 (2.5) K 0.92 3.6 ( 1.1) K 6.9 (2.0) K 0.83
The <strong>IASI</strong> SSTs have been routinely compared to the
EUMETSAT Ocean and Sea-Ice Satellite Application Facility
(OSI-SAF) AVHRR/in situ Matchup Dataset (MDB) (Le
Borgne et al., ‘‘Operational SST retrieval from METOP/
AVHRR validation report’’, Ocean and Sea-Ice SAF CDOP
report, Version 2.0, July 2008) to provide a multi-matchup
dataset (MMD) of collocated <strong>IASI</strong>, AVHRR and drifting

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T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
Fig. 16. Clear-sky (AATSR–<strong>IASI</strong>) SST plotted over an AVHRR image (visible channel) of the Arabian Sea on 19 March 2010. Land and clouds appear in
saturated white, dust loads colourlight grey.
buoy SSTs. This MMD was used to determine the biases
and errors of the <strong>IASI</strong> SSTs. In order to enable direct
comparison with buoy sub-skin SSTs, 0.17 K are added to
the <strong>IASI</strong> skin SSTs after Donlon et al. [54], <strong>IASI</strong> L2
providing the skin surface temperature. Quality control
of the buoy data requires the buoy SSTs to be within 5 K of
climatology and a buoy blacklist supplied by the Ocean
and Sea-Ice SAF (OSI-SAF) is used in addition. As for the
AVHRR products, the confidence flags are used to select
good quality retrievals (QC flag 42) and match-ups are
considered if the AVHRR SST over an <strong>IASI</strong> footprint is less
than 0.3 K. Additionally, along the GHRSST guidelines, the
<strong>IASI</strong> and in situ SST time difference must be within 2 h;
only drifting buoys are used and only night-time <strong>IASI</strong>
observations over sea are used in order to reduce diurnal
variations.
As the retrieval method does not provide an associated
error estimate and a major error contribution to SST
retrievals is the amount of water vapour in the atmosphere
[55], climatological SSTs are used to define <strong>IASI</strong>
SST quality levels by stratification against the integrated
water vapour (IWV), in order to define the SSES thresholds
and criteria. The <strong>IASI</strong> water vapour profiles contained
in the L2 product are taken as input. AVHRR, buoys and
<strong>IASI</strong> match-up databases are routinely constructed and
the characterisation of the errors in the <strong>IASI</strong> SSTs, stratified
against the IWV is updated at EUMETSAT on a 6month
basis to complete the information in the <strong>IASI</strong> SST
L2Pcore product. With the introduction of the <strong>IASI</strong> L2 PPF
v5, the bias in the <strong>IASI</strong> SSTs compared to drifting buoy
SSTs, assessed between October 2010 and March 2011,
gave a value of 0.3 K (<strong>IASI</strong> colder) with a standard
deviation of 0.3 K. Comparisons against AVHRR SSTs gave
a <strong>IASI</strong> SST bias of 0.33 K (<strong>IASI</strong> colder) and standard
deviation of 0.28 K, over the same period. In addition,
given three different observation sources, it is possible to
estimate the overall standard deviation of error of the
observation type [56], assuming that the errors between
the observations are uncorrelated. The global standard
deviation of errors using this method, over the period
October 2010 to March 2011, are 0.26 K (<strong>IASI</strong>), 0.14 K
(AVHRR), and 0.19 K (drifting buoys).
Future developments for this product will address the
slight angular dependency and the inclusion of the band 3
(shorter wavelengths) in the retrieval at night time.
Another aspect where improvements are anticipated is
in the detection of dust layers with <strong>IASI</strong> for SST product
quality flagging and possible correction.
2.6. Temperature and humidity profiles
2.6.1. Results of Lindenberg validation campaign
To the first order, the validation of remotely-sensed
atmospheric profiles can consist of a level-to-level comparison
with some independent representation of the true
atmospheric state. The reference data can be for instance
acquired in-situ by airborne, radio-sonde or drop-sonde
systems. This is the approach followed in the <strong>IASI</strong> product
assessment part of the ESA GlobVapour project, which
aims at generating validated multi-annual global water
vapour datasets with error estimates. (www.globvapour.
info, last accessed 23/01/2012). One limitation is that the
retrieval and the reference system do not give a representation
of the true state with the same vertical resolution.
In fact, the OEM retrieval scheme dictates that the
final retrieved state vector is a combination of the true
state vector and of the a priori that constrains Eq. (2).
The a priori enters the retrieval for the portions of the

atmosphere where the measurements do not carry
enough information or are not given enough weight e.g.
too large measurement noise in R in (2). Rodgers [45]
showed that from (2) derives:
^x ¼ Axv þðI AÞxa þe ð3Þ
where ^x is the retrieved vector, entering the L2 product, x v
is the true atmospheric state and xa the a priori profile. A
is a matrix called the averaging kernel, giving a measure
of the vertical resolution and vertical sensitivity of a given
parameter. I is the identity matrix and e is the retrieval
error to be assessed. The trace of A gives a measure of the
independent pieces of information retrieved from the
measurements, or degrees of freedom (DoF) for the
retrieval [45]. They are smaller than the number of levels
in the vertical grid and can vary with the atmospheric
configuration. For example, up to 14 and 10 DoFs in the
temperature and water-vapour profiles retrieved from
<strong>IASI</strong> for 2007 summer cases in Lindenberg.
Additionally, for non-uniform and dynamic atmospheres,
the reference data are a limited representation
of the true state observed by the space-borne instrument
because of unavoidable spatial and temporal non-coincidences
between the correlative products. Pougatchev et
al. [57] evaluated the errors in the <strong>IASI</strong> L2 products
version 4.3 with radio-sondes operated during a dedicated
validation campaign at Lindenberg, Germany.
Between June and September 2007, Vaisala RS92-SGP
radiosondes were launched one hour and five minutes
prior to <strong>IASI</strong> overpasses in order to sample the higher and
lower troposphere simultaneously with <strong>IASI</strong> acquisitions
(full documentation on the campaign in the final report
‘‘EUMETSAT Polar System Programme Atmospheric
Sounding Campaign’’). The study considered <strong>IASI</strong> L2 single
retrievals within 100 km of Lindenberg and evaluated the
contribution of the non-coincidences to the total error
budget. In conclusion, the expected and the assessed
errors in the operational <strong>IASI</strong> L2 are in good agreement
between 800 (700) hPa and the tropopause for temperature
(water vapour) retrievals, with typical error standard
deviation of 0.6 K between 800 and 300 hPa and increasing
up to 2 K at the surface. The bias evaluated against
radiosondes oscillates within 70.5 K. For relative humidity
(RH), the single retrieval error is below 10% RH
standard deviation between 700 and 300 hPa and up to
15% closer to the surface. The bias is within 710% RH.
Larger errors in the boundary layer were assumed in this
Table 6
Validation classes definition.
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1359
study to be partly due to incorrect surface parameters
(e.g. emissivity) and undetected clouds or haze.
2.6.2. Global assessment of the <strong>IASI</strong> L2 profiles v4 vs v5
Global error estimate are found by inter-comparisons
to ECMWF analyses fields, which we summarise here. A
detailed presentation of the validation strategy and the
analyses can be consulted from EUMETSAT validation
report ‘‘Vertical Temperature and Humidity Profiles
within <strong>IASI</strong> L2 PPF v5: Non-Regression Tests and Validation
Results’’ (EUM/MET/TEN/09/0448), available on-line
at: www.eumetsat.int. In view of the introduction of the
version 5, the <strong>IASI</strong> L2 temperature and humidity profiles
of version 4 and version 5 have been compared to NWP
analyses from ECMWF integrated forecast system (IFS) to
characterise the improvements. The reference data used
here are provided on a 0.51 0.51 geographical grid and
are represented on a varying hybrid 91 pressure level
vertical grid, defined for each point with the local surface
pressure. They are available for the synoptic times 00, 06,
12 and 18 UTC. The single <strong>IASI</strong> L2 retrievals were matched
with the closest ECMWF latitude/longitude grid point and
the corresponding NWP profile was interpolated onto the
L2 product grid.
The results obtained with two validation datasets are
reported here. The first one is composed of <strong>IASI</strong> products
from 43 orbits centred on the regular ECMWF analysis
times. As a result, the associated reprocessed L2 v4 and v5
retrievals are on average within half-an-hour of the
reference data. These 43 orbits were randomly selected
with weekly intervals to span a nine-month period running
from June 2007 to March 2008 and to equally
represent the four analysis times against which the
validation is performed. The second data set addresses
the full Earth coverage with consecutive retrievals during
the period 19–24 March 2010. A time-interpolation
was applied to the vertically re-sampled analyses prior
to computing the departures, in order to account for
potential temporal non-coincidences. They both lead to
consistent statistics and conclusions. The statistics of <strong>IASI</strong>
L2–ECMWF departures were analysed in nine geographical
and surface categories (description in Table 6). They
are reproduced here for the period 19–24 March 2010
only, in Fig. 17 (temperature) and Fig. 18 (humidity).
2.6.2.1. <strong>IASI</strong> L2 temperature vs. ECMWF analyses. The
temperature entering the final L2 products is nominally
Class Label Surface pressure (hPa) Surface type Latitude Time
1 North Pole (NP) o1050 Land and sea 4601 Day and night
2 North Sea 4900 Sea [301; 601] Day and night
3 North land 4900 Land [301; 601] Day and night
4 Elevated terrain o900 Land [ 601; 601] Day and night
5 Intertropical Sea 4900 Sea [ 301; 301] Day and night
6 Intertropical land 4900 Land [ 301; 301] Day and night
7 South Pole o1050 Land and sea o 601 Day and night
8 South Sea 4900 Sea [ 601; 301] Day and night
9 South land 4900 Land [ 601; 301] Day and night

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T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
Fig. 17. Temperature vertical profile retrievals, departures from ECMWF analyses products (Retrieved—ECMWF). <strong>IASI</strong> L2 retrievals shown in black and
red correspond respectively to the PPFs v5.0.3 and v4.3.3. Dashed lines denote the bias, while the standard deviation and rms are plotted with thin and
thick plain lines, respectively. The classes listed in Table 6 appear in order from left to right and top to bottom. (For interpretation of the references to
colour in this figure legend, the reader is referred to the web version of this article.)
selected from the OEM described in Section 2.4.2. The
agreement between the temperature retrieval and the
reference varies with the nature of the scene, the best
matches occurring over ocean surfaces; outsides the
Polar regions. The (<strong>IASI</strong>–ECMWF) departures increase in
the boundary layer and are usually smaller for PPF v5
than for PPF v4. The rms differences are typically 0.7–1 K
(1–1.5 K) between 200 and 800 hPa, and increase up to
2 K (2.5 K) at lower atmospheric levels for PPF v5 (PPF
v4). Over the continents, the PPF v5 still shows the
same good statistics in the mid and upper troposphere
while PPF v4 errors are larger by 1.5 K. Below
800 hPa, however, both revisions exhibit similar errors,
ranging between 2.5 and 3.5 K on average. The same
observations apply to the North Pole and the elevated
surfaces. The highest disagreements are found over
Antarctica, with rms differences exceeding 4 K below
600 hPa, where the models are also expected to have
intrinsically larger uncertainties.
The bias oscillates vertically around 0 K for PPF v4,
with an amplitude of approximately 1 K. This behaviour
disappears with revision 5, whose products have a bias of
about þ0.5 K rather constant with pressure level for nonpolar
land and ocean surfaces between pressure levels
200 and 800 hPa. Closer to the surface however, the sign
of the average departure changes and the bias can be as
large as 3 K (for tropical land surfaces), sometimes even
larger than the PPF v4 biases over land.

T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1361
Fig. 18. Same as previous figure, for water-vapour profiles in volume mixing ratio.
These departures were stratified against the viewing
angle to assess potential angular variations of the retrieval
quality. The rms differences show an increase of 0.5 K from
the Nadir to the swath edge in the mid-troposphere for v4
products, in both the bias and the standard deviation,
which is no longer present in the v5. An angular effect
however remains in the bias closer to the surface with v5,
with amplitude of about 1 K at 940 hPa. As the ECMWF
analyses are independent of the scan angle, this effect is
attributed to the retrieval scheme. The reasons still need to
be investigated, they could be due for instance to scan
angle dependent performances in the cloud detection.
2.6.2.2. <strong>IASI</strong> L2 humidity vs. ECMWF analyses. Tests showed
that the water-vapour profiles retrieved in the low
troposphere with the OEM are essentially dominated by
the a priori (see discussions in Section 2.6.1), yielding large
systematic errors. The final choice of retrieval for the <strong>IASI</strong>
L2 product is configurable and, in the case of the watervapour,
it was therefore decided for version 5 to select the
first guess profiles retrieved with the EOF regression [19]
introduced in Section 2.4.1. The water vapour retrievals
perform differently depending on the scene and the overall
water vapour content. As with the temperature profiles,
the retrieved profiles (with v4 and v5) and the modelled
profiles are in good agreement down to 700–800 hPa, with
an rms of about 10% relative humidity (RH) and no bias.
Consistently with the conclusions drawn from the
validation with sondes, the departures are larger in the
boundary layer, with an rms of the order of 20% RH. The
retrieved relative humidity profiles for both PPF v4 and v5
lie in a different range than the ECMWF analyses, usually
with a much smaller dynamic. This is typically the case in
the Northern Hemisphere over oceans where the modelled

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T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
Fig. 19. Time series of the temperature errors at 700 hPa (bias: plain line, standard deviation: dash-lines) with different satellite products (blue: AIRS,
purple: <strong>IASI</strong>/NOAA, gold: <strong>IASI</strong>/EUMETSAT, green: UCAR, red: GFS forecastts) as evaluated against collocated in-situ sonde measurements. (http://www.
star.nesdis.noaa.gov/smcd/opdb/poes/NPROVS.php). (For interpretation of the references to color in this figure legend, the reader is referred to the web
version of this article.)
humidity at 980 hPa varies from 20% to 90% RH while PPF
v5 (v4) spans a shorter 55–80% RH (30–70% RH) range. The
best agreement is obtained in the tropics [301S; 301N] over
land with a correlation coefficient of 0.76 for PPF v5 and
0.58 for PPF v4. The overall performances of the <strong>IASI</strong> L2
humidity products v5 are otherwise comparable to the v4.
2.6.2.3. Monitoring against sondes. The operational EUMET-
SAT <strong>IASI</strong> L2 products have been monitored at the U.S.
National Oceanic and Atmospheric Administration (NOAA)
in the scope of the NOAA Products Validation System
(NPROVS) which compiles datasets of collocated radiosonde,
dropsonde and appended numerical weather prediction
(NWP) data for comparisons to satellites products [58].
Each day, the statistics (mean and standard deviation) for
temperature and water vapour at selected pressure levels are
computed and stored in view of monthly and long-term
trend visualisations for, among other instruments, the
infrared sounders <strong>IASI</strong> and AIRS L2 products. Fig. 19,
generated by the NPROVS on-line tool (http://www.star.
nesdis.noaa.gov/smcd/opdb/poes/NPROVS.php, last accessed
23/01/2012), shows the error and long-term trend for
temperatures retrieved at 700 hPa from different spaceborne
instruments. The operational EUMETSAT <strong>IASI</strong> L2
product is displayed in gold, the <strong>IASI</strong> L2 produced at NOAA
in purple, the AIRS L2 temperatures in blue and the retrievals
from the COSMIC (Constellation Observing System for
Meteorology, Ionosphere & Climate) mission in green. In
addition, numerical weather predictions are shown in red.
The black arrows indicate the introduction of the <strong>IASI</strong> L2 PPF
v5. The improvement with respect to the former version as
compared to the sonde measurements appears clearly in the
bias (plain thick line), which drops from about 1 K to around
0.25 K, as well as in the standard deviation, which is reduced
by approximately 1 K. The <strong>IASI</strong> L2 v5 products also perform
very well in comparison to the other satellite products. These
conclusions apply at 500 hPa. At 300 hPa and higher, version
5 does not bring an improvement, with <strong>IASI</strong> L2 products
having good quality in comparison to the other satellite
products: the bias towards the sondes ranges between 0
and 0.5 K and the standard deviations is around 1.5 K. At
850 hPa, the departures are larger: the standard deviation is
around 2.5 K for all satellite products and EUMETSAT <strong>IASI</strong>
temperature shows larger biases larger than NOAA’s
temperatures, by 1–1.5 K on average, at the level of the
biases characterised for AIRS products. As for the watervapour,
the <strong>IASI</strong> L2 v5 is comparable to v4, as concluded in
the previous section, with departures from in-situ
measurements usually slightly larger than for the other
satellite products analysed in NPROVS. The reasons for
these relative larger errors are still under investigations and
current developments address a different configuration (e.g.
channel selection, land surface emissivity inputs) as well as
the joint use of independent information from the microwave
measurements, less sensitive to cloud-contamination, to
constrain the retrievals in the lower troposphere.
2.7. The atmospheric composition products
2.7.1. Ozone products and reference datasets
The <strong>IASI</strong> L2 O 3 product consists of a total and three partial
columns from ground to 478.54 hPa ( 6km), 222.94hPa

T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1363
( 12 km) and 132.49 hPa ( 16 km). The vertical profile
resulting from the OEM (see Section 2.4.2) isintegratedto
form these columnar amounts and is included in the final
<strong>IASI</strong> L2 product. Only the total column is currently distributed
as an operational product. The partial columns are still
subject to further validation and development.
The validation of the total column (TC) retrieved from
<strong>IASI</strong> measurements involved comparisons to O3 retrievals
generated by the EUMETSAT SAF on ozone and Atmospheric
Chemistry Monitoring (O3MSAF) from the Global
Ozone Monitoring Experiment-2 (GOME-2) instrument, on
Metop-A. This was done as part of the EUMETSAT internal
calibration and validation activities on EPS and was completed
by an external study performed by the ‘‘Laboratoire
Atmospheres, Milieux, Observations Spatiales’’ (LATMOS),
France. GOME-2 is a nadir viewing scanning ultraviolet and
visible spectrometer covering 240–790 nm with spectral
resolution varying from 0.26 to 0.51 nm that allows the
daytime retrieval of O 3 among other species, such as NO 2,
SO2, BrO, OClO and CH2O. It has a ground pixel size of
80 40 km 2 and a swath width of 1920 km comparable to
that of <strong>IASI</strong>, which provides an almost daily global coverage.
For more details, see Munro et al., 2006 (‘‘GOME-2 on
Metop: From in-orbit verification to routine operations’’,
proceedings of EUMETSAT Meteorological Satellite Conference,
2006, Helsinki). The accuracy of the GOME-2 O3 TC
product had been assessed using ground-based retrievals
from Dobson and Brewer measurements as references [59].
It is shown to have little bias, varying with location, season
and viewing geometry (e.g. solar zenith and satellite
scanning angles) between 1% and 1% generally, with
larger systematic errors in the tropics ( 2–3%). The
standard deviations typically range from 4–5% with high
correlation score ( 0.94) between the satellite and the
ground-based products.
2.7.2. Intercomparison results
We extract hereafter a summary of the methodology
and the outcome of the external validation study performed
by George and Clerbaux (LATMOS) in 2010. <strong>IASI</strong> O 3
TC from the month of August 2009, reprocessed with the
<strong>IASI</strong> L2 PPF v5, were compared with available observations
from the thermal infrared satellite missions Aqua/AIRS [7]
and from GOME-2/Metop. The reader is referred to the
final report (George and Clerbaux, July 2010, ‘‘Validation
Study for <strong>IASI</strong> Trace Gas Retrievals’’) for full details. The
comparison was performed for cloud-free retrievals (as per
<strong>IASI</strong> L2 flags) on monthly averages over a 11 11 latitude/
longitude grid. The correlation between the O3 TC from
<strong>IASI</strong> and with the two reference products is about 0.9. The
<strong>IASI</strong> products are on average 1.5% higher than the operational
product from GOME-2 (day) and approximately also
1.5% lower than the AIRS (day and night) retrievals. The
best agreement is found in the Southern Hemisphere
(latitude in [451S; 151S]), where EUMETSAT <strong>IASI</strong> products
show a slight positive bias of 0.35% against GOME-2 O 3 TC
and 0.43% (0.37% night) against AIRS products. The correlations
are of 0.97 and 0.95 (0.96 at night) with GOME-2 and
AIRS products, respectively.
In the background regions with low concentrations,
the <strong>IASI</strong> O3 is larger than in the GOME-2 and AIRS
products. In the inter-tropical band (latitudes between
[151S; 151N]), the bias in <strong>IASI</strong> products is of 2.1% and the
departures are within 0% and 5%. The correlation is of
0.93. In contrast, the range of differences with AIRS is
much broader, spanning a 10–15% interval. The correlation
coefficient between these <strong>IASI</strong> and AIRS data samples
is of 0.69 only. These monthly average results are consistent
with a pixel-to-pixel inter-comparison study performed
at EUMETSAT for the period 19–24 March 2010 at
latitudes below 701. Fig. 20 shows the maps (GOME-2–
<strong>IASI</strong>) bias and standard deviation. Quantitatively, <strong>IASI</strong>
products have an overall positive bias of approximately
2.6%, with standard deviations below 3%. Larger discrepancies
are observed over the Sahara, with s getting as
high as 6–9%, which are assumed to result from inaccurate
surface parameter settings and possible dust contamination;
investigations are still ongoing in this area. As
reported in the validation of GOME-2 products with
ground-based data, the average relative difference
(GOME-2 <strong>IASI</strong>) varies with latitude from about 4% (<strong>IASI</strong>
higher) around the Equator and in Southern tropical
latitudes to little or no bias (o1%) at Northern midlatitudes.
Not presented here, the departures assessed as a
function of the <strong>IASI</strong> scan position and showed discontinuities
of the order of 1% which are attributed to scan
angle dependencies, known and corrected for in the
reference product [59].
In a second step, the operational <strong>IASI</strong> O3 TC was
compared to the research products generated at the ‘‘Université
Libre de Bruxelles’’ (ULB) in collaboration with the
LATMOS [25,60]. They are based on the retrieval software
Fast Optimal Retrievals on Layers for <strong>IASI</strong> (FORLI) [61],
implementing an OEM after Rodgers [45], and the radiative
transfer model Atmosphit. Independent validation of this
product were carried out with ground-based retrievals by
Anton et al. [62] over the Iberian Peninsula as well as with
in-situ sonde measurements (G. Dufour, 2011, in discussion:
‘‘Validation of three different scientific ozone products
retrieved from <strong>IASI</strong> spectra using ozonesondes’’, Atmos.
Meas. Tech. Discuss., 4, 5425–5479, 2011, doi:10.5194/
amtd-4-5425-2011). In [62] FORLI-O3 retrievals are shown
to overestimate the O 3 TC by 4.4%, with standard deviations
of error of 3– 5%. Dufour, 2011, evaluated the global error
budget to approximately 5%. The correlation with reference
O3 TC is high in both studies, usually above 0.9.
EUMETSAT reprocessed v5 and FORLI O 3 TC were
directly inter-compared for the whole month of August
2009, on a pixel-to-pixel basis. The two products correlate
well, with a global correlation coefficient larger than 0.9.
The overall (EUMETSAT–FORLI) biases (and associated
standard deviation) are 1.7% (3.6%) for day time and
2.1% (3.6%) for night time. EUMETSAT O 3 values are
generally smaller than FORLI and, in the light of FORLI
validation results referred to previously, have less bias
with respect to the true O3 concentrations. The best
correlations are observed in the Southern Hemisphere
(latitudes [ 451; 151]) where high O3 concentrations
are measured, with correlation coefficients of 0.96 (for
day and night time). In this region, the mean biases (and
associated standard deviation) are 2.2% (2.7%) for day
time and 2.5% (2.6%) for night time. Specific topical case

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T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
Fig. 20. Maps of the (GOME-2–<strong>IASI</strong> v5) O 3 relative bias (top) and standard deviation (bottom) for the period 19–24 March 2010.
studies were performed in 51 51 latitude/longitude
boxes, addressing regions of high O 3 concentrations
(e.g. along Polar vortex) and background O3 regions (such
as the mid-Atlantic, Tropical forests and Africa), as well as
over polluted (China) and urban (Teheran and San Francisco)
areas. They confirmed the global figures, but with
larger amplitude with high O3 load (bias 3.5%) as
compared to O 3 background locations (bias 1.8%).
As part of the Continuous Development and Operations
Phase (CDOP-2) of the O3M-SAF (o3msaf.fmi.fi), the
FORLI-O3 algorithm will be integrated to the operational
<strong>IASI</strong> L2 processing chain. Additional validation work is
foreseen, including comparison to ozone sondes and
additional satellite products.
2.7.3. Carbon monoxide 1
The operational CO product in <strong>IASI</strong> L2 v5 is the total
column amount retrieved with the artificial neural
1 The studies involving real <strong>IASI</strong> measurements were performed
with the pixels 3 and 4 only. Because of an inter-pixel difference in the
L1C radiances (which is not discussed in this paper), mostly affecting the
CO and N 2O region and to which the CO retrieval was sensitive, the
network introduced in Section 2.4.1. As such, it does not
include vertical sensitivity and error estimates, as can be
obtained with the OEM for other parameters. Similar to O3
in Section 2.7.1, its performance was first assessed internally
at EUMETSAT and the validation was completed by an
external study performed by LATMOS. We present here a
summary of the rationale and outcome of the performance
assessments characterising the updated CO product which
became operational on 14/09/2010, with the release of the
<strong>IASI</strong> L2 v5. This addresses the theoretical accuracy as well
as inter-comparison with other satellite products.
2.7.3.1. Theoretical performances. The theoretical performances
can be assessed with the database used to teach
the artificial neural network. An overview of its composition
and associated results is given here, a more detailed
(footnote continued)
operational production of <strong>IASI</strong> L2 CO (and N 2O) TC on pixels 1 and 2 was
interrupted with the release of the PPF v5. The radiance inter-pixel
difference was reduced with an update of the <strong>IASI</strong> L1c processing chain
on 07/02/2011 and the production of L2 retrievals for the four IFOVs
resumed on 14/03/2011.

T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1365
Fig. 21. CO absolute (left) and relative (right) training errors. The overall statistics are displayed in black while the fitting Gaussian and associated
numbers are shown in red. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
presentation and discussion on the performance is available
on-line in EUMETSAT Technical Note EUM/MET/TEN/09/
0232 cited in Section 2.4.1. The training base approximately
contains 200,000 patterns made up of atmospheric
state vectors and their associated synthetic <strong>IASI</strong> spectra
computed with RT<strong>IASI</strong>-5.3. The atmospheric temperature,
humidity surface pressure and wind components are based
on the climatological database from Chevallier, 2001, cited
in Section 2.4.2.3. As trace gas profiles are not part of this
dataset, they were generated to cover the whole range
of expected situations with random variations around
standard profiles. In the case of CO, the vertical distributions
are based on 43 original profiles sampled by D.
Cunnold, 2001 (personal communication) from the
MOZART 3D chemical transport model calculations [63].
To generate a realistic and continuous set of scenarios for
CO, the selected profile was subsequently randomly either
left unchanged or varied by adding up to half of the
variability (max min) of the mixing ratio in the basic 43level
modelled vertical distributions. By construction, the
training set, which can be seen as the background in the
OEM, is characterised by a high vertical correlation. As for
surface parameters, elevated areas are by essence included
in the training set which also comprises a wide range of
surface types. Over water, the surface emissivity was
computed analytically [15,16]. Ground emissivities were
derived from the MODIS UCSB emissivity library as
follows: based on the spectra of pure surfaces, composite
surfaces have been generated with random contributions
from up to three different types, excluding however
combinations like snow/ice at tropical temperatures.
Random noise as per the <strong>IASI</strong> instrument noise
characteristics was added to the input radiances in order
to regularise the training [64]. Neither clouds nor aerosols
were included to compute the synthetic radiances such that
the networks learnt pure clear cases only, which are also
subsequently their nominal domain of validity.
The teaching database was split into a training and a
control set. To avoid overtraining, i.e. the net becoming too
specific to the training patterns and losing its generalisation
ability, one monitors the retrieval error of the control set and
stops the learning before it starts diverging from the training
error. The correlation between retrieved and target columns
is 0.99 and the linear relation is very close to unity (slope
0.97). Due to the non-linear nature of the MLP, the errors
are not statistically Gaussian-like, as can be seen in Fig. 21.
The absolute rms error typically lies between 0.2 and
0.24 10 18 molecule/cm 2 (Fig. 21, left) and is independent
of the column density itself (not shown here). As a result, the
relative errors are higher for the thinnest columns. For total
columns higher than 0.7 10 18 molecule/cm 2 , the relative
error ranges from 7% to 11% standard deviation with small
bias below 1%. These figures are consistent with an error of
11% as characterised in a <strong>IASI</strong> L2 CO product assimilation
experiment in the chemistry and transport model MOCAGE
[65] (El Amraoui 2011, personal communication).
2.7.3.2. Intercomparisons with other satellite products.
Following the approach of George et al. [66], the
characteristics of the operational CO product were
evaluated against other space-borne CO products, with
longer operational and validation heritage. Namely, they
are retrievals from the Measurement Of Pollution In The
Troposphere (MOPITT) instrument, from Aqua/AIRS and
from the Tropospheric Emission Spectrometer (TES)
onboard Terra. We summarise hereafter the results of the
comparisons against MOPITT CO as obtained at EUMETSAT
and in the frame of the external study performed by
George and Clerbaux (LATMOS). The full details can be
consulted on-line (www.eumetsat.int) in the EUMETSAT
technical Note EUM/MET/TEN/09/0232, and in the
validation report (George, July 2010) cited in Section 2.7.2.
MOPITT is part of the polar orbiting Terra satellite
payload and was launched in December 1999. The ground
pixel size is larger than for <strong>IASI</strong>, 22 22 km 2 at Nadir, and
has a swath width approximately half the size of <strong>IASI</strong>’s,
allowing a global coverage in 3 days. Terra and Metop are
on sun-synchroneous Polar orbits, with slightly different
parameters and a descending node at 10.30 and 09.30,
respectively, such that correlative <strong>IASI</strong> and MOPITT

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T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
Fig. 22. CO total column retrieved from MOPITT (top) and <strong>IASI</strong> (bottom) during 25–31 August 2008.
sensing were distant by one hour on average in this study.
The MOPITT CO version 3 products were used in this
work. They are based on the observations in the 4.7 mm
CO fundamental band only and exploit the same spectral
region as the retrievals from <strong>IASI</strong> measurements. The
profiles are retrieved on 7 vertical levels using a nonlinear
optimal estimation method including a unique and
global a priori [67]. They are distributed with the integrated
total column, an estimate of vertical sensitivity
(contribution of background a priori vs. retrieved information)
and a cloud mask (no retrievals in cloudy pixels).
The degrees of freedom for signal, i.e. the number of
independent pieces of information in the retrieved profiles,
are usually maximal in the tropics, where they do
not exceed 2, and drop down to 1 or even below 1 at mid
and Polar latitudes [68].
In the assessment performed at EUMETSAT, we compared
single <strong>IASI</strong> retrievals to L3 Daily MOPITT CO total
column, gridded in 11 11 longitude/latitude bins for
daytime and night time sensing separately. Departures
were computed for the whole months of August and
November 2008 between each cloud-free <strong>IASI</strong> IFOV (as
per <strong>IASI</strong> L2 flags) and the average retrieval in the corresponding
day/night nearest MOPITT product grid point.
The MOPITT CO profiles with average contributions from
the background a priori larger than 50% were discarded
from the inter-comparison. This mostly occurs at higher
latitudes or at night, when the surface cools down and has
no thermal contrast with the boundary layer [25]. Asan
example, we present qualitative comparison MOPITT vs.
<strong>IASI</strong> CO for the last week in August 2008 in Fig. 22. The
main CO patterns are observed in both products: biomass
burning in Africa and South-America, as well as pollution
in Eastern Asia, transported out over the Pacific and
between North America and Europe [69]. Differences
between the two products are visible in the Sahara, off
the coast of California and west coast of South America.
The latter discrepancy is likely caused by undetected
clouds in the <strong>IASI</strong> L2 processing as there is no or little
counterpart in the MOPITT products, which are generated
for clear-sky only. Quantitatively, the global correlation is
of 0.82 and 0.79 for August and November, respectively.
The statistics were broken down in latitude bands and
day/night conditions. The results for August 2008 are
repeated in Table 7, the conclusions are similar for
November 2008. The best agreement is found between
301S and 601N; especially in the tropical regions with a
correlation as high as 0.86 and a small relative bias below
3%. Overall, the standard deviation (MOPITT–<strong>IASI</strong>) CO TC
range is 0.17–0.25 10 18 molecules/cm 2 , or 8–15% in
relative terms. <strong>IASI</strong> CO TC underestimates MOPITT retrievals:
the bias varies with the latitude and larger systematic
differences from 10% to 20% are observed in the North
Pole and at higher Southern latitudes, exceeding even 30%
over Antarctica. They correspond to areas with colder
surface temperature and lower thermal contrast, where
the respective retrievals are expected to be more influenced
by the background a priori. The comparison was
extended by George (2010) to February 2009 and performed
on monthly averages. The results confirmed the
characterisation of an average bias of about 10% as
compared to MOPITT CO (<strong>IASI</strong> CO lower) and high correlation
coefficients around 0.9 in the tropics for all three
months.

T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1367
Table 7
Statistics of departures (MOPITT–<strong>IASI</strong>) CO total column in 10 18 molecules/cm 2 for different latitudes in August 2008 (bias, standard deviation, correlation
coefficient and sample size are stored in columns).
August 2008
Day Night
Bias s r # Bias s r #
[601N; 901N] 0.14 0.21 0.66 86106 0.22 0.17 0.69 4713
[301N; 601N] 0.06 0.23 0.72 127351 0.01 0.25 0.72 104175
[301S; 301N] 0.07 0.21 0.84 250628 0.04 0.22 0.86 237613
[601S; 301S] 0.33 0.20 0.68 39146 0.29 0.21 0.63 37801
[901S; 601S] 0.63 0.12 0.38 431 0.54 0.18 0.32 1795
2.7.3.3. Intercomparisons with FORLI-CO. The operational
total column was also evaluated against the research
product retrieved from <strong>IASI</strong> measurements with FORLI-
CO at ULB/LATMOS [25]. FORLI-CO is a dedicated version
of the OEM research algorithm introduced in Section 2.7.1
[61], allowing the retrieval of CO profiles under clear-sky
assumptions. The FORLI-CO products were validated
against in-situ airborne measurements in the specific
Polar context [70], where correlation from 0.15 to 0.84
and biases between 5% and 12% with respect to the
reference data are reported. The best match is achieved
in situations with favourable thermal contrasts. George
et al. [66] assessed their accuracy against retrievals from
other space-borne sensors (MOPITT, AIRS and TES) [66]
and characterised a high correlation coefficient, between
0.83 and 0.94 between FORLI-CO and the respective
correlative satellite products. Typically 2.5 DoFs were
found in the tropics, decreasing to 1.5 on average at
mid-latitudes and down to 0.8 at higher latitudes,
towards the Poles. The study confirmed that Polar
retrievals, especially over Antarctica, were essentially
constituted by the background a priori. The main
difference between the FORLI-CO and the EUMETSAT
operational CO TC is in the numerical method employed
(OEM vs. ANN, respectively), which allows the retrievals
of profiles with error and vertical sensitivity estimates on
one hand (FORLI) and is limited to the total column on the
other hand (EUMETSAT). Another important difference is
the processing of cloud-contaminated pixels with FORLI
while the <strong>IASI</strong> L2 PPF strictly limits the CO retrievals to
cloud-free IFOVs. Indeed, FORLI implements a cloud test
similar to the NWP test described in Section 2.2.1 with a
less conservative threshold and tolerates cloud
contaminated pixels with potential cloud fractions of up
to 25% ([25] Section 3.2.1). As a result, the retrieved CO TC
yield and therefore its coverage are much higher with
FORLI-CO.
In an external study performed at LATMOS in 2010,
George and Clerbaux compared the two <strong>IASI</strong> CO products
on a direct pixel-to-pixel basis, for the months of August
2008, November 2008 and February 2009. Only cloud-free
pixels, as per <strong>IASI</strong> L2 flags, were retained for the intercomparison.
A summary of the results is reported here,
the details can be consulted in the validation study final
report. For the three months investigated, EUMETSAT CO
distributions match the FORLI distributions well. For data
over all latitudes, the (EUMETSAT–FORLI) biases (and
associated standard deviations) are of 2.1% (10.6%), 3.1%
(11.5%) and 1.4% (15.2%), respectively, for August 2008,
November 2008 and February 2009. We observe the best
correlations in the equatorial region (latitudes between
151S and 151N) with respective correlation coefficients of
0.93, 0.92 and 0.94. In this region the agreement is
excellent with the slopes of the regression lines close to
1 and the biases (standard deviation) are 1.4% (7.9%), 4.7%
(8.2%) and 1.2% (7.3%), respectively, for August 2008,
November 2008 and February 2009. The correlation drops
to 0.52 at boreal latitudes but remains around 0.8 at midlatitudes.
In regions of low background CO concentrations
(Atlantic, Pacific) as well as over forests, EUMETSAT CO TC
is consistently higher than FORLI-CO, with biases of 5–7%
and departures standard deviations of 6.5–9%. In biomass
burning regions, the bias between EUMETSAT and FORLI
products is small; 1% (EUMETSAT lower) with 9%
relative standard deviation. In the context of high pollution
(China), however, EUMETSAT CO TC is noticeably
smaller than FORLI by 6.7% on average, with a standard
deviation of 11.6%. Some retrieval artefacts over the
deserts observed in FORLI-CO maps, induced by inaccurate
surface emissivity [70], are not seen in EUMETSAT
products, which show a higher spatial coherence in these
regions. Conversely, a few dubious high CO concentrations
were observed in EUMETSAT products off the
Namibian and Californian coasts. The current assumption
attributes this feature to undetected low level clouds, to
which FORLI-CO seems less sensitive.
This good agreement between both <strong>IASI</strong> CO products
was confirmed in a recent case study evaluating the
ability of space-borne infrared sounders to quantify the
Russian fire CO emissions with validation against groundbased
measurements and retrievals [71]. The FORLI-CO
profiles have been monitored and assimilated in the
numerical model run in the scope of the Monitoring
Atmospheric Composition and Climate (MACC) project
since February 2009. MACC is the current pre-operational
atmospheric service of the European GMES programme
(www.gmes-atmosphere.eu, last accessed 23/01/2012). It
provides data records on atmospheric composition for
recent years, data for monitoring present conditions and
forecasts of the distribution of key constituents for a few
days ahead (Inness et al., 2009,‘‘GEMS data assimilation
system for chemically reactive gases’’, ECMWF Technical
Memorandum 587) [72]. With the introduction of the <strong>IASI</strong>
L2 PPF v5, the CO TC generated at EUMETSAT was

1368
monitored in this context during the whole month of
September 2010. Fig. 23 (Inness, 2010, personal communication)
illustrates the noticeable improvement of
EUMETSAT CO product with the introduction of <strong>IASI</strong> L2
v5 on 14/09/2010 as compared against the MACC model
first guess (blue) and analyses (red), as well as its overall
good quality as compared to FORLI-CO total column.
Future developments in this area will address the
operational production of CO profiles with error estimate
to allow the assimilation into numerical models. In that
perspective, as part of the Continuous Development and
Operations Phase (CDOP-2) of the O3M-SAF, the FORLI-CO
retrieval will ultimately become part of the <strong>IASI</strong> L2
processors operated at EUMETSAT central application
facility (CAF).
2.7.4. N2O, CH4 and CO2
The N2O, CH4 and CO2 products planned in the <strong>IASI</strong> L2
trace gases suite are not operational. These retrievals are
still subject to research and development. The total
columns are currently retrieved with individual artificial
neural networks similar in concept to the network used
for CO. They benefited from the same algorithm improvements
introduced in <strong>IASI</strong> L2 PFF v5 and are currently
produced in an experimental mode. Ricaud et al. [73]
studied the <strong>IASI</strong> L2 v4 N 2O during the months of March–
April–May 2008 and found a reasonable agreement
between retrieved N 2O total column and transported
patterns modelled with MOCAGE [73]. In order to extend
these assessment studies, the <strong>IASI</strong> L2 trace gas products
were recently reprocessed with the PPF v5 for the whole
year of 2008 and the months of February, May, August
and November in the years 2009–2011.
The synergistic use of <strong>IASI</strong> with microwave measurements
from Metop/AMSU is anticipated to improve the
separation of temperature and the trace gas (CH 4,CO 2)
information present in the <strong>IASI</strong> spectra [74,75].
3. Conclusions and outlook
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371
Fig. 23. Departures monitoring of <strong>IASI</strong> CO observations—MACC model (first guess in blue, analyses in red) in September 2010. Credits: A. Inness
(ECMWF). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
We have presented the structure of the operational <strong>IASI</strong>
L2 processing chain version 5, which became operational
on 14/09/2010; the individual retrieval modules and their
algorithms and configuration, a summary of the performance
assessment through various internal and external
validation studies, and the current and future development
activities after 5 years of operations at EUMETSAT. The
validation of the various retrieved geophysical parameters
has been conducted with a wide range of satellite products
(CALIOP, AATSR, MODIS, GOME-2, AVHRR, MOPITT, SEVIRI,
etc.), with numerical weather prediction and chemistry
models (ECMWF, MOCAGE, MACC) and with in-situ measurements
(radio-sondes, buoys etc.). Due to the evolving
nature of the PPF, updates since Metop launch and also
upcoming upgrades, systematic reprocessing will take
place at EUMETSAT to generate continuous and consistent
<strong>IASI</strong> L2 products records over the overall operational period
of <strong>IASI</strong>. They will start with the <strong>IASI</strong> L1C in 2013 after the
commissioning of Metop-B, and will be followed by the L2
products.
Significant improvements were obtained with version 5:
in characterisation of cloud products. The cloud top
pressure is usually retrieved within 50 hPa rmse as
compared to ground-based radars. The correlation is
high (0.9) with the cloud Lidar CALIOP for Polar clouds.
in cloud detection, with a positive impact on the SST.
in characterisation of the SST. A demonstrational L2P
product has been created and distributed in the scope
of the GHRSST. The SST is typically characterised by a
cold bias of about 0.3 K and error standard deviation of
approximately 0.3 K.
in LST. The retrievals show a correlation of 0.9 with the
SEVIRI LST derived at the LSA-SAF and the errors
estimated are below 2.5 K rmse.
in temperature profile retrievals, especially in the mid and
upper troposphere where rms of departures computed
against ECMWF analyses are as low as 0.7 K over oceans;
in ozone total column. The operational O3 TC shows
high correlation with other calibrated satellite products
from GOME-2 and low relative departures ranging
from 3% to 5%.
in trace gas retrieval. Comparison of the operational
CO against models and satellite products (MOPITT and
FORLI-CO) confirmed the improvement expected with
synthetic data. The typical errors vary between 8% and
15% (standard deviation) with latitude dependent
smaller biases.
Day-2 developments are ongoing to further improve
the products, with a particular focus on the retrieval of
temperature and humidity in the boundary layer, where

the errors are larger. This is addressed with a different
configuration of the background information in the OEM,
in order to take advantage of the natural cross-correlation
between temperature and humidity [76,77]. On the measurement
side, recent work from Masiello et al. [78]
indicates that the information content of <strong>IASI</strong> is not
sufficiently well exploited with the current channel sampling
and that a different channel selection or the use
of <strong>IASI</strong> principal components is needed to sound the
temperature and especially humidity in the boundary
layer [78]. Work has also started to optimise the characterisation
of the measurement error described in
Section 2.4.2.2, in order to give a more adequate weight
to the measurements. Synergistic processing of microwave
measurements (from AMSU and MHS/Metop)
together with <strong>IASI</strong> radiances is investigated as a means
to provide additional independent temperature/humidity
information and to support the explicit exploitation of
cloud contaminated radiances, which RTTOV-10 now
allows. The absolute validation of water-vapour profiles
still requires dedicated studies to create a more exhaustive
accurate reference data set and to take account of the
spatial and temporal non-coincidences which limit the
validation with in-situ radio-sondes measurements as
discussed by Pougatchev et al. [57] during the validation
campaign at Lindenberg. The absolute accuracy of the
operational radiosonde water-vapour measurements was
discussed by Vömel et al. [79] and exhibit significant
biases of the order of 6–8%, depending on the type of
sensor used. More recently, Calbet et al. [80] showed the
importance of using high-quality synthetic data, computed
from temperature and humidity profiles measured
with cryogenic frost-point hygrometers flown on sondes,
for fitting the <strong>IASI</strong> measurements with simulated spectra.
In the area of atmospheric composition, the FORLI-CO and
O3 algorithms [25,61], allowing profiles retrievals and
error estimates, will be integrated to the operational <strong>IASI</strong>
L2 processor in the scope of the O3M-SAF CDOP-2 phase.
Acknowledgements
T. August et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 113 (2012) 1340–1371 1369
The authors wish to acknowledge the contributions
and the support of a large number of partners and
cooperating institutions. Without them this work would
not have been possible. To mention all individuals would
be beyond the scope of this paper, however we would like
to acknowledge and appreciate the role of the <strong>IASI</strong>
Sounding Science Working Group, established jointly by
CNES and EUMETSAT, which played a central role for this
development. Claude Camy-Peyret is one of its co-chairs
from the very beginning and his help and guidance is very
much appreciated.
The authors wish to thank CNES for the investigations
and the reduction of the <strong>IASI</strong> interpixel differences.
The authors acknowledge the work done by Maya
George and Cathy Clerbaux (LATMOS) in the external
validation study of the CO and O 3 products.
The satellite products from US missions were downloaded
from the NASA EO portail, former Warehouse
Inventory Search Tool, replaced as of January 2012 by
Reverb/ECHO (http://reverb.echo.nasa.gov). MOPITT CO
and CALIOP cloud products were obtained from the NASA
Langley Research Center Atmospheric Science Data Center.
The authors acknowledge ESA and IFREMER for the
availability of the AATSR L2P products (ftp://ftp.ifremer.fr/
ifremer/medspiration/data/l2p/ats_nr_2p/esa/).
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