AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-6651-2018Improving algorithms and uncertainty estimates for satellite NO2
retrievals: results from the quality assurance for the essential climate
variables (QA4ECV) projectImproving algorithms and uncertainty estimates for satellite NO2 retrievalsBoersmaK. Folkertboersma@knmi.nl https://orcid.org/0000-0002-4591-7635EskesHenk J.https://orcid.org/0000-0002-8743-4455RichterAndreashttps://orcid.org/0000-0003-3339-212XDe SmedtIsabellehttps://orcid.org/0000-0002-3541-7725LorenteAlbahttps://orcid.org/0000-0002-2287-4687BeirleSteffenhttps://orcid.org/0000-0002-7196-0901van GeffenJos H. G. M.https://orcid.org/0000-0003-2121-4553ZaraMarinaPetersEnnohttps://orcid.org/0000-0002-8380-3137Van RoozendaelMichelWagnerThomasMaasakkersJoannes D.van der ARonald J.https://orcid.org/0000-0002-0077-5338NightingaleJoanneDe RudderAnneIrieHitoshiPinardiGaiahttps://orcid.org/0000-0001-5428-916XLambertJean-ChristopherCompernolleSteven C.https://orcid.org/0000-0003-0872-0961Royal Netherlands Meteorological Institute, Satellite Observations, De Bilt, the NetherlandsWageningen University, Meteorology and Air Quality Group, Wageningen, the NetherlandsInstitute of Environmental Physics (IUP-UB), University of Bremen, Bremen, GermanyBelgian Institute for Space Aeronomy (BIRA-IASB), Brussels, BelgiumMax-Planck Institute for Chemistry (MPI-C), Mainz, GermanyHarvard University, Cambridge, Massachusetts, USANational Physics Laboratory (NPL), Teddington, UKCenter for Environmental Remote Sensing (CEReS), Chiba University, Chiba, JapanK. Folkert Boersma (boersma@knmi.nl) 17December201811126651667820June201816August201812November201825November2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/6651/2018/amt-11-6651-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/6651/2018/amt-11-6651-2018.pdf
Global observations of tropospheric
nitrogen dioxide (NO2) columns have been shown to be feasible from
space, but consistent multi-sensor records do not yet exist, nor are they
covered by planned activities at the international level. Harmonised,
multi-decadal records of NO2 columns and their associated
uncertainties can provide crucial information on how the emissions and
concentrations of nitrogen oxides evolve over time. Here we describe the
development of a new, community best-practice NO2 retrieval
algorithm based on a synthesis of existing approaches. Detailed comparisons
of these approaches led us to implement an enhanced spectral fitting method
for NO2, a 1∘× 1∘ TM5-MP data
assimilation scheme to estimate the stratospheric background and improve air
mass factor calculations. Guided by the needs expressed by data users,
producers, and WMO GCOS guidelines, we incorporated detailed per-pixel
uncertainty information in the data product, along with easily traceable
information on the relevant quality aspects of the retrieval. We applied the
improved QA4ECV NO2 algorithm to the most current level-1 data sets
to produce a complete 22-year data record that includes GOME (1995–2003),
SCIAMACHY (2002–2012), GOME-2(A) (2007 onwards) and OMI (2004 onwards). The
QA4ECV NO2 spectral fitting recommendations and TM5-MP
stratospheric column and air mass factor approach are currently also applied
to S5P-TROPOMI. The uncertainties in the QA4ECV tropospheric NO2
columns amount to typically 40 % over polluted scenes. The first validation
results of the QA4ECV OMI NO2 columns and their uncertainties over
Tai'an, China, in June 2006 suggest a small bias (-2 %) and better
precision than suggested by uncertainty propagation. We conclude that our
improved QA4ECV NO2 long-term data record is providing valuable
information to quantitatively constrain emissions, deposition, and trends in
nitrogen oxides on a global scale.
Introduction
Nitrogen oxides (NOx= NO +NO2) in the
atmosphere have far-reaching effects on the Earth system. In the lower
troposphere, nitrogen oxides promote the photochemical production of ozone
(e.g. Liu et al., 1987; Grewe et al., 2012), whereas in the stratosphere,
NOx leads to the catalytic destruction of ozone and the
formation of reservoir species for halogens (e.g. Crutzen et al., 1970).
Nitrogen oxides contribute to aerosol formation, and they are linked to the
oxidising efficiency of the troposphere via ozone, which plays an important
role in the formation of the hydroxyl radical (OH). NO2 itself is
only a weak greenhouse gas (Solomon et al., 1999) but has considerable
relevance for radiative forcing because nitrogen oxides are important
precursors of tropospheric ozone, aerosols, and OH. The net effect of
nitrogen oxides on climate forcing is modelled to be negative or cooling,
with NOx-driven aerosol screening dominating over
tropospheric ozone warming (Shindell et al., 2009). In 2011, the World
Meteorological Organization (WMO) Global Climate Observing System (GCOS)
included NO2 (together with SO2, HCHO, and CO) in its
Implementation Plan for the Global Observing System for Climate in Support of
the UNFCCC (WMO, 2011) “in recognition of the emission-based view on climate
forcing of ozone and secondary aerosols, relevant for climate mitigation and
important for processes”. The formal attribution of NO2 as
a precursor to the essential climate variables, or ECVs
(Bojinski et al., 2014), of ozone and aerosols implies that the scientific community has committed
itself to providing reliable, long-term measurement records of NO2.
Apart from its relevance to climate change, atmospheric nitrogen oxides are
also important for the health of ecosystems and humans. Deposition of
nitrogen to ecosystems may affect the structure and functioning of ecosystems
(e.g. Galloway et al., 2003). Recently, the World Health Organization stated
that it is reasonable to infer that NO2 has direct short-term
health effects, such as airway inflammation and reductions in lung function
(WHO, 2013), and a literature review of epidemiological studies over a wide
geographic area by Hoek et al. (2013) showed that human mortality was
significantly associated with long-term exposure to NO2.
High-quality observations are needed to monitor the concentrations of
nitrogen oxides in the atmosphere, both close to the ground, where
NO2 is relevant for deposition and health aspects, as well as
aloft, where nitrogen oxides influence atmospheric chemistry and climate.
Such measurements are useful for reanalysis studies (e.g. Inness et al.,
2013), contribute to documenting changes in NO2 concentrations and
NOx emissions (e.g. Zhang et al., 2008; Vinken et al.,
2014), and to attributing any such changes to their underlying causes (e.g.
Miyazaki et al., 2012; Verstraeten et al., 2015; Xu et al., 2013). This provides policy makers with
options for decisions to counter environmental problems (e.g. Witman et al.,
2014). Measurements may also enhance the public's appreciation of the extent
and scope of the problem of air pollution. In situ measurements of
NOx concentrations taken on the ground are representative of
the quality of the air people breathe close to the measurement station.
But such stations are relatively scarce in many countries and cannot provide
spatio-temporal continuity on a global scale. Satellite observations, on the
other hand, provide global coverage, thereby offering the unique opportunity
to study spatial patterns and temporal variation in NO2 pollution.
For any type of measurement, it holds that they can only be used properly in
science or as evidence basis for policy decisions if there is unequivocal
confidence in the data sets, as well as a proper understanding of their
limitations.
The EU Seventh Framework (FP7) project, Quality Assurance for Essential
Climate Variables (QA4ECV, 2018, http://www.qa4ecv.eu, last access: 13 December 2018), was designed to demonstrate how
reliable climate data sets can be generated, along with detailed and
traceable information on the quality of such data. Specifically, for
NO2, the goals of this project are as follows:
to generate a multi-decadal (1995–2017) satellite data record of
tropospheric and stratospheric NO2 column densities based on
calibrated satellite data and state-of-the-art retrievals, and
to
provide fully traceable uncertainty metrics for this record, ready for
ingestion in models or in other interpretation efforts. Obtaining global,
long-term, and stable satellite observations with validated accuracy and
precision is not straightforward. The GOME (1995–2003; Burrows et al.,
1999), SCIAMACHY (2002–2012; Bovensmann et al., 1999), OMI (from 2004
onwards; Levelt et al., 2006), and GOME-2A (from 2007 onwards; Munro et al.,
2006) instruments have been providing global observations of NO2
over the last 22 years, but there are important differences in overpass time,
instrumental artefacts (e.g. calibration and design differences), and
signal-to-noise levels that need to be taken into account. To be used
properly, the information content of the NO2 products needs to be
validated over a variety of regions, and users need guidance provided by
well-established quality information to help them judge the
fitness for purpose of the NO2 products.
In this work, we demonstrate our approach to improving a retrieval algorithm
and apply it to generate a multi-decadal record of NO2 columns with
a consortium of European retrieval groups. We follow the guidelines for the
generation of ECV data sets from WMO (2010). Our efforts are inspired by the
QA4ECV project goals described above, but also by recent studies showing that
there is still room for substantial improvement in all sub-steps of the
retrieval (e.g. Richter et al., 2011; Lin et al., 2014; van Geffen et al.,
2015; Krotkov et al., 2016), by the outcome of validation studies showing
that various state-of-science retrievals have biases of the order of tens of
percents (e.g. Jin et al., 2016; Drosoglou et al., 2017; Kollonige et al.,
2018), and the considerable structural uncertainty in retrieved tropospheric
NO2 columns emerging when different retrieval methodologies are
applied to the exact same satellite observations (e.g. van Noije et al.,
2006; Lorente et al., 2017). The efforts from five European retrieval groups
within the QA4ECV consortium allow us to perform a detailed comparison of
current approaches to various retrieval sub-steps. These comparisons have
proven to be helpful in reducing and better quantifying the uncertainty of
the NO2 retrieval. The improved quality of the QA4ECV NO2
record itself and the improved knowledge of the uncertainties should make
the QA4ECV satellite data record better fit the purpose of trend
analysis, data assimilation, and inverse modelling studies.
The paper is organised as follows: in Sect. 2 we discuss how
NO2 data user requirements, the expertise from NO2 data
providers, and the quality requirements defined by GCOS are providing
direction for this study. In Sect. 3 we assess the quality of the best
currently available level-1 data sets for NO2 retrieval from GOME,
SCIAMACHY, OMI, and GOME-2(A) and discuss how this guides the selection of
spectral fitting approaches. Section 4 focuses on the algorithm design and
the traceability of the retrieval approach and external data used. In
Sect. 5, we give an overview of the main lessons learnt in the
intercomparisons of retrieval sub-steps. Section 6 summarises the
uncertainty information provided in the QA4ECV data product and how these
uncertainty estimates compare to the intercomparison results from Sect. 5.
We conclude with a first validation of our new QA4ECV OMI NO2
tropospheric columns and their uncertainties against independent MAX-DOAS
measurements collected during a 1-month campaign over Tai'an, China.
User needs and expert recommendationsUser survey
At the start of the QA4ECV project, we identified the requirements of
data users in terms of uncertainty information and usability of the data
product. This included a survey of 22 NO2 data users and
interviews with three NO2 “champion users”, who provided more detailed
written answers to questions. The questionnaire was aimed at establishing
what users need in terms of quality flags, traceability information, and
product uncertainty description. The main outcome of the survey for
NO2 is summarised in the Supplement. Briefly, users need detailed
quality flags, specific information on random and systematic contributions to
the uncertainties, traceability information on the product, and validation
of the product and algorithm. The full survey also includes results for the
HCHO and CO data products and can be found in QA4ECV Deliverable 1.1
(Nightingale et al., 2015).
Producer requirements
We also carried out a survey of data producer requirements for quality
assurance in satellite data records and discussed retrieval priorities and
quality assurance (QA) needs with retrieval experts from different groups
within the consortium (BIRA-IASB, IUP Bremen, KNMI, Max Planck Institute for
Chemistry, and Wageningen University in alphabetical order). Producers of
data products (other than those involved in QA4ECV) that we interviewed
recognised the need for processing chain information to be more transparent
and more easily accessible for data users.
There was also a strong intrinsic motivation from NO2 data
producers to improve the retrieval algorithms and generate a long-term
NO2 data set from available satellite reflectance measurements. The
NO2 retrieval groups in the QA4ECV consortium discussed priorities
for retrieval improvement, based on their collective experience with the
retrieval, validation, and use of existing individual NO2 data
products for different sensors. The central idea was to arrive at a QA4ECV
consortium algorithm based on best practices derived from lessons learnt
from intercomparisons between approaches for all relevant retrieval
sub-steps and extend the steps initiated within the ESA S5P verification
project (DLR, 2015).
QA4ECV consortium activities
Retrieval of tropospheric NO2 columns is based on a three-step
approach. First, a set of absorption cross sections, including NO2,
is fitted to the measured top-of-atmosphere reflectance spectrum, which
provides the slant column densities (SCDs, Ns). Then
(step 2), the stratospheric contribution to the SCD (Ns,strat) is
estimated and subtracted from the SCD. In the third step, the tropospheric
air mass factor (or AMF, Mtrop) is calculated based on knowledge
of the satellite viewing conditions and assumptions on the state of the
atmosphere in order to convert the residual tropospheric SCD into a
tropospheric vertical column density, VCD (Nv,trop). The retrieval
equation is as follows:
Nv,trop=Ns-Ns,stratMtrop.
The following activities leading to the retrieval improvement were identified
and conducted during the QA4ECV project:
Institutes compared different approaches to spectral fitting. NO2 SCDs were
computed by all groups for the same orbits of level-1 data and results were
compared. This resulted in a quantification of the level of agreement on the
slant columns and a better understanding of the factors responsible for the
remaining differences. This is a relevant exercise in view of the substantial
revisions of spectral fitting approaches over the last years (e.g. Richter et
al., 2011; van Geffen et al., 2015; Marchenko et al., 2015; Anand et al.,
2015) and resulted in the definition of the QA4ECV best-practice spectral
fitting algorithm.
The algorithm SCD uncertainties were evaluated against independent
statistical uncertainty estimates (Zara et al., 2018).
Stratospheric NO2 fields and associated
tropospheric residues from different approaches were compared for consistency and plausibility
checks, and quantification of differences. Recent improvements in the KNMI
data assimilation approach (Maasakkers, 2013) and the newly developed STREAM
scheme (Beirle et al., 2016) provided more insight into the stratospheric
correction and the associated uncertainties.
Altitude-dependent or box air mass factors (AMFs) for
simplified scenarios were compared. This comparison established the degree of consistency
between radiative transfer models, pointed out discrepancies, and provided
hints for possible improvements. The resulting spread between the (box) AMFs
can be interpreted as the structural uncertainty
Structural
uncertainty can be identified with the metrology concept “uncertainty of
measurement method” (see the guide to the expression of uncertainty in
measurement; GUM, 2008, Sect. F.2.5): uncertainty associated with the method of measurement, as there can be other methods, some of them as yet unknown or in some way impractical, that would give systematically different results of apparently equal validity.
when using different radiative
transfer models, vertical layering, and interpolation schemes (Lorente et al.,
2017).
Tropospheric AMFs calculated by different groups with an
increasing number of differences in algorithm choices were compared: from identical
settings (wherein only model, vertical layering, and interpolation differ
between groups), via preferred settings (every group using their own
preferred information on clouds, albedo, NO2 profile, etc.), to a
wider round-robin comparison wherein groups outside of Europe also
participated. This last comparison was unguided; i.e. groups could freely
decide how to calculate their AMFs, deciding for themselves whether to
include aerosol corrections, using look-up tables, correcting for residual
clouds, etc. The spread between the round robin AMFs is indicative of the
structural uncertainty in the AMF calculation (Lorente et al., 2017).
It is impossible at the algorithm development stage to have a full
understanding of which settings and approaches lead to the best results. This
led the consortium to consider it beneficial to include more than one best-practice approach for the stratospheric correction and AMF calculation
sub-steps. Specifically, apart from the proposed default stratospheric
correction method, stratospheric NO2 column estimates from the
independent STREAM method have also been included in the QA4ECV NO2 data
product. For the tropospheric AMF calculation, it was decided to provide both
the standard tropospheric AMF (linear combination of a partly cloudy, partly
clear-sky AMF) but also to include the clear-sky AMF in the data product.
This allows data producers to directly test different retrieval options
(correcting for residual clouds vs. cloud clearing) at the validation
stage and provides users with the possibility to test the robustness of the
data product beyond the quoted retrieval uncertainty alone.
GCOS requirements and GCOS guidelines for data set generation
The Global Climate Observing System (GCOS) published a set of requirements
that tropospheric NO2 columns should fulfil. The requirements from
GCOS report 154 (WMO, 2011) are listed in Table 1 below. The recently
published requirements from GCOS report 200 (GCOS, 2016) are not considered
here yet.
GCOS requirements for satellite retrievals of tropospheric
NO2 columns (WMO, 2011).
1 An uncertainty of 0.03 DU (Dobson units) corresponds to
0.8 × 1015 molec. cm-2. The 0.03 DU holds for
tropospheric NO2 columns up to
4.0 × 1015 molec. cm-2. For larger column values the
relative uncertainty of 20 % holds. Note that we replaced the heading
“accuracy” (WMO, 2011) with “uncertainty” to be compliant with ISO
standard on metrology (VIM). Indeed, (WMO, 2011) states that “the (accuracy)
requirements are indicative of acceptable overall levels for the
uncertainties of product values.” 2 According to GCOS,
the user requirement for stability is a requirement on the extent to which
the uncertainty of a measurement remains constant over a long period
(GCOS-200, 2016).
The GCOS requirements, especially those on resolution, can be discussed for
their adequacy. These are target requirements, which should be advanced
towards when generating a long-term record of tropospheric NO2
column measurements. The resolution requirements listed above cannot be met
by the satellite sensors capable of measuring NO2 that have been
operational over the last 20 years, because of limitations in their
instrument design, with the exception of the recently launched S5P-TROPOMI
sensor, which does meet the requirement. Indeed, the GCOS report states that
“products at lower spatial and temporal resolution” than 5–10 km (that is
the NO2 products currently available from GOME, SCIAMACHY, OMI, and
GOME-2) “… would be sufficient to provide an independent instrument
data record of long-term precursor trends to assist in the attribution of
changes in ozone and aerosol”.
The target requirements for uncertainty and stability are possibly within
reach, judging from validation studies, and these have motivated the QA4ECV
consortium to find ways to reduce the retrieval uncertainties and to better
estimate the systematic error component of the retrieval uncertainty.
GCOS has also established guidelines for the generation of climate data sets
(GCOS, 2010). Those guidelines serve as a checklist against which ECV
producers can evaluate their production and documentation process
(Nightingale et al., 2018). Section 3 of the Supplement provides a
point-by-point overview of how these guidelines have been taken into account
for the generation of the QA4ECV NO2 data product. A comprehensive
comparison with respect to these and other GCOS requirements (GCOS, 2016;
WMO, 2010, 2011) is available in the QA4ECV Deliverable D6.1 (Compernolle, 2018).
Quality of level-1 data
In the early stages of the QA4ECV project design, it was decided to use GOME,
SCIAMACHY, OMI, and GOME-2 (on MetOp-A) to generate a data record for
tropospheric and stratospheric NO2 vertical columns spanning the
period 1995–2017. Table 2 lists the relevant specifics for these
instruments. For all instruments, the most recent and corrected level-1
data sets are used.
Satellite instruments and level-1 data contributing to the QA4ECV
NO2 ECV data product.
LocalCalibratedSpectralMain level-1 issueoverpassSpatiallevel-1resolution/Instrumenttimeresolutiondata setssamplingGOME (1995–2003)110:30 LT320 × 40 km2version 50.40 nm, 0.20 nmSpectral structures in solar irradiancecaused by diffuser plateSCIAMACHY (2002–2012)10:00 LT60 × 30 km2version 80.44 nm, 0.24 nmOMI (2004–)13:40 LT24 × 13 km2 (at nadir)collection0030.63 nm, 0.21 nmRow anomaly (blockage), stripesGOME-2(A) (2007–)09:30 LT80 × 40 km2; 40 × 40 km2EUMETSAT/R/5_120.50 nm, 0.20 nmThroughput loss resultingafter 15 July 2013in more noise
1 GOME lv1 data are in principle
available up until September 2011, but for a limited area of the globe only.
In June 2003 the on-board tape recorder failed, resulting in reduced coverage
of GOME-observations, since data could only be downlinked in real time during
overpasses above ground-receiving stations. 2 The level-1
data are not exactly the same as in Coldewey-Egbers et al. (2018), which is
v5.1. The main difference between v5 and v5.1 is the consistency of orbits
and not the radiances themselves (Angelika Dehn, personal communication,
2018).
Prior to algorithm testing, we assessed the quality of the relevant level-1
data. Here we briefly discuss our findings and discuss how the quality of
the level-1 data may affect the retrieval of NO2 SCDs and their
uncertainties.
GOME
GOME level-1 data with global coverage are available from July 1995 to
June 2003. ESA produced a GOME level-1 data set for the mission called
version 5.1 (GOME Products and Algorithms, 2018) that is sufficiently well
characterised and complete. An important concern with GOME level-1 data is
that the solar irradiance signal is detected after reflection from a diffuser
plate, whereas the radiance signal is not. The reflection on the diffuser
plate created large and seasonally varying artificial spectral structures in
the solar irradiance (Richter and Wagner, 2001). This makes it very difficult
for GOME to use solar irradiance spectra as a reference in the DOAS spectral
fitting. To avoid the issue, Earthshine radiances over remote regions can be
used as reference spectra. The implication is that only differential
NO2 SCDs are retrieved. To provide the total NO2 SCDs
necessary for an ECV data set of stratospheric and tropospheric NO2
columns, a background correction, typically estimated from an external
source, is required.
Detector degradation is another relevant issue for NO2 and cloud
retrievals in the visible channel. This degradation in the level-1 data has
been estimated to amount to approximately 15 % between 1995 and 2003
(Slijkhuis et al., 2015) and is anticipated to result in modest increases in
the GOME NO2 SCD uncertainty. The quality of the GOME level-1 data
has also been affected by other instrument-related issues, but these may be
of less relevance to the quality of the NO2 spectral fits.
Different GOME scan angles (east, nadir, west) are affected differently in
terms of throughput degradation and dichroic mirror degradation, possibly
resulting in systematic differences in NO2 SCDs and cloud products
for the different scan angles (or stripes).
SCIAMACHY
SCIAMACHY lv1 data are available from August 2002 to April 2012. SCIAMACHY
lv1 version 7.04 data have been made available by ESA in 2016. One particular
feature of the SCIAMACHY level-1 data is that co-adding of spectra was
performed on board SCIAMACHY, prior to downlinking the data from the
satellite to receiving stations. The cluster 424–527 nm was read out more
frequently (than other spectral bands) in order to minimise the co-addition
of spectra and thereby optimising the spatial resolution for NO2 to
60 × 30 km2 (30 × 30 km2 in some latitude
bands). A consequence of this is that only spectral data from the
424–527 nm cluster are available for DOAS NO2 spectral fitting.
Similarly to GOME, SCIAMACHY solar irradiances suffer from spectral
structures from the diffuser plate. A second diffuser was therefore included
in the instrument, mounted on the backside of the azimuthal scan mirror.
Using solar irradiances from this azimuthal scan mirror strongly reduces the
apparent seasonality in NO2 introduced by the diffuser, although
some structures still remain (Richter et al., 2011).
Over its lifetime, the SCIAMACHY instrument suffered from degradation of its
optical components. This degradation is the result of a complex mixture of
aging of the front optics through UV radiation and photochemical reactions,
detector contamination by water vapour deposition, and changes in the thermal
equilibrium of the platform. As a result, the throughput of SCIAMACHY
decreased over the years, in particular in the UV. In addition, small changes
in spectral sensitivity over time, for example from
etaloning
Etaloning refers to unintended multiple reflection of
radiation between optical elements leading to constructive and destructive
interference for some wavelengths.
, are cancelled out when using daily
irradiance spectra for DOAS spectral fits, but this prevents the use of a
single solar irradiance for the full time series. As degradation of the scan
mirror leads to scan-angle-dependent degradation, scan-angle-dependent
biases, or stripes, can therefore develop over time in the NO2
SCDs.
OMI
The OMI instrument produces stable (to ∼ 2 % over the mission time,
in the row anomaly-free areas) lv1 radiances over the period 2004–2017 for
rows not affected by the row anomaly. The OMI level-1 data are from the
Collection 3 data. Processing of this Collection 3 data started in February
2010 with version 1.1.3 of the ground data processing system software
(Dobber et al., 2008) and has produced a complete level-1 data set for the
entire OMI mission. The main issue of the OMI level-1 data is the
row anomaly (RA). From June 2007 onwards, several rows of the CCD detector
(each corresponding to a specific part of the OMI nadir field of view)
received less light from the Earth, and some other rows appear to receive
sunlight scattered off a peeling piece of spacecraft insulation. A plausible
reason for these effects is a partial obscuration of the entrance port by
insulating layer material that may have come loose on the outside of the
instrument. For rows affected by the RA, successful spectral fits can still
be achieved for NO2, but the cloud retrievals suffer from large
errors that cannot be overcome; thus the affected pixels have to be removed
from further analysis. Figure 1 shows the rows flagged in the Collection 3
level-1 data over time. By 2017, 38 % of the available data were affected
by the row anomaly. All rows affected are flagged with a specific row
anomaly flag in the QA4ECV OMI NO2 data product, addressing
the user needs expressed in Sect. 2.1.
Spurious across-track variability, or stripes, are apparent in current OMI
NO2 data products. The stripes appear as discrete jumps in
NO2 SCDs from one viewing angle to the other. The origin of the
stripes is probably related to small differences in spectral calibration and
detector sensitivity from one viewing angle to the other. There is currently
no solution via the level-1 data, but application of a destriping correction
(e.g. Boersma et al., 2011) reduces the systematic stripes to within
acceptable limits. The magnitude of the NO2 destriping corrections
has increased from 0.3 × 1015 to
0.5 × 1015 molec. cm-2 between 2004 and 2016,
related to the use of an annual mean (2005) irradiance spectrum as
reference in the DOAS spectral fits.
Optical throughput changes in OMI's (visible) irradiance channel is of the
order of 1–1.5 % over the mission period (for rows not affected by the
RA). For a signal-to-noise ratio of approximately 500, a deterioration of
1.5 % leads to only marginal increases in NO2 fitting
uncertainties (Zara et al., 2018). Spectral stability, important for the
accuracy of DOAS retrievals, has also been very good in the visible channel
at 0.002 nm. Such wavelength shifts, if unaccounted for, cause NO2
SCD errors of less than 1 %. For more details, please see Sect. 2.2 of
QA4ECV Deliverable 4.2 (Müller et al., 2016) and Schenkeveld et
al. (2017).
GOME-2(A)
GOME-2 on EUMETSAT's MetOp-A satellite is an improved version of the GOME
instrument (Munro et al., 2016). Level-1 data, version 6.0, are available from
January 2007 onwards. A key concern is the accuracy of the long-term record
of GOME-2(A) level-1 data. Like GOME and SCIAMACHY, GOME-2 suffered from
degradation of its optical components during its lifetime. The optical parts
of GOME-2(A) are thought to be increasingly contaminated by outgassing
coating material that was meant to protect the detector electronics (Hassinen
et al., 2016). This contamination resulted in a progressive
wavelength-dependent loss of the instrument throughput. The discontinuity
appearing in September 2009 reflects the second throughput test,
during which the temperature of the GOME-2 instrument was changed in a
controlled way to observe whether or not there was a recovery in performance
at any point during the heating. Although the test did not recover the
degradation already suffered, it did succeed in stabilising the throughput
from September 2009 onwards. The main impact of this degradation is an
increase in the noise due to throughput loss. As a result, uncertainties from
random error on the NO2 slant columns are expected to increase with
time, especially between January 2007 and September 2009. Compared to GOME
and SCIAMACHY, degradation of GOME-2(A) started immediately after launch and
proceeded faster, but was stabilised after the throughput test.
OMI row anomaly in the UV-2/VIS channel as a function of
time throughout the OMI mission (2007–2017). Prior to 2007, there was no row
anomaly. Affected rows (red crosses) are suffering from a partial blockage of
light entering the instrument, so that the absolute radiance levels become
compromised. The upper x axis indicates the percentage of OMI pixels (defined
as the 100 % × ratio of the number of pixels affected by the row
anomaly to the total number of pixels) being affected in a particular month.
In-flight analysis of the GOME-2(A) instrument slit function using a
non-linear fitting of Gaussian line shapes to the Kurucz solar atlas has
revealed significant time variations of the GOME-2 slit function in channel 3
(e.g. Dikty et al., 2011). Specifically, the nominal width of the slit
function (0.50 nm) has decreased over time, probably due to thermal
fluctuations of the GOME-2(A) optical bench associated with seasonal and
long-term changes in the solar irradiance (Munro et al., 2016). In QA4ECV,
this issue is addressed by including the GOME-2(A) slit function as a fit
parameter in the DOAS spectral fitting procedure. However, it is unlikely
that this fully resolves the issue, so that further increases in
NO2 SCD uncertainties over time should be anticipated (Zara et al.,
2018). Compared to GOME and SCIAMACHY, GOME-2(A) solar irradiances suffer
much less from spectral structures caused by the diffuser plate, but some
small effects remain (Richter et al, 2011). One minor issue is the
sensitivity for polarisation structures in the level-1 spectra. In principle,
this is corrected for in the level 0-to-1 algorithm (Munro et al., 2016), but
some residual small spectral features remain that may interact with
atmospheric absorbers in the DOAS fitting.
The instrument specifics, intrinsic quality, and degradation of the four
instruments' level-1 data have guided us in selecting the basic settings for
spectral fitting of QA4ECV NO2 SCDs. We used these guiding
principles:
Select the same spectral fitting window for the four different instruments
(and if not possible ensure spectral overlap as much as possible). NO2 SCDs are known to be sensitive to the selection
of fitting window, as shown in van Geffen et al. (2015), and in the S5P
TROPOMI Verification Report (2015).
Select a wide fitting window including more NO2 absorption features
for an instrument with a relatively low signal-to-noise, i.e. OMI. This is
known to reduce the random component of the uncertainty in the NO2
SCDs (e.g. Bucsela et al., 2006; Boersma et al., 2007).
Select the most practical reference spectrum for the DOAS spectral fitting.
Ideally these spectra are daily solar irradiances, as in the case of OMI, but
if these are compromised in any way, they may be replaced by an average
irradiance spectrum, or by daily Earthshine spectra, as is done for GOME. For
the latter, a correction for the amount of NO2 absorption signature
in the Earthshine reference spectrum is still required.
Algorithm design and traceability
An important ambition of the QA4ECV project is to provide full traceability
on retrieval algorithms. Usually, a condensed flow diagram for the retrieval
algorithm is included in an Algorithm Theoretical Baseline Document (ATBD).
The drawback is that ATBDs are often not easily accessible and that it is
not immediately clear which ancillary information has been used in particular
algorithm sub-steps. We therefore generated an algorithm traceability
chain, a web-hosted interactive flow diagram that shows how the QA4ECV
NO2 algorithm is put together, which external pieces of information
are embedded in the retrieval process, and where details on those pieces of
information can be found. The traceability chain has different layers (Fig. 2).
The main entry for users is the overall algorithm flow chart. Users can
click on algorithm process elements, which takes them a level deeper into the
algorithm. Figure 2 shows how to interact with the NO2 traceability
chain at multiple levels. The chain is provided as a clickable option on the
QA4ECV website along with the options “Data Access” and “User Forum”.
Providing these options at the same entrance level allows users to obtain
a good understanding of how the algorithm works and where ancillary data are
coming from. The “Traceability Chain” button, leads to the full chain
(first layer). Next, as an example, clicking the “DOAS + wavelength
calibration” step will lead to details on that sub-process (second layer).
The absorption cross sections used in the DOAS step are available under
“Laboratory Absorption Cross Sections”, which contains the references to the
cross-section data and papers describing them (third layer). The
references themselves are linked to the digital object identifiers (DOIs)
and take users directly to the relevant paper.
Traceability chain for the QA4ECV NO2 retrieval algorithm.
The orange blocks (rectangles) are the building blocks of the retrieval, and
in the main chain these are clickable and show more details in deeper layers.
The light-blue blocks are also clickable and will provide more information on
that process in a pop-up window. The parallelograms provide information on
algorithm choices and input data sets. The interactive traceability chain is
available at http://www.qa4ecv.eu/ecv/no2-pre (last access: 13 December
2018).
Intercomparison of retrieval sub-steps and algorithm selection
Differences between NO2 retrievals from different retrieval groups
can be traced back to different settings and to different a priori parameters
used in the individual retrievals. We made a systematic step-by-step analysis
of all components of the NO2 retrieval by documenting and
comparing approaches from the consortium institutes and analysing their
contribution to differences and their benefits. These tests, evaluations, and
innovations have guided the development of the QA4ECV consortium best-practice algorithm for generating a multi-decadal record for NO2
and helped to characterise the uncertainties of each retrieval sub-step.
Evaluation of spectral fitting approaches
NO2 spectral fitting approaches by BIRA-IASB, IUP Bremen, KNMI, and
MPI-C were compared in two rounds, with emphasis on OMI and GOME-2, for
common (as much as possible identical) settings for the same level-1 data,
preferred retrieval settings defined by each group.
The intercomparison comprised 4 full days in winter and summer and early and late
in the mission in order to investigate the agreement of retrieval codes with
respect to seasonal and instrumental changes. Table 3 shows the details of
the spectral fitting retrieval code from the four participating institutes.
The retrieval algorithms are based on the same principles, but have been
implemented differently and use different software packages. The KNMI code
applies a wavelength shift prior to the DOAS fit and does not include an
intensity offset in the intensity-fitting model. The common settings are
listed in the caption of Table 3.
Overview of OMI SCD retrieval codes from the QA4ECV consortium's
institutes. The common settings used for round 1 were a 405–465 nm fitting
window, polynomial degree of 4, and inclusion of O3, NO2,
O2-O2, and H2O, Ring cross sections, use of mean solar
irradiance as reference spectrum. The cross sections have been convolved with
the OMI slit function for each row separately.
InstituteRetrievalCodeMethodWavelengthcalibrationIntensity offsetSpike removalReferenceBIRA-IASBQDOASCOptical depth, non-linearleast squares regression(Levenberg–Marquardt)Via Fraunhofer atlas,and shift and squeezeYesYesFayt and VanRoozendael (2001)IUP BremenNLINPASCAL/DELPHIOptical depth, non-linearleast squares regression(Levenberg–Marquardt)Via Fraunhofer atlas,and shift and squeezeYesYesRichter (1997)KNMIOMNO2A v2CIntensity fit, non-linearleast squares regression(Levenberg–Marquardt)Via Fraunhofer atlas,and shiftNoYesvan Geffen et al. (2015)MPI-CMPI-CMATLABOptical depth, non-linearleast squares regression,shift and squeeze accounted for by pseudo-absorbersVia Fraunhofer atlas,and shift and squeeze (non-linear)YesYesBeirle etal. (2013)
1 An optical depth fitting model is of the form lnIλI0λ=-∑iσiλNs,i+∑jajλj with I(λ) the radiance, and
I0(λ) the irradiance spectrum, σi(λ) the
absorption cross-section spectrum of trace gas i, Ns,i the fitting
coefficient, or slant column density of trace gas i, and aj the
coefficients of a low-order polynomial. 2 An
intensity-fitting model is of the form Iλ=I0λe-∑iσiλNs,i+∑jajλj.
The common settings intercomparison of OMI NO2 SCDs for all orbits
on 2 February and 16 August 2005, 4 February, and 4 August 2013 showed very
good agreement between the different algorithms. The correlation between SCDs
from each pair of retrieval codes is always > 99.8 % for all
OMI orbits within the 4 selected days. The correlation is slightly less (but
still > 99 %) between the KNMI code and the other three codes,
suggesting that algorithms agree in capturing the full dynamical range of
NO2 SCDs. The remaining differences appear over background regions
and can be attributed to using a non-linear intensity-fitting model instead
of a linear optical density fit (resulting in NO2 SCD differences
over the oceans up to 1 × 1015 molec. cm-2; see Fig. 3)
and to including or excluding an intensity-offset term in the set of fit
parameters (differences up to 1 × 1015 molec. cm-2,
reducing contrast between bright and dark scenes). Retrieval on optical
densities has the advantage of being a linear fit and has traditionally been
used in DOAS applications. Fitting intensities has the advantage of a more
transparent treatment of the Ring effect and has been applied in operational
OMI data retrieval (van Geffen et al., 2015). While none of the two
approaches is better by definition, the results differ in particular in
combination with the offset correction applied. Based on these outcomes, it
was recommended to use optical density fitting and include the intensity
offset in the QA4ECV fitting model, even though the exact physical meaning of
this term is not entirely clear. Including the intensity-offset term appears
to account for spectral signatures originating from vibrational Raman
scattering in open water (Oldeman, 2018) and associated incomplete Ring
corrections, and prevents O3 misfits over water and over land.
Excluding the intensity-offset term results in larger NO2 SCD
uncertainties and in (spurious) spatial patterns in the O3 SCDs
that resemble the spatial patterns in TOA reflectance. In QA4ECV, the
spectral fitting is approximated by Eq. (2) in Zara et al. (2018) (QDOAS),
and a variation thereof for NLIN. For more details see QA4ECV Deliverable
4.2, Sect. 2.3 (Müller et al., 2016).
OMI NO2 slant column differences between KNMI intensity
(KNMI_NL) and optical density fit (KNMI_L) (a) and IUPB fit
including (IUPB(w)) and excluding the intensity offset (IUPB(w/o))
(b). Data are from 2 February 2005. The units of the colour bar in
both panels are 1015 molec. cm-2.
In round 2, each institute applied preferred settings to retrieve OMI
NO2 SCDs for the same set of days. The KNMI settings are identical
to those in round 1 (Table 3). Relative to the common settings, IUP Bremen used the
425–497 nm fitting window and included a signature for sand absorption
(see Richter et al., 2011) in the fitting model, BIRA-IASB applied a
425–460 nm window and included both sand and CHO-CHO signatures, and MPI
applied a 431–460 nm window and excluded liquid water absorption from the
fit. The intercomparison of preferred settings for SCDs again showed very good
agreement between the algorithms. The correlation between the different pairs
is > 98 %, and the average differences between the different
sets are < 1 × 1015 molecules cm-2. The largest
offset (+0.9 × 1015 molecules cm-2) appears between
KNMI and IUP Bremen (Fig. 3a). The higher KNMI SCDs are explained by the
intensity fit used by KNMI and by the relatively large difference
in the centre wavelengths of the fitting window between these algorithms (435 nm
for KNMI, 461 nm for IUP Bremen). Between 405 and 435 nm, the O3
optical thickness is smaller, and photon paths through the stratosphere are
slightly longer than in the 435–500 nm spectral region, located in the
flanks of the Chappuis band. DAK simulations indeed show 1.5 % higher air
mass factors at 405 nm than at 500 nm (Fig. 4b). For the majority of SCDs
retrieved over unpolluted regions, the use of an intensity fit, together with
the bluer fitting window, explains the differences between the KNMI and IUP
Bremen retrievals. It was not possible to point out a clear winner among
the different fitting approaches, but including an intensity offset and
liquid water absorption in the fit model reduced fitting residuals and
improved NO2 and O3 fit results. NO2 SCDs are
most sensitive to the fitting approach, i.e. intensity fit or optical density
fit.
(a) Correlation plots of IUP Bremen (425–497 nm fitting
window) and KNMI (405–465 nm) NO2 slant columns retrieved using
preferred fit settings for OMI orbit OMIL2_2005m0202t0339, including only
pixels with SZA < 88∘ and
intensity < 1.1 × 1014. (b) Wavelength
dependency of the total air mass factor for a scenario with
SZA = VZA = 30∘ (geometrical AMF = 2.31), as calculated
with DAK for a midlatitude standard atmospheric profile with a total
NO2 column of 5.9 × 1015 molecules cm-2
(mostly situated in the stratosphere) (red curve), and for the same
midlatitude standard atmospheric profile but now with absorption by both
NO2 and O3 (total column of 322 DU, purple curve).
The comparisons of the fitting approaches led to a number of clear
recommendations for spectral fitting of NO2 for the QA4ECV record.
A complete list can be found in QA4ECV Deliverable 4.2 (Müller et al.,
2016). We highlight the most important ones here:
An intensity-offset correction should be included.
Given the sensitivity to selecting intensity fit or optical density fit
(systematic bias up to 1 × 1015 molecules cm-2), it is
recommended to use one and the same fit model for all sensors.
For the 405–465 nm fitting window, the absorption spectrum of liquid water
should be included (not necessary for the smaller windows)
Together with the recommendations driven by level-1 data quality
considerations shown in Table 3, this led to the definition of spectral
fitting of NO2 and data processing from GOME, SCIAMACHY, OMI, and
GOME-2 as summarised in Table 4. Here, the rationale was to maintain as much
as possible the same fit approach and fit settings for GOME-2, SCIAMACHY, and
GOME as for OMI (for details see Table 2 in Müller et
al. (2018).
For the morning sensors GOME-2, SCIAMACHY, and GOME, tests were done to
evaluate the consistency between results of the spectral fitting approaches,
since some settings such as the fitting window had to be different in order
to avoid SCIAMACHY and GOME features for wavelengths < 425 nm
interfering with the spectral fit. The results of these tests are reported in
the QA4ECV Deliverable document 4.5 (Müller et al., 2018), and we summarise
them here. Monthly mean normalised NO2 SCDs from GOME-2(A) and
SCIAMACHY agreed very well in space and time despite the differences between
the instruments in terms of coverage, pixel size, and fitting window
(Figs. 17 and 18 in Müller et al., 2018). Because GOME suffers from a
diffuser plate artefact emerging in the irradiance files, we used daily
radiance spectra obtained over the Pacific Ocean as a substitute for the
irradiance spectrum. The region over the Pacific is largely free of
tropospheric NO2. We then determined the offset correction as the
difference between the normalised SCD values from SCIAMACHY and GOME between
August 2002 and June 2003, when both instruments were operational over the
reference sector. It amounts to 1.48 × 1015 molec. cm-2.
A subsequent matching of the corrected GOME stratospheric columns to the
SCIAMACHY stratospheric columns over the reference region showed that the
robustness of the correction is excellent, with only small deviations
(±1014 molec. cm-2) between GOME and SCIAMACHY for the period
of overlap (Fig. 20 in Müller et al., 2018).
Recommended settings for the QA4ECV NO2 spectral
fitting for the retrieval of NO2 slant columns from GOME,
SCIAMACHY, OMI, and GOME-2(A) for generating a multi-decadal data record for the
period 1995–2017.
1 Averaged for the area enclosed by 160–260∘ E,
10∘ S–10∘ N. The offset has been determined from a
comparison with coincident SCIAMACHY SCDs (2002–2003).
2 GOME is the only sensor that requires an undersampling and
eta (polarisation) correction because the spectral sampling is too
coarse for the full width at half maximum of the GOME slit function (Chance
et al., 2005) and the polarisation structure needs to be accounted for. For
the other sensors this is not needed.
Evaluation of stratosphere–troposphere separation
We compared stratospheric correction approaches by IUP Bremen, KNMI, and
MPI-C to establish best practices for this algorithm step. The stratospheric
correction approach from IUP Bremen is based on scaling model-simulated
(B3dCTM model) stratospheric vertical columns to match satellite observations
over the remote Pacific (Hilboll et al., 2013). In the KNMI approach,
NO2 SCDs are assimilated in the TM4 model, so that model
simulations of stratospheric NO2 columns agree well with the
retrieved slant columns over regions away from strong tropospheric pollution
(Dirksen et al., 2011). MPI-C uses a modified reference sector approach
called STREAM (Beirle et al., 2016). This approach estimates the
stratospheric vertical columns from retrievals over regions where
tropospheric NO2 is assumed to be negligible and over regions with
high clouds, where the tropospheric column is shielded. The derived
stratospheric field is then smoothed and interpolated globally based on the
assumption that the spatial pattern of stratospheric NO2 does not
feature strong gradients.
The intercomparison of stratospheric correction approaches focused on 2
individual days (1 January and 19 July 2005) and 2 monthly means (January and
July 2005). This comparison should be regarded as a “preferred settings”
round, where SCD inputs were identical, but the stratospheric AMFs and
methods used to estimate the stratospheric NO2 columns varied between
the groups. We evaluated the success of the stratospheric corrections via
checks on the smoothness of stratospheric patterns and on the plausibility
of the tropospheric residues (defined as Nv-Nv,strat)
over remote regions where values are expected to be low and not strongly
negative. The comparisons (Sect. 2.4 of QA4ECV Deliverable 4.2, Müller et
al., 2016) indicated that the different schemes showed similar stratospheric
NO2 columns and tropospheric residues and each of the approaches
would be appropriate for use in the QA4ECV NO2 algorithm. The
quantitative differences between the stratospheric NO2 columns were
generally smaller than 0.5 × 1015 molecules cm-2, a
number that can be regarded as an upper limit for the structural
uncertainty in the stratospheric estimate, but the patterns also revealed
that IUP Bremen and KNMI stratospheric NO2 columns were biased high
at high solar zenith angles in the winter hemisphere. In Lorente et
al. (2017), we attributed this bias to the SCIATRAN and DAK radiative
transfer models not fully accounting for the sphericity of the atmosphere in
describing photon transport after backscattering. The McArtim model does
account for the sphericity of the atmosphere for both incoming and
backscattered light, resulting in lower stratospheric AMFs, especially for
extreme solar zenith angles.
The KNMI data assimilation was selected as the default approach for
estimating the stratospheric NO2 column in the QA4ECV algorithm.
This ensures consistent knowledge of the state of the atmosphere
(NO2 and temperature profiles, stratospheric dynamics) derived from
the same model that predicts the a priori tropospheric NO2 profile
shape required by the tropospheric AMF calculation. We decided to update the
model framework for assimilation from TM4 to TM5-MP (Williams et al., 2017).
Moreover, the data assimilation approach has incorporated a correction for
sphericity via McArtim, as described in Lorente et al. (2017). Retrieval
results point out that the stratospheric AMFs, together with improvements in
the data assimilation scheme, lead to much fewer negative tropospheric
columns for retrievals at extreme viewing geometries, also at midlatitudes.
As a second option, the consortium selected MPI-C STREAM as a complementary
algorithm for stratospheric NO2 estimates to be included in the
QA4ECV data product. STREAM is based on the measurements alone without
involving models. This allows QA4ECV data users to switch approaches, which
may be beneficial under certain circumstances. Especially in situations with
strong stratospheric NO2 gradients, such as near the polar vortex,
assimilation is the preferred approach. It has been shown that the data
assimilation captures the strong spatial gradients occurring near the vortex
(Dirksen et al., 2011), whereas the STREAM method by design results in
zonally smooth structures in those regions. STREAM could be a useful
alternative to data assimilation for studies into weak NOx
sources, such as emissions from soil, ships, and small, isolated
anthropogenic sources. The strength of STREAM is that it is based on
measurements and does not rely on models. Data assimilation is potentially
somewhat vulnerable to misinterpreting tropospheric contributions as
stratospheric NO2, so that STREAM could be used in areas away from
strong stratospheric gradients (where the zonally smooth structure of the
stratospheric field is of little consequence). Furthermore, the differences
between the two methods are useful as a measure of structural uncertainty in
the stratospheric correction, beyond the typical uncertainties of
0.2 × 1015 molecules cm-2 derived from the observation–forecast statistics of the assimilation scheme (Dirksen et al., 2011).
Regions of enhanced structural uncertainty are relevant, especially over areas
with small tropospheric NO2 enhancements, such as from outflow of
continental pollution over oceans, shipping lanes, and over areas with soil
NOx emissions.
As an example, Figure 5 shows OMI stratospheric NO2 estimates from
both the data assimilation and STREAM approach for the QA4ECV v1.1 product on
2 February 2005. The upper panel illustrates that the latitudinal gradients
in NO2 between the data assimilation and STREAM agree reasonably
well. It is evident that the data assimilation approach captures more
variability along a zonal band, resulting on this day in lower stratospheric
NO2 over North America and Europe and higher amounts over north-eastern
Asia than in the STREAM method. The differences are up to
1 × 1015 molec. cm-2, such that they have a substantial impact on the
tropospheric column retrievals.
Stratospheric NO2 columns from OMI on 2 February 2005,
estimated with the data assimilation method (a) and with the STREAM
method (b). Panel (c) shows the differences between the
stratospheric NO2 estimates.
Evaluation of air mass factor calculations
We performed a comparison of approaches to calculate AMFs for NO2
and mapped the uncertainties associated with these approaches. Much of this
comparison has been reported in Lorente et al. (2017) and in Sect. 2.5 of
QA4ECV Deliverable 4.2 (Müller et al., 2016), so we give only a brief
summary here. First, we compared radiative transfer models from the
consortium (LIDORT, SCIATRAN, DAK, McArtim) for their top-of-atmosphere
reflectances and their capacity to compute vertically resolved or box
AMFs. The agreement between reflectances from the four models at 440 nm (and
also at 340 nm) was excellent. Mean relative differences between models were
generally small (< 1 %), with the exception of high solar zenith
angles (> 80∘), where systematic differences with the
McArtim model amount to up to 10 %. McArtim is the only model that
simulates radiative transfer in full sphericity for direct and
diffuse light (Deutschmann et al., 2011). Other differences, such as different
layering schemes, polarisation description, refractive index, and Rayleigh
scattering cross-section spectrum, only lead to small differences
(< 1 %) between the models.
To establish the QA4ECV NO2 algorithm settings, we selected the
appropriate wavelength for calculating the NO2 box AMFs. We
investigated the wavelength dependency of the NO2 AMFs for
retrieval scenarios with substantial tropospheric pollution
(Nv,trop= 16 × 1015 molec. cm-2) and
considered that the AMF calculated at a single wavelength should be
representative of the fit window average AMF. Tropospheric NO2
AMFs were calculated between 400 and 500 nm with 1 nm steps. Figure 6 shows a
distinct increase in AMF with wavelength. This increase reflects the
increasing transparency of the lower troposphere towards the green part of
the spectrum where Rayleigh scattering is weakening. In general, tropospheric
AMFs increase by 0.2 %–0.3 % per nanometre redshift. The purple, blue, and
light-blue lines show the AMF averaged over all spectral points in three relevant
fitting windows used within QA4ECV and by individual groups.
NO2 tropospheric air mass factor (black) as a function of
wavelength computed with DAK for a polluted boundary layer for a specific
viewing geometry (θ= 60∘,
θo= 45.6∘). Horizontal lines show averaged
multi-wavelength AMF for different fitting windows (purple, 425–450 nm,
blue 405–465 nm and light blue 425–497 nm). The grey line shows
NO2 absorption cross section from Vandaele et al. (1998) at 220 K.
A midlatitude standard atmosphere was used including O3. The AMF
was computed for a polluted boundary layer with
16 × 1015 molec. cm-2, without aerosols, a boundary
layer height of 1 km and surface albedo of 0.05. The non-smooth behaviour of
the black line is because the spectral resolution of the AMF is not
sufficient to resolve the NO2 cross section used in the
calculation. If a constant cross-section value is used in the RTM for
calculating TOA reflectance, the increasing AMF with wavelength would be
spectrally smooth.
We saw in Fig. 4 that total NO2 AMFs decrease weakly with
wavelength (-0.01 % nm-1 redshift). Figure 6 shows that
tropospheric NO2 AMFs increase with wavelength
(+0.2–0.3 % nm-1). This difference can be understood from
Rayleigh scattering, occurring mostly in the lowest kilometres of the
troposphere. The bulk scattering increasingly screens NO2 in the
boundary layer towards the UV, so that tropospheric AMFs are smallest for
shorter wavelengths. For the fitting windows considered for QA4ECV
NO2 retrievals (425–465 and 405–465 nm), we recommend
calculating the NO2 AMF at 437.5 nm for all sensors. The blue and
purple lines in Fig. 6 indicate that 437.5 nm is a representative wavelength
used to calculate the NO2 AMF. 437.5 nm is reasonably near to the
centre wavelength of both windows (435 and 445 nm respectively) and the
437.5 nm AMF is within 2 % of the average AMF for both windows.
Uncertainties related to the exact choice of AMF wavelength calculation are
much smaller than other AMF uncertainties, such as clouds, albedo, trace gas
and aerosol profiles, as discussed below and in Lorente et al. (2017).
We compared altitude-resolved AMFs and tropospheric AMFs calculated with the
four different radiative transfer models. We found that the agreement is very
good (within 3 % and 6 % respectively) if identical ancillary data
(surface albedo, terrain height, cloud parameters, and trace gas profile) and
cloud and aerosol corrections are being used. This shows that the choice of
RTM (radiative transfer modelling) for
calculation of the tropospheric AMF introduces a modest uncertainty of no
more than 6 %, which is intrinsic to the calculation method and cannot be
avoided.
To assess the full impact of preferred settings and methods for AMF
calculations, we organised a round robin comparison. Six groups joined this
round robin, each using their preferred setting to calculate the tropospheric
AMFs. Besides the QA4ECV-partners KNMI/WUR, BIRA-IASB, and IUP Bremen,
NASA GSFC, Leicester University, and Peking University also participated. The
six groups used widely different calculation methods (RTMs, temperature, cloud
and aerosol corrections) and preferred ancillary data on albedo, terrain
height, NO2 profile, etc. (Müller et al., 2016). The ensemble
mean AMF served as a reference with which to compare the AMFs by the
individual groups. The round robin exercise focused on China because it
provides challenging retrieval conditions and Peking University only
calculates AMFs over that region. The overall spatial pattern of AMF values
was well reproduced by all groups. AMFs generally agree to within 10 %
over unpolluted areas but show differences of up to 40 % with respect to
the ensemble mean over polluted regions in eastern China and Korea. These
differences can be traced back to differences in the preferred surface
albedo, clouds, and a priori NO2 profiles used in the AMF
calculation. It is not possible to identify the single most important forward
model dependency for the AMF calculation. The analysis in QA4ECV Deliverable
4.2 (Müller et al., 2016) and in Lorente et al. (2017) suggests that
accurate knowledge of surface albedo, clouds, and a priori NO2
profiles are of similar importance, and their interplay, in combination with
the choices for cloud and aerosol correction methods, is driving the
structural uncertainty in the NO2 AMFs.
Based on the results from the comparisons discussed above, the following
recommendations for calculating QA4ECV NO2 AMFs were made:
Calculate the NO2 AMFs at 437.5 nm for all instruments.
Apply the independent pixel approximation for cloud correction, but also
include clear-sky AMFs in the product.
Use cloud information (cloud fraction, cloud pressure) from FRESCO+ for
GOME, SCIAMACHY, GOME-2A (Wang et al., 2008) and OMCLDO2 for OMI (Veefkind et
al., 2016). These have been derived using the same physical principles as in
the AMF calculation.
Apply implicit aerosol correction (via the cloud correction). This correction
is effective in most retrieval scenarios with moderate aerosol pollution.
When accurate, observation-based aerosol information becomes available from
ECMWF CAMS or NASA GMAO. Explicit aerosol corrections will be
considered.
Use surface albedo climatologies (as close as possible to the 437.5 nm AMF
wavelength) consistently with the ones used in the cloud retrievals. For GOME,
SCIAMACHY, and GOME-2A, this is the albedo climatology from Tilstra et
al. (2017), and for OMI it is the updated 5-year climatology (Kleipool et al.,
2008).
Use the DEM_3KM pixel-average terrain height.
Use spatially interpolated (to pixel centre) NO2 profiles simulated
by TM5-MP at 1∘× 1∘. TM5-MP is the model used for
the data assimilation of NO2 SCDs to estimate the stratospheric
contribution (Sect. 5.2).
The QA4ECV NO2 product contains an algorithm uncertainty estimate
associated with each individual pixel's tropospheric NO2 column.
This estimate is calculated theoretically via uncertainty propagation based
on the principal retrieval equation (Eq. 1):
σ=σNSMtr2+σNs,stratMtr2+NS-NS,stratσMtrMtr22.
The uncertainty propagation accounts for spectral fitting uncertainties
(σNS) and contributions from uncertainties in a priori
and ancillary data required for calculating the stratospheric NO2
background (σNs,strat) and the AMF
(σMtr). The uncertainty in the tropospheric AMF, or AMF
covariance is written as follows:
σMtr2=∂M∂AsσAs2+∂M∂fclσfcl2+∂M∂pclσpcl2+0.1Mtr2+2∂M∂As∂M∂fcl,
where ∂M∂As represents the local
sensitivity of the air mass factor to surface albedo As,
σAs the best estimate of the uncertainty in the surface
albedo, and so on. The fourth term on the right-hand side represents the
contribution from the uncertainty in the a priori profile shapes and is
tentatively approximated as 10 % of the tropospheric AMF. This term is
absent when using the averaging kernel in satellite data applications (Eskes
and Boersma, 2003), which removes the dependence on the a priori profile. The
last term represents the contribution from the error correlation between cloud
fractions and surface albedo
〈ϵfclϵAS〉; surface
albedo influences AMF directly, and indirectly because cloud fractions are
sensitive to surface reflectance (see Eqs. 20 and A2 in Boersma et al., 2004
and Lorente et al., 2018 for more detail). As
〈ϵfclϵAS〉 and
∂M∂fcl are negative and ∂M∂As is positive, this last term gives a positive
contribution to σMtr2.
The uncertainty σ should be interpreted as the best guess of the
retrieval uncertainty for one specific measurement. This uncertainty contains
random and systematic error components, and the different systematic error
components (due to errors in profile shape, surface albedo, etc.) each have
their own spatial and temporal scales. Therefore, when averaging over multiple
pixels (spatially) or over time, part of the error will cancel out or be
smoothed, but (an unknown) part of the systematic error will remain even
after averaging; see Boersma et al. (2016).
We recommend using Eq. (4) below to estimate the uncertainty
σo for spatially or temporally averaged data. This method takes the
area-weighted (statistical) retrieval uncertainty σ and then
accounts for a partial correlation in the errors between pixels as in Eskes
et al. (2003):
σ0=σ1-cn+c,
with c as the error correlation between the n retrievals. In Boersma et
al. (2016), c=0.15 is proposed based on the consideration that errors in
surface albedo, clouds, a priori NO2 profile, and aerosols (or lack
of description thereof) are typically correlated at the spatio-temporal scales
of moderate resolution (global) models, i.e. down to
0.5∘× 0.5∘ and over 1 month (for example the
surface albedo is from a monthly climatology). Equation (4) with c=0.15
implies that the spatially or temporally averaged uncertainty cannot reduce
to below 39 % of the level of typical single-pixel uncertainties
(σ), even when many observations are available.
Algorithm uncertainties and quality flags
Table 5 gives an overview of the most important uncertainties and the quality
flags of QA4ECV NO2 provided in the data product. Note that the
uncertainty estimates and quality flags provide clearly different types of information
to the user. The uncertainty characterises the dispersion of the
NO2 column, given the value of the measured column, and our best
understanding of the retrieval process. Quality flags indicate whether the
retrieved value and the uncertainty estimate have been obtained under
conditions where they are expected to be valid.
Overview of the main uncertainty estimates and quality flags
included in the QA4ECV NO2 ECV precursor product. The third column
indicates whether the estimate is unique for that pixel, derived from a global
estimate, or is a blend of individual and global estimates.
NameMeaningPer pixel or globalSymbolTropospheric NO2 column uncertaintyAlgorithm uncertaintyestimate of the tropospheric NO2 columnPer pixelσTropospheric NO2 column uncertainty when averaging kernel is appliedAlgorithm uncertainty estimate, as above, but contribution from profile uncertainty removedPer pixelσAKStratospheric NO2 column uncertaintyGlobal estimate of uncertaintyin the stratospheric VCDGlobalσNstratUncertainty of the sum of the tropospheric and stratospheric vertical NO2 columnsAlgorithm uncertainty estimate of the total NO2columnPer pixelNO2 SCD uncertaintyUncertainty estimated fromthe DOAS spectral fitting of NO2Per pixelσNSSlant-column-related uncertainty of the NO2 tropospheric vertical columnFirst term on the right-handside of Eq. (2)Per pixelσNSMtrStratospheric-column-related uncertainty of the NO2 tropospheric vertical columnSecond term on the right-handside of Eq. (2)Mix of pixel and globalσNs,stratMtrTotal tropospheric AMF-related uncertainty of the tropospheric NO2 vertical columnThird term on the right-handside of Eq. (2).Per pixelNS-NS,stratσMtrMtr2Surface-albedo-related uncertainty of the tropospheric vertical NO2 columnContribution to the uncertaintyof uncertainties in the surface albedo in the troposphericAMF*Per pixel∂M∂AsσAsNvMtrCloud-fraction-related uncertainty of the tropospheric vertical NO2 columnContribution to the uncertaintyof uncertainties in the cloud fraction in the tropospheric AMFPer pixel∂M∂fclσfclNvMtrCloud-pressure-related uncertainty of the tropospheric vertical NO2 columnContribution to the uncertaintyof uncertainties in the cloudpressure in the tropospheric AMFPer pixel∂M∂pclσpclNvMtrTM5-profile-related uncertainty of the tropospheric vertical NO2 columnGlobal estimate of the contribution to the uncertainty of uncertaintiesin the TM5 NO2 profile in the tropospheric AMFGlobal0.1 NvProcessing error flagFlag indicating whether theprocessing was successful (0)or failed (-1)Per pixelProcessing quality flagsFlags indicating conditionsthat affect the quality of the retrievalPer pixel
* The term between brackets indicates
the AMF uncertainty caused by uncertainty in the forward model parameter,
here surface albedo. These terms are the same as in Eq. (3) in this work and
Eq. (12) in Boersma et al. (2004). To arrive at the contribution of the
forward model parameter uncertainties to the NO2 column uncertainty,
we ratio the AMF uncertainty contribution by the AMF itself and multiply it
with the actual tropospheric NO2 column.
Evaluating the sub-step uncertainty estimates
An innovative aspect of the QA4ECV project is the evaluation of the
uncertainty estimates of retrieval sub-steps against independent estimates
of the same metric and structural uncertainties.
Evaluation of NO2 SCD uncertainties
We compared the DOAS uncertainty estimates (σNS) from the
spectral fitting algorithm against independent estimates obtained from the
spatial variability of an ensemble of DOAS SCDs over areas with little
geophysical variability using a statistical approach (Boersma et al., 2007).
Our SCD uncertainty evaluation is described in detail in QA4ECV Deliverable
5.5 (Boersma et al., 2017a) and in Zara et al. (2018) for OMI and
GOME-2A, and we summarise the results here. For both instruments, we found that
the improved QA4ECV OMI NO2 retrieval shows smaller uncertainties
than other OMI algorithms and good agreement between the DOAS and statistical
SCD uncertainties. This suggests that the recommendations made in Sect. 5.1
and in QA4ECV D4.2 (Müller et al., 2016) have improved the spectral
fitting of NO2 such that the typical mission-average SCD
uncertainties for both instruments amount to 0.7–0.8 × 1015
(was ∼ 1.0 × 1015) molec. cm-2. For OMI, this
uncertainty is dominated by random contributions from propagation of
measurement noise, but we also noticed a 30 % systematic contribution
from stripe effects. For OMI, the trend in SCD uncertainties was small
(< 2 % yr-1) in line with the known radiometric stability
of the instrument (Schenkeveld et al., 2017), but for GOME-2A, the
NO2 SCD uncertainties increased by 8 % yr-1 until
September 2009 and after heating the instrument by
< 3 % yr-1 over 2009–2015. The structural (systematic)
uncertainty, estimated from the differences between NO2 SCDs
calculated with different but equally plausible fitting methods (with or
without intensity-offset correction; see Sect. 5.1), is larger the theoretical and statistical estimates than but of a
similar magnitude. Table 6 gives
an overview of the various estimates of uncertainty for the NO2
SCDs.
Evaluation of uncertainties in the stratospheric correction
The uncertainty of the stratospheric NO2 vertical column in QA4ECV
NO2 product is based on a global statistical analysis of results
from the data assimilation procedure and documented as
0.2 × 1015 molecules cm-2 (Dirksen et al., 2011). The
assimilation predicts stratospheric NO2 columns from an
observation-constrained (analysed) start field and TM5-modelled transport and
chemistry. The average discrepancies between the 24 h forecast and actual
satellite-observed NO2 slant column fields over pristine areas are
regarded as a measure of the uncertainty in the stratospheric NO2
field. In QA4ECV Deliverable 5.5 (Boersma et al., 2017a), we verified that
the observation minus forecast (O – F) assimilation statistics over the Pacific
are indeed consistent with an uncertainty estimate of
0.2 × 1015 molecules cm-2 for the stratospheric column.
To further evaluate the estimate of the stratospheric column uncertainty, we
compare the QA4ECV data assimilation and STREAM OMI stratospheric
NO2 column estimates for 2 February 2005. There are considerable
methodological differences between the data assimilation and STREAM
techniques. Yet the data assimilation and STREAM stratospheric NO2
distributions agree to a reasonable extent, with data assimilation
stratospheric columns generally smaller and their spatial features sharper
than in STREAM. The TM5-MP assimilation approach distinguishes stratospheric
NO2 from free-tropospheric background contributions, while STREAM
does not do this. This may be the main reason for the structurally lower values
in the assimilation. This is illustrated in Fig. 7, which shows the
meridional variability in the stratospheric NO2 column from data
assimilation and from STREAM along 40∘ N on 2 February 2005. Between
75–125∘ W over the United States and between 0 and 40∘ E
(Europe), the data assimilation stratospheric columns values are
0.2–0.5 × 1015 molecules cm-2 lower than the STREAM
values. Over eastern Asia (100–140∘ E), data assimilation and STREAM
agree to within ±0.3 × 1015 molecules cm-2. These
differences reflect the structural uncertainty in stratospheric (vertical)
NO2 columns, arising when different retrieval methodologies are
applied to the same satellite observations, and both uncertainty estimates
are included in Table 6.
Meridional average QA4ECV OMI NO2 column averaged over
39–41∘ N on 2 February 2005. No cloud radiance, albedo, or AMF
filtering have been applied. Data assimilation and STREAM stratospheric
columns are indicated in the black and green lines; the total slant columns
divided by the geometric AMF are light blue. Both data assimilation and STREAM
stratospheric column estimates are included in the QA4ECV NO2
product.
Evaluation and breakdown of uncertainties in the air mass
factors
The uncertainty in the tropospheric AMF is calculated via the uncertainty
propagation from Eq. (3). The contribution of each parameter to the overall
AMF uncertainty depends on the specific observation conditions for each
pixel. The air mass factor sensitivities (e.g. ∂M∂As) describe the sensitivity of the AMF to changes in the local
parameter value, evaluated around the specific value for the parameter at the
pixel. The uncertainties in the cloud parameters (σfcl,
σpcl), surface albedo (σAs), and the a
priori profile shape have been estimated from the literature or derived from
comparisons with independent data. For QA4ECV OMI NO2, we use an
uncertainty in the surface albedo of 0.015, based on various comparisons of
albedo databases (e.g. Boersma et al., 2011), uncertainties of 0.025 and
50 hPa in the OMI O2-O2 cloud fraction and cloud
pressure estimates, respectively, based on recent improvements in the cloud
algorithm (Veefkind et al., 2016), and a 10 % contribution from
NO2 profile uncertainty. The latter is based on comparing AMFs
calculated with simulated a priori profiles to AMFs calculated with measured
NO2 profiles from aircraft and lidar (e.g. Hains et al., 2010 and
references therein).
Apart from the overall AMF uncertainty estimate, the QA4ECV NO2 ECV
precursor data product also provides the individual contributions from the
cloud parameters, surface albedo, and a priori profile shapes. Figure 8
presents the relative monthly average tropospheric AMF uncertainties and
their individual contributions from surface albedo, cloud, and profile
uncertainties (not shown because they have been set at the 10 % level)
for OMI throughout 2005 over Europe, the United States, China and
Johannesburg, South Africa, regions polluted with NO2. The largest
contribution to AMF uncertainty is from surface-albedo–cloud cross term
(10 %–20 %), with substantial surface albedo
(±10 %). In winter the uncertainty in cloud pressure
is a substantial contributor in Europe and China. The strong surface-albedo–cloud fraction cross term 2∂M∂As∂M∂fcl〈ϵfclϵAS〉 can be understood from the strong sensitivity of the cloud
fraction to the surface albedo, especially when cloud fractions are small
(see Appendix in Boersma et al., 2004). The overall tropospheric AMF
uncertainties are estimated to be 20 %–25 %, comparable to earlier
estimates for GOME tropospheric NO2 presented in Boersma et
al. (2004).
Average (single-pixel) QA4ECV OMI tropospheric AMF uncertainty
(black line) estimated for Europe (40–55∘ N,
10∘ W–15∘ E), United States (35–45∘ N,
100∘ W–75∘ W), China (35–45∘ N,
110–140∘ E), and Johannesburg (24–28∘ S,
26–30∘ E) in 2005. The coloured lines indicate the contribution to
AMF uncertainty from the various inputs to the AMF calculation indicated in
the legend. The contribution from a priori profile uncertainty is assumed to
be constant at 10 % of the AMF uncertainty (not plotted).
We quantified the structural uncertainty in tropospheric AMFs by comparing an
ensemble of different AMF calculation methods and parameter assumptions over
eastern China, a region with high amounts and a complex mixture of aerosols,
clouds, and NO2 pollution (Lorente et al., 2017). Retrieval groups
used their preferences for ancillary data and for cloud and aerosol
corrections. The outcome of the comparisons suggested systematic AMF
differences of up to 15 % in summer and 40 % in winter between the
groups. We consider these structural uncertainty estimates to be
conservative, as they have been calculated for the particularly challenging
retrieval regime of eastern China in 2005. Including the structural
uncertainties in the overall budget, as done for the QA4ECV HCHO ECV
precursor product (De Smedt et al., 2017), would bring tropospheric AMF
uncertainties to ±30 % in summer and ±50 % in winter.
Comparison of uncertainty estimates
for the main QA4ECV OMI NO2 retrieval steps. The SCD and
stratospheric SCD uncertainties are representative of all possible retrieval
scenarios. AMF uncertainties are representative of situations with high
NO2.
a Zara et al. (2018). b Section 5.1 of
this work. c Dirksen et al. (2011) and analysis of data
assimilation observation minus forecast differences QA4ECV Deliverable 5.5
(Boersma et al., 2017a). d Figure 7 of this work.
e Lorente et al. (2017).
Overall uncertainties in tropospheric NO2 columnsUncertainties in single-pixel tropospheric NO2 columns
Here we present estimates of typical algorithm, single-pixel uncertainties
for the QA4ECV NO2 columns in four regions: Europe, United States,
and China, as showcases for typical polluted regions, and the Pacific Ocean as
an example of a remote region, with low, background levels. These uncertainty
estimates should be interpreted as representative of typical, single-pixel
uncertainties encountered by users interpreting the data. We see from Fig. 9
that, over the polluted regions in wintertime, the single-pixel retrieval
uncertainty is dominated by the uncertainty in the tropospheric AMF. In
summer, contributions from uncertainties in the SCD are largest, but there
are comparable contributions from uncertainties in the stratospheric correction
and the tropospheric AMF. On average a single pixel is 35 %–45 %
uncertain in the polluted regions. Over the background region (Pacific
Ocean), we see that the tropospheric NO2 column uncertainty exceeds
100 % and is dominated year-round by the uncertainties in SCD and
the stratospheric column estimate.
Uncertainties in averaged tropospheric NO2 columns
When averaging tropospheric columns over space, uncertainties may be
considerably reduced. For example, over regions such as the Pacific Ocean,
where the uncertainty is dominated by a random SCD error, the tropospheric
column uncertainty will be greatly reduced when averaging over a month or over a
larger region. Over polluted regions dominated by uncertainties in the
tropospheric AMF, averaging will also reduce the tropospheric column
uncertainties, but an unknown systematic component will remain. For both
retrieval situations, we adopt Eq. (4) to account for possible systematic
errors arising from imperfect knowledge of surface albedo, a priori
NO2 profile, clouds, and correlations between these.
Average (single-pixel equivalent) QA4ECV OMI tropospheric
NO2 columns (solid line) and associated total uncertainties (dashed
black line) for Europe (40–50∘ N, 10∘ W–15∘ E),
United States (35–45∘ N, 100–75∘ W), eastern China
(30–45∘ N, 110–140∘ E), and the Pacific Ocean
(35–45∘ N, 160–140∘ W) in 2005. The dashed coloured lines
indicate the contributions to the tropospheric NO2 column
uncertainty from SCD (pink,
σNSMtr), stratospheric
correction (light blue, σNs,stratMtr), and the
tropospheric AMF (purple, NS-NS,stratσMtrMtr2).
In model-column comparisons and in-trend analysis studies, it is often
important to have knowledge of temporally averaged uncertainties. Because the
temporal variability in tropospheric NO2 columns is typically
strong (because of the diurnal cycle, day-to-day variability, weekly cycles,
etc.), this implies considerable variability in day-to-day uncertainties. To
obtain the uncertainty in a monthly mean tropospheric NO2 column
over a certain region, we recommend taking whichever is largest: (a) the
temporally averaged values for σo (Eq. 4), or (b) the standard
deviation of the mean (standard error) of the daily tropospheric
NO2 columns. If there is substantial temporal variability (from
changes in photochemistry, transport events), the standard error will be a
good representation of the uncertainty in the monthly mean tropospheric
NO2 column. Figure 10 shows a comparison of monthly averaged
uncertainties σo and the local standard deviation of the mean
NO2 columns for four small regions
(0.25∘× 0.25∘). The figure confirms that the
averaged uncertainties provide an optimistic estimate of the uncertainty, at
±10 %, in the monthly mean NO2 columns. For the polluted
regions, the standard deviation of the mean is 15 %–30 %, exceeding
the average uncertainties. This illustrates that calculating the uncertainty
in a monthly mean over a small region such as a city is more driven by
sampling limitations than by the intrinsic uncertainty of the retrieval.
Monthly mean single grid-cell QA4ECV OMI tropospheric NO2
columns (solid line), standard deviation of the mean (standard error, dashed
red line), and super-observation uncertainty (σ0, dashed black
line) in 2005 over Amsterdam (52.375∘ N, 4.875∘ E), New
York City (40.875∘ N, 73.875∘ W), Beijing
(39.875∘ N, 116.375∘ E), and the Pacific
(39.875∘ N, 149.875∘ W). Grid cell size of
0.25∘× 0.25∘. Only pixels with cloud radiance
fraction < 0.5 were included in the calculation.
Validation of QA4ECV NO2 columns and uncertainties
As an example of the validation efforts taken within QA4ECV, here we compare
the QA4ECV OMI tropospheric NO2 with independent MAX-DOAS column
measurements in the polluted city of Tai'an, China. We compare OMI pixels
measured within 20 km and 30 min of a MAX-DOAS measurement in Tai'an. We
validate both the QA4ECV v1.1 and the well-established DOMINO v2 product for
reference.
The MAX-DOAS measurements were conducted by Irie et al. (2008) in the Chinese
city of Tai'an in May–June 2006 when pollution levels were substantial. The
instrumentation and retrieval technique have been described extensively in
Irie et al. (2008, 2012). The slant column retrievals have been tested in a
semi-blind intercomparison exercise in Cabauw, the Netherlands, indicating
agreement to within 10 % of other groups (Roscoe et al., 2010).
Uncertainties in the MAX-DOAS NO2 columns are driven by noise, air
mass factor and temperature uncertainties amounting to approximately 15 %
uncertainty. The representative horizontal footprint of the MAX-DOAS
measurement is of the order of 10 km. It was suggested by Irie et al. (2012)
that the spatial distribution of NO2 tropospheric columns around
Tai'an during their observation period was rather homogeneous compared to
other sites used for their validation comparisons. More quantitative
characterisation of this aspect will be discussed below.
We compare OMI NO2 tropospheric columns measured with a pixel
centre within 20 km of the location of the MAX-DOAS instrument in Tai'an
(for some days more than 1 pixel can be matched up with a MAX-DOAS
measurement). This coincidence criterion limits spatial representativeness
mismatches between MAX-DOAS and OMI and is consistent with the spatial
dimensions of the MAX-DOAS (±10 km) and OMI (20–30 km) footprints
(we excluded pixels from the outer four OMI rows). We furthermore require that
the OMI columns were measured within 30 min of the coinciding MAX-DOAS
measurement, have a pixel footprint area < 700 km2, and that
the satellite retrieval was done under mostly clear-sky conditions (cloud
radiance fraction < 0.5), which is in line with recommendations on
the appropriate use of QA4ECV data as documented in the Product Specification
Document (QA4ECV Deliverable D4.6). Earlier studies (e.g. Pinardi et al.,
2017; Drosoglou et al., 2017) found the largest discrepancies between MAX-DOAS
and satellite NO2 columns over strongly polluted regions. Such
discrepancies are at least partly due to spatial inhomogeneity in the
NO2 field around the station location. To quantify the spatial
representativeness of the Tai'an MAX-DOAS site for the OMI pixels included in
the comparison, we calculated the campaign-mean spatial tropospheric
NO2 column distribution (Fig. 11). We then use the ratio of this
campaign-mean column at Tai'an to the campaign-mean column at the location of
the individual OMI pixel to project individual OMI NO2 columns
(NV,p, i.e. what is usually validated) within our criteria to
values more representative of the location of the Tai'an
(NV,T):
NV,T=NV,T‾NV,p‾⋅NV,p.
For example, for a pixel observed directly south-west of Tai'an, where
pollution levels are somewhat higher than directly over Tai'an, the scaling
factor will be smaller than 1. For the coincidence criterion of 20 km used
here, the scaling factors stay close to 1 and modifications do not exceed
1 × 1015 molec. cm-2 (< 20 % of the Tai'an
column; see Fig. S2 in the Supplement).
Campaign mean (30 May–30 June 2006) of the QA4ECV tropospheric
NO2 column distribution over eastern China for clear-sky situations
(cloud radiance fraction < 0.5). The black circle indicates the
location of Tai'an, where Chiba University operated the MAX-DOAS instrument.
One cell corresponds to 0.1∘× 0.1∘. On average
there are 15 satellite pixels per cell used to calculate the campaign mean.
We match each OMI pixel fulfilling the spatio-temporal coincidence criteria
with the corrected MAX-DOAS NO2 columns. By discarding pixels with
effective cloud pressures > 875 hPa (often indicative of aerosol
haze), we find 31 QA4ECV OMI pixels matching 13 independent MAX-DOAS
measurements collected over 7 different days. Figure 12a shows a scatter plot
of QA4ECV vs. MAX-DOAS tropospheric NO2 columns for Tai'an. We find
a bias (mean difference) of -0.15 × 1015 molec. cm-2
(-2 %) and the root mean square deviation is
1.08 × 1015 molec. cm-2 (16 %). Not applying the
scaling factors from Eq. (5) leads to a bias of
-0.47 × 1015 molec. cm-2 (-7 %) and a root mean
square deviation of 1.19 × 1015 molec. cm-2 (18 %).
Using a reduced major axis regression analysis, we find a relationship
between QA4ECV (y) and MAX-DOAS NO2 columns (x) as
y=-0.86 × 1015 molec. cm-2+1.10x
(R2=0.26, n=31). Also including pixels with high effective cloud
pressures (> 875 hPa) leads to a bias of
-0.48 × 1015 molec. cm-2 (-6.6 %, n=37) and a
root mean square deviation of 1.35 × 1015 molec. cm-2
(20 %).
Figure 12b shows the scatter plot of DOMINO v2 vs. MAX-DOAS NO2
columns for Tai'an. There are now 45 DOMINO v2 pixels matching 17
independent MAX-DOAS measurements. This higher number of matches can be
explained from the previous version of the OMI O2-O2
cloud product (Acarreta et al., 2004), used in the DOMINO v2 retrieval,
containing effective cloud pressures that are too low compared to independent
information (Boersma et al., 2011; Veefkind et al., 2016), so that more OMI
pixels pass the selection criteria. The bias for DOMINO v2 is
+0.85 × 1015 molec. cm-2 (+11 %, n=45), with a
root mean square deviation of 2.66 × 1015 molec. cm-2
(35 %).
(a) Scatter plot of QA4ECV OMI vs. MAX-DOAS tropospheric
NO2 columns for Tai'an (China) in May–June 2006. The solid line
shows the result of a reduced major axis regression to the data. Only pixels
measured a cloud radiance fraction < 0.5 and an effective cloud
pressure < 875 hPa, within 20 km and 30 min of a MAX-DOAS
measurement have been selected. (b) Same as (a), but now
for DOMINO v2 vs. MAX-DOAS tropospheric NO2.
The differences between OMI and MAX-DOAS NO2 columns provide an
opportunity to evaluate the uncertainties of the satellite retrievals. This
relies on good knowledge of the MAX-DOAS uncertainties and relatively small
uncertainties associated with the representativeness of MAX-DOAS for the
coincident OMI columns. Assuming that the retrieval errors between OMI and
QA4ECV are independent and follow a normal distribution, we expect that the
distribution of the differences between OMI and MAX-DOAS takes on a Gaussian
form characterised by width σ=σO2+σMD2+σR21/2, with
σO being the uncertainty reported (in the data files) for QA4ECV OMI
NO2 columns, σMD the uncertainty reported for
MAX-DOAS NO2 columns, and σR the uncertainty from
spatio-temporal mismatches between the satellite and ground-based measurement
(Table 7). The mean reported uncertainties (σO and
σMD) are regarded as random errors here (see discussion in
Sect. 2.3 in Boersma et al. (2004) and below). In Fig. 13 we compare the
distribution of differences predicted from the above Gaussian function based
on the uncertainties reported in the OMI and MAX-DOAS data files and a
10 % representativeness difference error (estimated from deviations from
the Tai'an value shown in Fig. S2 in the Supplement) to the actual
observed differences (individual pairs of OMI and MAX-DOAS NO2
column values). We see from Fig. 13 that the differences between OMI and
MAX-DOAS NO2 columns are more narrowly distributed than expected
from algorithm uncertainties and theory, although the sample size is small
(n=31 for QA4ECV, n=45 for DOMINO v2). This holds for QA4ECV differences,
which are 39 % smaller than expected, but also for the DOMINO v2
differences, 16 % smaller than expected over Tai'an. The tighter
distribution of the observed differences implies the following:
The uncertainties in OMI and MAX-DOAS retrievals possess some degree of
correlation (for instance in situations when OMI has a high bias. Also,
MAX-DOAS may be biased high, limiting the magnitude of the differences).
OMI and/or MAX-DOAS algorithm uncertainty estimates are too
conservative.
OMI or MAX-DOAS uncertainties contain an unknown
persistent error component, so that σO or σMD have
been overestimated.
The uncertainties are a combination of the above.
The MAX-DOAS NO2 retrieval technique suffers from some similar
error contributions (a priori NO2 profile shape, aerosols) but it
is also different from the satellite retrieval by design (no albedo or
stratospheric correction dependence, ground-based perspective), so we should
expect some but not a full error correlation. If there was a substantial
systematic and persistent error component to σO or
σMD (and we have no indication for this nor do we know about
its magnitude or sign), we would have needed to reduce our estimates for
σO and σMD in Table 7 and expect a distribution of
the differences that is more narrowly Gaussian and peaking at a typical
systematic difference (or bias). Figure 13 shows a small bias for QA4ECV. We
therefore conclude that the OMI (and MAX-DOAS) retrieval uncertainties
estimates could be too conservative, although our findings are based on a
small sample. In the case of QA4ECV, a reduction of both the OMI and MAX-DOAS
uncertainties by 35 % would be in much better agreement with the
observed differences at the Tai'an station.
Expected and observed differences between OMI and MAX-DOAS
NO2 columns observed over Tai'an in June 2006 for the QA4ECV
(n=31) and DOMINO v2 (n=45) ensemble. Summary of uncertainties for the
all (31 or 45) matching pixels. σO
(reported OMI uncertainty) and σM (reported MAX-DOAS
uncertainty) are the mean of 31 or 45 individual values, and σR is
considered to be a 10 % contribution from mismatches.
This first validation is based on a limited time range and one site. A more
comprehensive validation work, based on several MAX-DOAS sites and several
years of data, is in preparation (Compernolle, 2018).
(a) Histogram of differences in QA4ECV OMI vs. MAX-DOAS
tropospheric NO2 columns for Tai'an (China) in May–June 2006. The
black line shows a Gaussian fit to the observed differences, and the red
dashed lines shows the Gaussian expected from the uncertainties reported in
the QA4ECV and MAX-DOAS data products. (b) Same as (a), but
now for differences between DOMINO v2 and MAX-DOAS tropospheric
NO2.
Summary
We have developed an improved algorithm and uncertainty assessment for
tropospheric NO2 satellite retrievals from UV/VIS satellite
sensors. Our effort has resulted in the generation of a 1995–2017 climate
data record of tropospheric NO2 columns with fully traceable
uncertainty metrics that can be readily used for model evaluation, for
estimating NOx emissions and nitrogen deposition. In
designing our new algorithm, we followed advice from the user and producer
community and from WMO GCOS best practices on generating climate data
records. Specifically, we extended the information content on flags and
uncertainties in the data files and present a traceability chain
along with the data files. This traceability chain is an easily accessible
web-hosted interactive flow diagram that shows the components of the QA4ECV
NO2 algorithm and how external information is embedded in the
retrieval process, providing details on where those pieces of information can
be found.
The QA4ECV project involved detailed comparisons of different approaches
between groups for the DOAS slant column retrievals and the estimate of the
stratospheric sub-column and air mass factors. Using the latest and best
available level-1 data for GOME, SCIAMACHY, GOME-2A, and OMI from the
relevant space agencies, the comparisons led us to improve the spectral
fitting of NO2 by accounting for liquid water absorption and an
intensity-offset correction. This improved the quality of the NO2
fit over clear-sky ocean scenes by up to 30 % (Zara et al., 2018), but
did not substantially affect the NO2 fit over polluted scenes. We
compared three alternate methods for estimating the stratospheric
NO2 background.
Data assimilation was considered to be the most viable option for the QA4ECV
algorithm because it provides a coherent framework for stratospheric
corrections as well as air mass factor (AMF) calculations. We based the data
assimilation on the TM5-MP chemistry transport model with
1∘×1∘ horizontal resolution, a major step forward
compared to earlier assimilation schemes based on a TM4
(3∘×2∘), and include corrections for sphericity
effects on atmospheric radiative transfer, as described in Lorente et
al. (2017). Our new stratospheric correction leads to fewer negative
tropospheric NO2 columns for retrievals at extreme viewing
geometries. We then tested various models and approaches to calculate
tropospheric AMFs under challenging retrieval scenarios. AMFs calculated with
different radiative transfer models agree well, as long as assumptions and
ancillary data inputs are consistent. With groups using their own preferred
settings, we find differences (or structural uncertainty) in AMFs up to
40 % with respect to the ensemble mean, stressing the importance of
adequate traceability. Many of the lessons learnt for QA4ECV algorithm
development are currently being applied to NO2 retrievals from
S5P-TROPOMI.
The QA4ECV NO2 product contains an algorithm uncertainty estimate
associated with each individual observation. We obtain this estimate via
uncertainty propagation calculations, accounting for pixel-specific
sensitivities to state parameters (Jacobians) such as surface reflectance,
clouds, and the NO2 vertical profile. The uncertainties are highest
in the cold season, when AMFs are particularly uncertain and typically
amount to 40 % over the polluted areas. For averaged QA4ECV NO2
data, associated uncertainties may be reduced, but part of the uncertainty
due to systematic error will remain. Our work provides recommendations on how
to estimate the uncertainty for spatially or temporally averaged data, taking
into account a partial correlation in the errors between pixels. We evaluated
the algorithm uncertainties against independent assessments of structural
uncertainties for each retrieval step and find that the structural
uncertainties are of similar magnitude or exceed the algorithm uncertainties
for all retrieval sub-steps. Finally, we used MAX-DOAS NO2 column
measurements obtained over the polluted Tai'an (China) region in June 2006 to
validate the OMI QA4ECV NO2 columns and their uncertainties.
By accounting for spatial differences between the pixel and the location of
Tai'an, we found good agreement between the QA4ECV and MAX-DOAS NO2
columns (bias =-2 %, rms differences 16 %, n=31), which are much
better than the agreement between DOMINO v2 and MAX-DOAS
(bias =+11 %, rms 35 %, n=45). The small differences between
coinciding QA4ECV and MAX-DOAS NO2 columns suggest that our QA4ECV
algorithm uncertainties are likely on the conservative side, at least over
Tai'an.
The QA4ECV NO2 essential climate variable
precursor product contains vertical NO2 columns for the period
1995–2017. The data set contains (1) the tropospheric vertical column
density, (2) the stratospheric vertical column density, and (3) the total
vertical column density. The NO2 ECV precursor data provide
geophysical information for each and every ground pixel observed by GOME,
SCIAMACHY, OMI, and GOME-2(A). The QA4ECV NO2 data product is
available online via http://www.qa4ecv.eu (last access: 13 December
2018), under “ECV data”. The data product has been processed with the
coherent algorithm described in this work.
For GOME, data are available from 1 July 1995 to 30 June 2003 (8 years).
For SCIAMACHY, data are available from 1 August 2002 to 30 April 2012
(9 years and 9 months). For GOME-2(A), data are available from 1 January 2007
to 31 December 2017, and for OMI from 1 October 2004 to
31 December 2017, so that the total length of the data set exceeds 22 years
at the time of writing. For each of the data sets, digital object identifiers
have been registered (Boersma et al., 2017b, c, d, e). Detailed information
on how to use the data can be found in the Product Specification Document for
NO2 ECV Precursor product (Boersma et al., 2017f).
The supplement related to this article is available online at: https://doi.org/10.5194/amt-11-6651-2018-supplement.
KFB led the effort to generate the QA4ECV NO2
data sets, prepared the figures and wrote the manuscript. HE and KFB developed
the overall retrieval framework and processed the data. AR coordinated the
SCD intercomparison and processed the GOME, SCIAMACHY and GOME-2
NO2 SCDs. IDS, JVG and SB provided inputs to the SCD
intercomparison; IDS processed the GOME-2 SCDs and JVG processed the OMI
SCDs. AL generated Fig. 5, and AL and KFB coordinated the AMF
intercomparison and processed the improved AMF look-up table. SB and KFB
compared the stratospheric corrections and implemented the STREAM estimates
to the data products. MZ and EP improved and analyzed the quality of the SCD
approaches. MZ made Fig. 1, and EP contributed to Fig. 3 and made Fig. 4.
MVR, TW, AR and KFB evaluated the level-1 data and oversaw the spectral
fitting harmonization. JDM and KFB prepared the transition from TM4 to TM5-MP
for the QA4ECV retrieval framework. RvdA helped to make data, images, and the
dois available online, and test-used the new data. JN and ADR collected user
feedback and developed the traceability chain concepts. HI and GP helped
develop ideas for validation, and JCL and SC contributed to the quality
assessment against international standards. All authors read and commented on
the manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
This research has been supported by the EU FP7 project, Quality Assurance for
Essential Climate Variables (QA4ECV), grant no. 607405. We acknowledge the
free use of the GOME-2 data provided by EUMETSAT. We thank all the persons
who have kindly and carefully responded to the QA4ECV user survey. We
appreciate the efforts by Michael Barkley (Leicester University), Lok Lamsal
(NASA GSFC), Jin-Tai Lin and Mengyao Liu (Peking University), who kindly
contributed to the NO2 AMF
intercomparison. Edited by: Lok Lamsal
Reviewed by: two anonymous referees
ReferencesAnand, J. S., Monks, P. S., and Leigh, R. J.: An improved retrieval of
tropospheric NO2 from space over polluted regions using an Earth
radiance reference, Atmos. Meas. Tech., 8, 1519–1535,
10.5194/amt-8-1519-2015, 2015.Beirle, S., Sihler, H., and Wagner, T.: Linearisation of the effects of
spectral shift and stretch in DOAS analysis, Atmos. Meas. Tech., 6, 661–675,
10.5194/amt-6-661-2013, 2013.Beirle, S., Hörmann, C., Jöckel, P., Liu, S., Penning de Vries, M.,
Pozzer, A., Sihler, H., Valks, P., and Wagner, T.: The STRatospheric
Estimation Algorithm from Mainz (STREAM): estimating stratospheric
NO2 from nadir-viewing satellites by weighted convolution, Atmos.
Meas. Tech., 9, 2753–2779, 10.5194/amt-9-2753-2016, 2016.Boersma, K. F., Eskes, H. J., and Brinksma, E. J.: Error analysis for
tropospheric NO2 retrieval from space, J. Geophys. Res., 109,
D04311, 10.1029/2003JD003962, 2004.Boersma, K. F., Eskes, H. J., Veefkind, J. P., Brinksma, E. J., van der A, R.
J., Sneep, M., van den Oord, G. H. J., Levelt, P. F., Stammes, P., Gleason,
J. F., and Bucsela, E. J.: Near-real time retrieval of tropospheric
NO2 from OMI, Atmos. Chem. Phys., 7, 2103–2118,
10.5194/acp-7-2103-2007, 2007.Boersma, K. F., Eskes, H. J., Dirksen, R. J., van der A, R. J., Veefkind, J.
P., Stammes, P., Huijnen, V., Kleipool, Q. L., Sneep, M., Claas, J.,
Leitão, J., Richter, A., Zhou, Y., and Brunner, D.: An improved
tropospheric NO2 column retrieval algorithm for the Ozone
Monitoring Instrument, Atmos. Meas. Tech., 4, 1905–1928,
10.5194/amt-4-1905-2011, 2011.Boersma, K. F., Vinken, G. C. M., and Eskes, H. J.: Representativeness errors
in comparing chemistry transport and chemistry climate models with satellite
UV–Vis tropospheric column retrievals, Geosci. Model Dev., 9, 875–898,
10.5194/gmd-9-875-2016, 2016.Boersma, K. F., De Smedt, I., George, M., Compernolle, S., Eskes, H. J.,
Zara, M., van Geffen, J., Lorente, A., Richter, A., Peters, E., Hilboll, A.,
Yu. H., Van Roozendael, M., Beirle, S., Dörner, S., Wagner, T.,
Nightingale, J., Lambert, J.-C., Coheur, P.-F., and Clerbaux, C.: Report on
the assessment and characterization of uncertainties in the retrieval
algorithms for Atmosphere ECV records, 71 pp.,
available at: http://www.qa4ecv.eu/sites/default/files/D5.5_v1.0.compressed.pdf (last
access: 12 April 2018), 2017a.Boersma, K. F., Eskes, H., Richter, A., De Smedt, I., Lorente, A., Beirle,
S., Van Geffen, J., Peters, E., Van Roozendael, M., and Wagner, T.: QA4ECV
NO2 tropospheric and stratospheric vertical column data from GOME
(Version 1.1) (data set), Royal Netherlands Meteorological Institute (KNMI),
10.21944/qa4ecv-no2-gome-v1.1, 2017b.Boersma, K. F., Eskes, H., Richter, A., De Smedt, I., Lorente, A., Beirle,
S., Van Geffen, J., Peters, E., Van Roozendael, M., and Wagner, T.: QA4ECV
NO2 tropospheric and stratospheric vertical column data from
SCIAMACHY (Version 1.1) (data set). Royal Netherlands Meteorological
Institute (KNMI), 10.21944/qa4ecv-no2-scia-v1.1, 2017c.Boersma, K. F., Eskes, H., Richter, A., De Smedt, I., Lorente, A., Beirle,
S., Van Geffen, J., Peters, E., Van Roozendael, M. and Wagner, T.: QA4ECV
NO2 tropospheric and stratospheric vertical column data from
GOME-2A (Version 1.1) (data set), Royal Netherlands Meteorological Institute
(KNMI), 10.21944/qa4ecv-no2-gome2a-v1.1, 2017d.Boersma, K. F., Eskes, H., Richter, A., De Smedt, I., Lorente, A., Beirle,
S., Van Geffen, J., Peters, E., Van Roozendael, M. and Wagner, T.: QA4ECV
NO2 tropospheric and stratospheric vertical column data from OMI
(Version 1.1) (data set), Royal Netherlands Meteorological Institute (KNMI).
10.21944/qa4ecv-no2-omi-v1.1, 2017e.Boersma, K. F., van Geffen, J., Eskes, H., van der A, R. J., De Smedt, I.,
Van Roozendael, M., Yu, H., Richter, A., Peters, E., Beirle, S., Wagner, T.,
Lorente, A., Scanlon, T., Compernolle, S., and Lambert, J.-C.: Product
Specification Document for the QA4ECV NO2 Precursor Product
(Version 1.1), available at:
http://temis.nl/qa4ecv/no2col/QA4ECV_NO_2_PSD_v1.1.compressed.pdf (last
access: 25 April 2018), 2017f.Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and
Zemp, M.: The concept of Essential Climate Variables in support of climate
research, applications, and policy, B. Am. Meteor. Soc., 1431–1443,
10.1175/BAMS-D-13-00047.1, 2014.
Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noel, S.,
Rozanov, V. V., Chance, K. V., and Goede, A. P. H.: SCIA- MACHY: Mission
objectives and measurement modes, J. Atmos. Sci., 56, 127–150, 1999.Bucsela, E. J., Celarier, E. A., Wenig, M. O., Gleason, J. F., Veefkind, J.
P., Boersma, K. F., and Brinksma, E. J.: Algorithm for NO2 vertical
column retrieval from the Ozone Monitoring Instrument, IEEE T. Geosci. Remote
Sens., 44, 1245–1258, 10.1109/TGRS.2005.863715, 2006.
Burrows, J. P., Weber, M., Buchwitz, M., Rozanov, V.,
Ladstätter-Weißenmayer, A., Richter, A., DeBeek, R., Hoogen, R.,
Bramstedt, K., Eichmann, K.-U., Eisinger, M., and Perner, D.: The Global
Ozone Monitoring Experiment (GOME): Mission Concept and First Scientific
Results, J. Atmos. Sci., 56, 151–175, 1999.Chance, K., Kurosu, T. P., and Sioris, C. E.: Undersampling correction for
array detector-based satellite spectrometers., Appl. Opt., 44, 1296–1304,
10.1364/AO.44.001296, 2005.Compernolle, S., Lambert, J.-C., Boersma, K. F., Schulz, J., Müller,
J.-P., Coheur, P.-F., De Smedt, I., Van Roozendael, M., Blessing, S., George,
M., and Gobron, N.: Report on the compliance of ECV records with GCOS and
user requirements (QA4ECV Deliverable 6.1), 69 pp.,
available at: http://www.qa4ecv.eu/sites/default/files/QA4ECV_D6p1_FINAL%
2BSMM.pdf
(last access: 12 April 2018), 2018.Crutzen, P. J.: The influence of nitrogen oxides on the atmospheric ozone
content, Q. J. Roy. Meteorol. Soc., 96, 320–325, 10.1002/qj.49709640815,
1970.Deutschmann, T., Beirle, S., Friess, U., Grzegorski, M., Kern, C., Kritten,
L., Platt, U., Prados-Román, C., Puíite, J., Wagner, T., Werner, B.,
Pfeilsticker, K.,: The Monte Carlo atmospheric radiative transfer model
McArtim: Introduction and validation of Jacobians and 3D features, J.
Quant. Spectrosc. Ra., 112, 1119–1137,
10.1016/j.jqsrt.2010.12.009, 2011.Dikty, S., Richter, A., Weber, M., Noel, S., Bovensmann, H., Wittrock, F.,
and Burrows, J. P.: GOME-2 on MetOp-A Support for Analysis of GOME-2
In-Orbit Degradation and Impacts on Level 2 Data Products – Final Report,
Tech. rep., Inst. of Environ. Phys., Bremen, Germany, available at:
https://www.eumetsat.int/website/home/index.html, document ITT 09/10000262 (last access: 25
September 2012), 2011.Dirksen, R. J., Boersma, K. F., Eskes, H. J., Ionov, D. V., Bucsela, E. J.,
Levelt, P. F., and Kelder, H. M.: Evaluation of stratospheric NO2
retrieved from the ozone monitoring instrument: intercomparison, diurnal
cycle, and trending, J. Geophys. Res., 116, D08305, 10.1029/2010JD014943,
2011.DLR, Max-Planck Institute, IUP, RAL Space, Royal Netherlands Meteorological
Institute: Sentinel-5P TROPOMI Science Verification Report, Issue
2.1, S5P-IUP-L2-ScVR-RP, Technical Document, 314 pp.,
available at: https://earth.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi/document-library/-/asset_publisher/w9Mnd6VPjXlc/content/sentinel-5p-tropomi-science-verification-report,
(last access: 22 August 2017), 2015.Dobber, M. R., Kleipool, Q., Dirksen, R., Levelt, P. F., Jaross, G., Taylor,
S., Kelly, T., Flynn, L., Leppelmeier, G., and Rozemeijer, N.: Validation of
ozone monitoring instrument level 1b data products, J. Geophys. Res., 113,
D15S06, 10.1029/2007JD008665, 2008.Drosoglou, T., Bais, A. F., Zyrichidou, I., Kouremeti, N., Poupkou, A.,
Liora, N., Giannaros, C., Koukouli, M. E., Balis, D., and Melas, D.:
Comparisons of ground-based tropospheric NO2 MAX-DOAS measurements
to satellite observations with the aid of an air quality model over the
Thessaloniki area, Greece, Atmos. Chem. Phys., 17, 5829–5849,
10.5194/acp-17-5829-2017, 2017.Eskes, H. J. and Boersma, K. F.: Averaging kernels for DOAS total-column
satellite retrievals, Atmos. Chem. Phys., 3, 1285–1291,
10.5194/acp-3-1285-2003, 2003.Eskes, H. J., van Velthoven, P. F. J., Valks, P. J. M., and Kelder, H. M.:
Assimilation of GOME total-ozone satellite observations in a
three-dimensional tracer-transport model, Q. J. Roy. Meteor. Soc., 129,
1663–1681, 10.1256/qj.02.14, 2003.Fayt, C. and Van Roozendael, M.: WinDOAS 2.1 software user manual, Uccle,
Belgium, BIRA-IASB, http://bro.aeronomie.be/WinDOAS-SUM-210b.pdf (last
access: 13 December 2018), 2001.Galloway, J. N., Aber, J. D., Erisman, J. W., Seitzinger, S. P., Howarth, R.
W., Cowling, E. B., and Cosby, B. J.: The Nitrogen Cascade, BioScience,
53, 341–356, 10.1641/0006-3568(2003)053[0341:TNC]2.0.CO;2, 2003.GCOS: Implementation plan for the Global Observing System for climate in
support of the UNFCC (2010 Update), available at: https://library.wmo.int/opac/doc_num.php?explnum_id=3851 (last access: 12
April 2018), GCOS-138, 180 pp., 2010.GCOS-200: The Global Observing System for Climate: Implementation Needs,
GCOS 2016,
Implementation Plan, GCOS No. 200, available at:
https://library.wmo.int/opac/doc_num.php?explnum_id=3417, last access: November 2016.GOME Products and Algorithms, available at:
https://earth.esa.int/web/sppa/mission-performance/esa-missions/ers-2/gome/products-and-algorithms/products-information
(last access: 13 December 2018), 2018.Grewe, V., Dahlmann, K., Matthes, S., and Steinbrecht, W.: Attributing ozone
to NOx emissions: Implications for climate mitigation measures, Atmos.
Environ., 59, 102–107, 10.1016/j.atmosenv.2012.05.002, 2012.GUM: Joint Committee for Guides in Metrology (JCGM/WG 1) 100:2008, Evaluation
of measurement data – Guide to the expression of uncertainty in a
measurement (GUM), ISO/IEC Guide 98-3:2008, available at:
http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf
(last access: 13 December 2018), 2008.Hains, J. C., Boersma, K. F., Kroon, M., Dirksen, R. J., Cohen, R. C.,
Perring, A. E., Bucsela, E., Volten, H., Swart, D. P. J., Richter, A.,
Wittrock, F., Schoenhardt, A., Wagner, T., Ibrahim, O. W., Van Roozendael,
M., Pinardi, G., Gleason, J. F., Veefkind, J. P., and Levelt, P.: Testing and
improving OMI DOMINO tropospheric NO2 using observations from the
DANDELIONS and INTEX-B validation campaigns, J. Geophys. Res., 115, D05301,
10.1029/2009JD012399, 2010.Hassinen, S., Balis, D., Bauer, H., Begoin, M., Delcloo, A., Eleftheratos,
K., Gimeno Garcia, S., Granville, J., Grossi, M., Hao, N., Hedelt, P.,
Hendrick, F., Hess, M., Heue, K.-P., Hovila, J., Jønch-Sørensen, H.,
Kalakoski, N., Kauppi, A., Kiemle, S., Kins, L., Koukouli, M. E.,
Kujanpää, J., Lambert, J.-C., Lang, R., Lerot, C., Loyola, D.,
Pedergnana, M., Pinardi, G., Romahn, F., van Roozendael, M., Lutz, R., De
Smedt, I., Stammes, P., Steinbrecht, W., Tamminen, J., Theys, N., Tilstra,
L. G., Tuinder, O. N. E., Valks, P., Zerefos, C., Zimmer, W., and
Zyrichidou, I.: Overview of the O3M SAF GOME-2 operational atmospheric
composition and UV radiation data products and data availability, Atmos.
Meas. Tech., 9, 383–407, 10.5194/amt-9-383-2016, 2016.Hilboll, A., Richter, A., Rozanov, A., Hodnebrog, Ø., Heckel, A., Solberg,
S., Stordal, F., and Burrows, J. P.: Improvements to the retrieval of
tropospheric NO2 from satellite – stratospheric correction using
SCIAMACHY limb/nadir matching and comparison to Oslo CTM2 simulations, Atmos.
Meas. Tech., 6, 565–584, 10.5194/amt-6-565-2013, 2013.Hoek, G., Krishnan, R. M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B.,
and Kaufman, J. D.: Long-term air pollution exposure and cardio- respiratory
mortality: a review, Environ. Health, 12, 1–15,
10.1186/1476-069X-12-43, 2013.Irie, H., Kanaya, Y., Akimoto, H., Tanimoto, H., Wang, Z., Gleason, J. F.,
and Bucsela, E. J.: Validation of OMI tropospheric NO2 column data
using MAX-DOAS measurements deep inside the North China Plain in June 2006:
Mount Tai Experiment 2006, Atmos. Chem. Phys., 8, 6577–6586,
10.5194/acp-8-6577-2008, 2008.Irie, H., Boersma, K. F., Kanaya, Y., Takashima, H., Pan, X., and Wang, Z.
F.: Quantitative bias estimates for tropospheric NO2 columns
retrieved from SCIAMACHY, OMI, and GOME-2 using a common standard for East
Asia, Atmos. Meas. Tech., 5, 2403–2411,
10.5194/amt-5-2403-2012, 2012.Inness, A., Baier, F., Benedetti, A., Bouarar, I., Chabrillat, S., Clark, H.,
Clerbaux, C., Coheur, P., Engelen, R. J., Errera, Q., Flemming, J., George,
M., Granier, C., Hadji-Lazaro, J., Huijnen, V., Hurtmans, D., Jones, L.,
Kaiser, J. W., Kapsomenakis, J., Lefever, K., Leitão, J., Razinger, M.,
Richter, A., Schultz, M. G., Simmons, A. J., Suttie, M., Stein, O.,
Thépaut, J.-N., Thouret, V., Vrekoussis, M., Zerefos, C., and the MACC
team: The MACC reanalysis: an 8 yr data set of atmospheric composition,
Atmos. Chem. Phys., 13, 4073–4109, 10.5194/acp-13-4073-2013,
2013.Jin, J., Ma, J., Lin, W., Zhao, H., Shaiganfar, R., Beirle, S., and Wagner,
T.: MAX-DOAS measurements and satellite validation of tropospheric NO2 and
SO2 vertical column densities at a rural site of North China, Atmos.
Environ., 133, 12–25,
10.1016/j.atmosenv.2016.03.031, 2016.Kleipool, Q. L., Dobber, M. R., de Haan, J. F., and Levelt, P. F.: Earth
surface reflectance climatology from 3 years of OMI data, J. Geophys.
Res.-Atmos., 113, D18308, 10.1029/2008JD010290, 2008.Krotkov, N. A., McLinden, C. A., Li, C., Lamsal, L. N., Celarier, E. A.,
Marchenko, S. V., Swartz, W. H., Bucsela, E. J., Joiner, J., Duncan, B. N.,
Boersma, K. F., Veefkind, J. P., Levelt, P. F., Fioletov, V. E., Dickerson,
R. R., He, H., Lu, Z., and Streets, D. G.: Aura OMI observations of regional
SO2 and NO2 pollution changes from 2005 to 2015, Atmos.
Chem. Phys., 16, 4605-4629, 10.5194/acp-16-4605-2016, 2016.Kollonige, D. E., Thompson, A. M., Josipovic, M., Tzortziou, M., Beukes, J.
P., Burger, R., Martins, D. K. van Zyl, P. G., Vakkari, V., and
Laakso, L.: OMI satellite and ground-based Pandora observations and their
application to surface NO2 estimations at terrestrial and marine
sites,J. Geophys. Res.-Atmos., 123,
1441–1459, 10.1002/2017JD026518, 2018.
Levelt, P. F., Van den Oord, G. H. J., Dobber, M. R., Malkki, A., Visser,
H., de Vries, J., Stammes, P., Lundell, J. O. V., and Saari, H.: The Ozone
Monitoring Instrument, IEEE T. Geosci. Remote, 44, 1093–1101, 2006.Lin, J.-T., Martin, R. V., Boersma, K. F., Sneep, M., Stammes, P., Spurr, R.,
Wang, P., Van Roozendael, M., Clémer, K., and Irie, H.: Retrieving
tropospheric nitrogen dioxide from the Ozone Monitoring Instrument: effects
of aerosols, surface reflectance anisotropy, and vertical profile of nitrogen
dioxide, Atmos. Chem. Phys., 14, 1441–1461, 10.5194/acp-14-1441-2014,
2014.Liu, S. C., Trainer, M., Fehsenfeld, F. C., Parrish, D. D., Williams, E. J.,
Fahey, D. W., Hübler, G., and Murphy, P. C.: Ozone production in the
rural troposphere and the implications for regional and global ozone
distributions, J. Geophys. Res., 92, 4191–4207,
10.1029/JD092iD04p04191, 1987.Lorente, A., Folkert Boersma, K., Yu, H., Dörner, S., Hilboll, A.,
Richter, A., Liu, M., Lamsal, L. N., Barkley, M., De Smedt, I., Van
Roozendael, M., Wang, Y., Wagner, T., Beirle, S., Lin, J.-T., Krotkov, N.,
Stammes, P., Wang, P., Eskes, H. J., and Krol, M.: Structural uncertainty in
air mass factor calculation for NO2 and HCHO satellite retrievals,
Atmos. Meas. Tech., 10, 759-782, 10.5194/amt-10-759-2017,
2017.Lorente, A., Boersma, K. F., Stammes, P., Tilstra, L. G., Richter, A., Yu,
H., Kharbouche, S., and Muller, J.-P.: The importance of surface reflectance
anisotropy for cloud and NO2 retrievals from GOME-2 and OMI, Atmos.
Meas. Tech., 11, 4509–4529, 10.5194/amt-11-4509-2018, 2018.Maasakkers, J. D.: Vital improvements to the retrieval of tropospheric
NO2 columns from the Ozone Monitoring instrument, M.Sc. thesis,
Eindhoven University of Technology, The Netherlands, 67 pp., 2013.Marchenko, S., Krotkov, N. A., Lamsal, L. N., Celarier, E. A., Swartz, W.
H., and Bucsela, E. J.: Revising the slant column density retrieval of
nitrogen dioxide observed by the Ozone Monitoring Instrument, J. Geophys. Res.-Atmos., 120,
5670–5692, 10.1002/2014JD022913, 2015.Miyazaki, K., Eskes, H. J., and Sudo, K.: Global NOx emission estimates
derived from an assimilation of OMI tropospheric NO2columns, Atmos.
Chem. Phys., 12, 2263–2288, 10.5194/acp-12-2263-2012, 2012.Müller, J.-P., Kharbouche, S., Gobron, N., Scanlon, T., Govaerts, Y.,
Danne, O., Schultz, J., Lattanzio, A., Peters, E., De Smedt, I., Beirle, S.,
Lorente, A., Coheur, P. F., George, M., Wagner, T., Hilboll, A., Richter,
A., Van Roozendael, M., and Boersma, K. F.: Recommendations (scientific) on
best practices for retrievals for Land and Atmosphere ECVs (QA4ECV
Deliverable 4.2 version 1.0), 186 pp.,
http://www.qa4ecv.eu/sites/default/files/D4.2.pdf (last access: 12 April
2018), 2016.
Munro, R., Eisinger, M., Anderson, C., Callies, J., Corpaccioli, E., Lang,
R., Lefebvre, A., Livschitz, Y., and Albin Pana, A. P.: GOME-2 on MetOp,
Proc. of The 2006 EUMETSAT Meteoro- logical Satellite Conference, Helsinki,
Finland, 12–16 June 2006, EUMETSAT P.48, 2006.Munro, R., Lang, R., Klaes, D., Poli, G., Retscher, C., Lindstrot, R.,
Huckle, R., Lacan, A., Grzegorski, M., Holdak, A., Kokhanovsky, A.,
Livschitz, J., and Eisinger, M.: The GOME-2 instrument on the Metop series
of satellites: instrument design, calibration, and level 1 data processing
– an overview, Atmos. Meas. Tech., 9, 1279–1301,
10.5194/amt-9-1279-2016, 2016.Nightingale, J., De Rudder, A., Boersma, F., Scanlon, T., Farquhar, C.,
Muller, J.-P., and Fox, N.: Results from the QA4ECV user requirements survey
on quality assurance in satellite data products (QA4ECV Deliverable 1.1
version 2.0), 16 pp.,
http://www.qa4ecv.eu/sites/default/files/QA4ECV_D.1.1_survey_report_V2.0_20150922.pdf,
last access: 10 April 2018, 2015.Nightingale J., Boersma, K. F., Muller, J.-P., Compernolle, S.,
Lambert, J.-C., Blessing, S., Giering, R., Gobron, N., De Smedt, I., Coheur,
P., George, M., Schulz, J., and Wood, A.: Quality Assurance Framework
Development Based on Six New ECV Data Products to Enhance User Confidence
for Climate Applications, Remote Sens., 10, 1254, 10.3390/rs10081254,
2018.Oldeman, A.: Effect of including an intensity offset in the DOAS
NO2 retrieval of TROPOMI, Internship report, R-1944-SE, Eindhoven
University of Technology/KNMI, Eindhoven, May 2018,
https://kfolkertboersma.files.wordpress.com/2018/06/report_oldeman_22052018.pdf,
(last access: 30 October 2018), 2018.Pinardi, G., Van Roozendael, M., Hendrick, F., Van Roozendael, M., Hendrick,
F., Compernolle, S., Lambert, J.-C., Granville, J., Gielen, C., Cede, A.,
Kanaya, Y., Irie, H., Wittrock, F., Richter, A., Peters, E., Wagner, T.,
Remmers, J., Friess, U., Vlemmix, T., Piters, A., Tiefengraber, M., Herman,
J., Abuhassan, N., Holla, R., Bais, A., Balis, D., Drosoglou, T., Kouremeti,
N., Hovila, J., Chong, J., Postylyakov, O., Borovski, A., and Ma, J.: Satellite nadir NO2
validation based on direct-sun and MAXDOAS network observations, 8th
International DOAS Workshop, Yokohama, Japan, 4–6 September 2017, O2-01,
2017.Richter, A.: Absorptionsspektroskopische Messungen stratosphärischer
Spurengase über Bremen, 53∘ N, PhD-Thesis, University of Bremen,
June 1997, 1997.
Richter, A. and Wagner, T.: Diffuser plate spectral structures and their
influence on GOME slant columns, Tech. Rep., Inst. Env. Phys., Univ. Bremen
and Inst. Env. Phys., Univ. Heidelberg, 2001.Richter, A., Begoin, M., Hilboll, A., and Burrows, J. P.: An improved
NO2 retrieval for the GOME-2 satellite instrument, Atmos. Meas.
Tech., 4, 1147–1159, 10.5194/amt-4-1147-2011, 2011.Roscoe, H. K., Van Roozendael, M., Fayt, C., du Piesanie, A., Abuhassan, N.,
Adams, C., Akrami, M., Cede, A., Chong, J., Clémer, K., Friess, U., Gil
Ojeda, M., Goutail, F., Graves, R., Griesfeller, A., Grossmann, K.,
Hemerijckx, G., Hendrick, F., Herman, J., Hermans, C., Irie, H., Johnston, P.
V., Kanaya, Y., Kreher, K., Leigh, R., Merlaud, A., Mount, G. H., Navarro,
M., Oetjen, H., Pazmino, A., Perez-Camacho, M., Peters, E., Pinardi, G.,
Puentedura, O., Richter, A., Schönhardt, A., Shaiganfar, R., Spinei, E.,
Strong, K., Takashima, H., Vlemmix, T., Vrekoussis, M., Wagner, T., Wittrock,
F., Yela, M., Yilmaz, S., Boersma, F., Hains, J., Kroon, M., Piters, A., and
Kim, Y. J.: Intercomparison of slant column measurements of NO2 and
O4 by MAX-DOAS and zenith-sky UV and visible spectrometers, Atmos.
Meas. Tech., 3, 1629–1646, 10.5194/amt-3-1629-2010, 2010.S5P/TROPOMI Science Verification Report, S5P-IUP-L2-ScVR-RP, Richter A. and
the Verification Teams: European Space Agency, 314 pp., available at:
https://earth.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi/document-library/-/asset_publisher/w9Mnd6VPjXlc/content/sentinel-5p-tropomi-science-verification-report
(last access: 12 April 2018), 2015.Schenkeveld, V. M. E., Jaross, G., Marchenko, S., Haffner, D., Kleipool, Q.
L., Rozemeijer, N. C., Veefkind, J. P., and Levelt, P. F.: In-flight
performance of the Ozone Monitoring Instrument, Atmos. Meas. Tech., 10,
1957–1986, 10.5194/amt-10-1957-2017, 2017.Shindell, D. T., Faluvegi, G., Koch, D. M., Schmidt, G. A., Unger, N., and
Bauer, S. E.: Improved Attribution of Climate Forcing to Emissions, Science,
325, 716–718, 10.1126/science.1174760, 2009.
Slijkhuis, S., Aberle, B., Coldewey-Egbers, M., Loyola, D., Dehn, A., and
Fehr, T.: GOME/ERS-2: New homogeneous Level 1B data from an old instrument,
Proceeding at the ESA ATMOS conference, 8–12 June 2015, University of Crete
Heraklion, Greece, 2015.Solomon, S., Portmann, R. W., Sanders, R. W., Daniel, J. S., Madsen, W.,
Bartram, B., and Dutton, E. G.: On the role of nitrogen dioxide in the
absorption of solar radiation, J. Geophys. Res., 104, 12047–12058,
10.1029/1999JD900035, 1999.Tilstra, L. G., Tuinder, O. N. E., Wang, P., and Stammes, P.: Surface
reflectivity climatologies from UV to NIR determined from Earth observations
by GOME-2 and SCIAMACHY, J. Geophys. Res.-Atmos., 122, 4084–4111,
10.1002/2016JD025940, 2017.Vandaele, A. C., Hermans, C., Simon, P. C., Carleer, M., Colin, R., Fally,
S., Mérienne, M. F., Jenouvrier, A., and Coquart, B.: Measurements of the
NO2 absorption cross section from 42 000 cm-1 to 10 000 cm-1
(238–1000 nm) at 220 K and 294 K, J. Quant. Spectrosc. Radiat. Transf.,
59, 171–184, 1998.van Geffen, J. H. G. M., Boersma, K. F., Van Roozendael, M., Hendrick, F.,
Mahieu, E., De Smedt, I., Sneep, M., and Veefkind, J. P.: Improved spectral
fitting of nitrogen dioxide from OMI in the 405–465 nm window, Atmos. Meas.
Tech., 8, 1685–1699, 10.5194/amt-8-1685-2015, 2015.van Noije, T. P. C., Eskes, H. J., Dentener, F. J., Stevenson, D. S.,
Ellingsen, K., Schultz, M. G., Wild, O., Amann, M., Atherton, C. S.,
Bergmann, D. J., Bey, I., Boersma, K. F., Butler, T., Cofala, J., Drevet, J.,
Fiore, A. M., Gauss, M., Hauglustaine, D. A., Horowitz, L. W., Isaksen, I. S.
A., Krol, M. C., Lamarque, J.-F., Lawrence, M. G., Martin, R. V., Montanaro,
V., Müller, J.-F., Pitari, G., Prather, M. J., Pyle, J. A., Richter, A.,
Rodriguez, J. M., Savage, N. H., Strahan, S. E., Sudo, K., Szopa, S., and van
Roozendael, M.: Multi-model ensemble simulations of tropospheric NO2
compared with GOME retrievals for the year 2000, Atmos. Chem. Phys., 6,
2943–2979, https://doi.org/10.5194/acp-6-2943-2006, 2006.Veefkind, J. P., de Haan, J. F., Sneep, M., and Levelt, P. F.: Improvements
to the OMI O2-O2 operational cloud algorithm and
comparisons with ground-based radar–lidar observations, Atmos. Meas. Tech.,
9, 6035–6049, 10.5194/amt-9-6035-2016, 2016.Verstraeten, W. W., Neu, J. L., Williams, J. E., Bowman, K. W., Worden, J.
R., and Boersma, K. F.: Rapid increases in tropospheric ozone production and
export from China, Nat. Geosci., 8, 690–695, 10.1038/ngeo2493, 2015.Vinken, G. C. M., Boersma, K. F., Maasakkers, J. D., Adon, M., and Martin, R.
V.: Worldwide biogenic soil NOx emissions inferred from
OMI NO2 observations, Atmos. Chem. Phys., 14, 10363–10381,
10.5194/acp-14-10363-2014, 2014.
Wang, P., Stammes, P., van der A, R., Pinardi, G., and van Roozendael, M.:
FRESCO+: an improved O2 A-band cloud retrieval algorithm for
tropospheric trace gas retrievals, Atmos. Chem. Phys., 8, 6565–6576,
10.5194/acp-8-6565-2008, 2008.Williams, J. E., Boersma, K. F., Le Sager, P., and Verstraeten, W. W.: The
high-resolution version of TM5-MP for optimized satellite retrievals:
description and validation, Geosci. Model Dev., 10, 721–750,
10.5194/gmd-10-721-2017, 2017.
Witman, S., Holloway, T., and Reddy, P.: Integrating Satellite Data into Air
Quality Management: Experience from Colorado, Environmental Manager (EM)
Magazine, 34–38, February 2014 Issue, 2014.World Health Organization: Review of evidence on health aspects of air
pollution REVIHAAP Project, Tech. rep., Copenhagen, Denmark, 302 pp.,
available at:
http://www.euro.who.int/__data/assets/pdf_file/0004/193108/REVIHAAP-Final-technical-report-final-version.pdf?ua=1
(last access: 1 April 2018), 2013.World Meteorological Organization: Guideline for the Generation of Datasets
and Products Meeting GCOS Requirements, Geneva, Switzerland, 12 pp.,
available at: https://library.wmo.int/opac/doc_num.php?explnum_id=3854
(last access: 1 April 2018), 2010.World Meteorological Organization: Systematic Observation Requirements for
Satellite-Based Data Products for Climate – 2011 Update, Geneva,
Switzerland, 128 pp., available at:
https://library.wmo.int/opac/doc_num.php?explnum_id=3710 (last access:
1 April 2018), 2011.Xu, X., Wang, J., Henze, D. K., Qu, W., and Kopacz, M.: Constraints on
aerosol sources using GEOS-Chem adjoint and MODIS radiances, and evaluation
with multisensor (OMI, MISR) data, J. Geophys. Res. Atmos., 118, 6396–6413,
10.1002/jgrd.50515, 2013.Zara, M., Boersma, K. F., De Smedt, I., Richter, A., Peters, E., van Geffen,
J. H. G. M., Beirle, S., Wagner, T., Van Roozendael, M., Marchenko, S.,
Lamsal, L. N., and Eskes, H. J.: Improved slant column density retrieval of
nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV:
intercomparison, uncertainty characterisation, and trends, Atmos. Meas.
Tech., 11, 4033–4058, 10.5194/amt-11-4033-2018, 2018.Zhang, L., Jacob, D. J., Boersma, K. F., Jaffe, D. A., Olson, J. R., Bowman,
K. W., Worden, J. R., Thompson, A. M., Avery, M. A., Cohen, R. C., Dibb, J.
E., Flock, F. M., Fuelberg, H. E., Huey, L. G., McMillan, W. W., Singh, H.
B., and Weinheimer, A. J.: Transpacific transport of ozone pollution and the
effect of recent Asian emission increases on air quality in North America: an
integrated analysis using satellite, aircraft, ozonesonde, and surface
observations, Atmos. Chem. Phys., 8, 6117–6136,
10.5194/acp-8-6117-2008, 2008.