Ozone in the troposphere affects humans and ecosystems as a pollutant and as a
greenhouse gas. Observing, understanding and modelling this dual role, as
well as monitoring effects of international regulations on air quality and
climate change, however, challenge measurement systems to operate at opposite
ends of the spatio-temporal scale ladder. Aboard the ESA/EU Copernicus
Sentinel-5 Precursor (S5P) satellite launched in October 2017, the TROPOspheric
Monitoring Instrument (TROPOMI) aspires to take the next leap forward by
measuring ozone and its precursors at unprecedented horizontal resolution
until at least the mid-2020s. In this work, we assess the quality of
TROPOMI's first release (V01.01.05–08) of tropical tropospheric ozone column (TrOC) data. Derived with the convective cloud differential (CCD) method,
TROPOMI daily TrOC data represent the 3 d moving mean ozone column
between the surface and 270 hPa under clear-sky conditions gridded at
0.5∘ latitude by 1∘ longitude resolution. Comparisons to
almost 2 years of co-located SHADOZ ozonesonde and satellite data (Aura OMI
and MetOp-B GOME-2) conclude to TROPOMI biases between -0.1 and
+2.3 DU (<+13%) when averaged over the tropical belt.
The field of the bias is essentially uniform in space (deviations <1DU) and stable in time at the 1.5–2.5 DU level. However,
the record is still fairly short, and continued monitoring will be key to
clarify whether observed patterns and stability persist, alter behaviour or
disappear. Biases are partially due to TROPOMI and the reference data records
themselves, but they can also be linked to systematic effects of the non-perfect co-locations. Random uncertainty due to co-location mismatch
contributes considerably to the 2.6–4.6 DU (∼14%–23 %)
statistical dispersion observed in the difference time series. We circumvent
part of this problem by employing the triple co-location analysis technique
and infer that TROPOMI single-measurement precision is better than
1.5–2.5 DU (∼8%–13 %), in line with uncertainty
estimates reported in the data files. Hence, the TROPOMI precision is judged
to be 20 %–25 % better than for its predecessors OMI and
GOME-2B, while sampling at 4 times better spatial resolution and almost
2 times better temporal resolution. Using TROPOMI tropospheric ozone columns at
maximal resolution nevertheless requires consideration of correlated errors at
small scales of up to 5 DU due to the inevitable interplay of
satellite orbit and cloud coverage. Two particular types of sampling error
are investigated, and we suggest how these can be identified or remedied. Our
study confirms that major known geophysical patterns and signals of the
tropical tropospheric ozone field are imprinted in TROPOMI's 2-year data
record. These include the permanent zonal wave-one pattern, the pervasive
annual and semiannual cycles, the high levels of ozone due to biomass burning
around the Atlantic basin, and enhanced convective activity cycles associated
with the Madden–Julian Oscillation over the Indo-Pacific warm pool.
TROPOMI's combination of higher precision and higher resolution reveals details
of these patterns and the processes involved, at considerably smaller spatial
and temporal scales and with more complete coverage than contemporary
satellite sounders. If the accuracy of future TROPOMI data proves to remain
stable with time, these hold great potential to be included in Climate Data
Records, as well as serve as a travelling standard to interconnect the
upcoming constellation of air quality satellites in geostationary and low
Earth orbits.
Introduction
Although present only in traces (concentrations of parts per billion by volume
of air) and representing just 10 % of the total column of atmospheric
ozone (O3), tropospheric ozone plays a central role in the oxidation
chemistry in the troposphere and references therein. It
is also an air pollutant: exposure to high levels of O3 can cause
respiratory issues and be detrimental to health, vegetation and materials.
Being a greenhouse gas it is recognised as an Essential Climate Variable (ECV)
for the Global Climate Observing System , for which
measurements in the long term are required on the global scale. While some
ozone descends from the stratosphere into the upper troposphere, most of the
ozone found in the troposphere is actually formed there by the interaction of
solar ultraviolet radiation with hydrocarbons and nitrogen oxides, its
precursors. The latter are emitted by natural processes (e.g. lightning and
wildfires) and anthropogenic activities (e.g. intentional biomass burning,
fuel combustion, power plants and other industrial activities). Precursors and their long-lived reservoirs can be transported over intercontinental
distances to the point of ozone production. As a result, the tropospheric
ozone field is highly variable over a wide range of spatial and temporal
scales, which, in turn, poses a clear challenge to the observing system.
Since the late 1980s the global distribution of tropospheric O3 has
been inferred from satellite measurements of the ultraviolet radiance
back-scattered at nadir. Tropospheric O3 from space was first
determined through residual techniques, which consist in subtracting from
satellite total O3 column data an estimate of the stratospheric
component, e.g. subtracting SAGE or SBUV stratospheric columns from TOMS
total columns
.
Residual-type retrievals were later advanced by cloud slicing techniques,
which use masking properties of the clouds present in the O3 data sets
to separate the tropospheric and stratospheric components of the total
O3 column or to infer ozone concentrations in a given layer of the
(upper) troposphere . The
technique is particularly successful in the presence of deep convective clouds, and
therefore the convective cloud differential technique (CCD) has been a
privileged approach to derive tropical tropospheric O3 fields from the
currently operating European satellite instruments: Aura OMI, MetOp GOME-2 and
Sentinel-5 Precursor TROPOMI, and their predecessors ERS-2 GOME and Envisat
SCIAMACHY
.
Ensuring the continuous global monitoring of tropospheric O3 beyond
horizon 2040 is a requirement of the EU Earth Observation Programme Copernicus
. Therefore the Copernicus Space Component (CSC) plans
a series of three Sentinel-5 atmospheric composition missions built by ESA and
to be launched nominally in 2023, 2030 and 2037, aboard the EUMETSAT
EPS/MetOp Second Generation platforms MetOp-SG-A1/2/3.
As a gap filler between heritage satellites and the Sentinel-5 series,
Sentinel-5 Precursor (S5P) was launched in October 2017 aboard the
TROPOspheric Monitoring Instrument (TROPOMI, ).
Pre-launch mission requirements for the Sentinel tropospheric O3 data
target a bias and an uncertainty
The ESA documentation uses bias
and random error, but the latter is not retained here since several
non-random components contribute to the uncertainty. Here, we use the VIM/GUM
terms bias (estimate of a systematic error) and uncertainty (non-negative
parameter that characterises the dispersion of the quantity values).
both
lower than 25 %. Since the beginning
of its nominal operation in April 2018, in-flight compliance of S5P TROPOMI
tropical tropospheric O3 data with pre-launch requirements has been
monitored routinely by the S5P Mission Performance Centre (MPC) through
comparisons to balloon-based ozonesonde measurements and to similar
CCD-derived satellite data from OMI and GOME-2B. A range of in-depth
investigations has also been carried out to assess the geophysical information
available in the TROPOMI data set, including analysis of uncertainty,
detection of geographical and sampling patterns, triple co-location studies,
and power spectrum analysis of the time series.
The objective of this paper is to report on the outcome of this comprehensive
investigation of the first 2 years of S5P TROPOMI tropospheric O3
column data. The TROPOMI data record under investigation, S5P L2_O3_TCL
V01.01.05–08, is described in Sect. .
Correlative measurements used as a reference in the validation studies are
described in Sect. , as well as the applied co-location
criteria. Comparisons with respect to ozonesondes and to other satellites are
reported in Sect. . Sampling errors at small scales
are determined in Sect. .
Section describes how TROPOMI captures known
geophysical features like the zonal wave-one pattern, tropospheric O3
enhancements associated with biomass burning and natural cycles like the
Madden–Julian Oscillation (MJO). In Sect. all results
are assembled and discussed to derive conclusions on the bias, the
uncertainty, the stability and the geophysical information available in the
S5P TROPOMI data.
The TROPOspheric Monitoring Instrument (TROPOMI, ) is the
unique payload on the Copernicus S5P satellite, the first atmospheric
composition mission in the EU Copernicus Earth Observation Programme
. TROPOMI was launched into a sun-synchronous low Earth
orbit on Friday 13 October 2017. The ascending node of the orbit crosses the
Equator at 13:30 local solar time. The four imaging spectrometers of TROPOMI
measure the spectral radiance scattered at nadir from the sunlit part of the
atmosphere and the solar spectral irradiance, in the 270–2385 nm
wavelength range at 0.2–0.5 nm resolution. The field of view at
nadir produces ground pixels of 5.5×3.5km2
(along×across track) since the pixel size switch of 6 August 2019 and
of 7×3.5km2 before. The large swath width of 2600 km
across track produces almost a daily coverage of the global (sunlit)
atmosphere. After spectral and radiometric calibration of the Earth radiance
and solar irradiance data , operational
data processors retrieve the column abundance of several atmospheric trace
gases related to air quality, climate, stratospheric ozone depletion, UV
radiation and environmental hazards. They retrieve in particular the vertical column amount of ozone and the cloud parameters needed for the computation of tropospheric ozone by application of the CCD technique described hereafter.
Convective cloud differential algorithm
Unlike other TROPOMI atmospheric data products, the tropospheric ozone column
data are not retrieved directly from the radiance data using an inversion
scheme. Rather, they are derived from total ozone column and cloud data using
the convective cloud differential approach (CCD), a technique that has been
applied successfully to many other sensors such as TOMS, GOME, SCIAMACHY, OMI
and GOME-2
.
The TROPOMI implementation inherits from the implementation used for earlier GOME-type sensors . The
first step consists of selecting ozone columns retrieved by the GODFIT V4
algorithm over deep
convective clouds in the tropical eastern Indian and western Pacific oceans
(20∘ S–20∘ N, 70∘ E–170∘ W). Outside
this reference region, thick, high and highly reflective clouds occur less
frequently. Deep convective clouds are identified by high cloud fraction
(≥ 0.8), high cloud albedo (≥0.8) and low effective cloud pressure
(≤300hPa) – information which is retrieved using the
OCRA/ROCINN_CRB algorithm by the UPAS V1 data processor
. In a second (standardisation) step,
the missing (or redundant) ozone column between the fixed reference level of
270 hPa (≃10.5km) and the retrieved effective cloud
pressure is added (or subtracted) using estimates from the
ozone profile climatology. The standardised columns
are subsequently averaged over 5 d windows and over 0.5∘ latitude
bins and then smoothed with a running mean over 2.5∘ latitude to reduce
the effect of undersampling. These values are reported as the reference
stratospheric ozone column (StOC) in the data products and typically include
1000–2000 TOC pixels. Then (step 3), total ozone columns over clear-sky
scenes (CF<0.1) are averaged over 3 d in 0.5∘ latitude by
1∘ longitude grid cells (on average ∼100 TOC pixels). In the
fourth and final step, a tropospheric ozone column (TrOC) is obtained over
each cell by subtracting the corresponding StOC from the cloud-free total
column. This explicitly assumes that StOC is uniform over the entire latitude
belt and representative of the 3 d mean state.
Data record and screening
The resulting TROPOMI tropospheric ozone column product is sampled daily and
represents clear-sky 3 d moving mean ozone column between the surface
and 270 hPa around 13:30 over 0.5∘×1∘ grid
cells between 20∘ S–20∘ N. Hence, the TROPOMI CCD
tropospheric column does not cover the uppermost part of the free troposphere
or the tropical transition layer. Data during the commissioning phase
(7 November 2017 to 30 April 2018) are not considered here since they are not
publicly released. We used 2 complete years of TROPOMI tropospheric ozone
column data (1 May 2018 to 30 April 2020), corresponding to offline processors
V01.01.05–V01.01.08. Differences between versions are negligible and mainly
reflect a change in input TOC or cloud data .
Figure shows the median (panel a) and
68 % interpercentile (panel b) of screened TROPOMI TrOC data over the 2-year
period. Features and patterns in these maps are discussed in subsequent
sections.
Statistics of 2 years of TROPOMI tropospheric O3 column data
(1 May 2018–30 April 2020): (a, b) median and 68 %
interpercentile of tropospheric ozone column; (c) median ex ante
tropospheric ozone column uncertainty. Data are screened according to the
recommendations given by the data providers. Red markers show the location of
the SHADOZ sites considered in this study. Red isolines trace the 500, 1000
and 2000 m surface elevation levels. Corresponding sampling
statistics and time are shown in Fig. S1 in the Supplement.
Only data with a quality assurance value above 0.7 are retained for this
analysis, as recommended by the data provider . The quality
assurance variable combines different quality criteria, primarily the mean
quality of the selected cloud-free TOC pixels and several indicators of
elevated error in StOC (large zonal variability in reference sector, too few
deep convective clouds, large difference between latitude bands). Additional
quality criteria include the sample size and standard deviation of cloud-free
TOC pixels and the fraction of negative TrOC in the spatio-temporal window.
If the final TrOC value is negative it is overwritten by a fill value.
Applying the recommended screening removes about 5 %–10 %
of the data in the inner tropics and up to 40 %–50 % close
to 20∘ latitude. In the outer tropics, mostly wintertime data are
rejected (Fig. S1 in the Supplement) because there is a lack of deep convective
clouds while the Intertropical Convergence Zone (ITCZ) is located in the
opposite hemisphere. The seasonal migration of the ITCZ
therefore leads to a temporal sampling bias in yearly averaged TrOC data.
Only data in the inner tropics (12∘ S–10∘ N) exhibit a
uniform temporal sampling distribution over a 12-month period (Fig. S1,
bottom, white shade). In the outer tropics, the sampling barycentre of annual
mean TrOC is located either in February (SH) or in August (NH).
Sources of uncertainty
Errors in the derivation of tropospheric ozone column data originate in the
retrieval of total ozone and cloud information, in the validity of underlying
assumptions, and in the representativeness of the data sample. Potentially the
most important source of systematic error in TrOC lies in a systematic
difference between clear-sky and fully cloudy TOC. The cloud-fraction
dependence of the bias in the offline total ozone column product in the
tropical belt is therefore assessed by comparisons to co-located Brewer
measurements in Sect. . This analysis does not reveal a
difference in bias between cloud-free and very cloudy scenes of more than
0.2 %.
An important source of random uncertainty in TrOC comes from the assumption
that the ozone field in the stratosphere and tropical transition layer (TTL) is
uniform along longitude. Various studies, each using complementary
techniques, concluded that this is a reasonable approximation at low latitudes
(<15∘) where the observed root mean square of stratospheric and TTL ozone within a
latitude belt ranges between 2–5 DU. In addition
to geophysical variability, sampling errors related to the presence/absence of
deep convective clouds over the reference region will affect the StOC
estimate. Any error in StOC propagates directly to the errors of all TrOC
values over the corresponding latitude belt. Sampling effects may play a role
too in the set of cloud-free TOC pixels which are used to derive the final
TrOC value.
Biases in the retrieval of effective cloud pressure for convective clouds will
bias the 270 hPa standardisation step and therefore cause systematic
error in StOC. Ozone mixing ratios are generally small over the Indian and
Pacific oceans, so the final impact on TrOC should be fairly small. The
standardisation step also introduces some random uncertainty since an
O3 climatology which is representative of the mean state over
decadal timescales is used.
The uncertainty reported in the TROPOMI data files reflects a purely random
component and is computed as the standard deviation of the set of estimated
TrOC within each spatio-temporal bin. It is therefore the combined result of
random measurement uncertainty and random geophysical variability within the
bin. However, not included is the random representativeness uncertainty resulting from inhomogeneous spatio-temporal sampling over the bin or the
random uncertainty in the estimate of the StOC reference. Reported
uncertainties typically range between 1.7–2.1 DU (∼7%–13 %) and increase somewhat (by at most
0.5 DU) over the South Atlantic Anomaly
(Fig. c). In the following, we use the terms ex ante
uncertainty and ex post uncertainty to distinguish the reported uncertainty
from the uncertainty estimated from comparisons to other observations
.
Reference TrOC data records and harmonisationOzonesonde
Attached to a small meteorological balloon, ozonesondes measure in situ the
abundance of tropospheric and lower stratospheric ozone at an effective
vertical resolution of 100–200 m. The instrument consists of an
electrochemical concentration cell (ECC) that produces an electrical current
proportional to the partial pressure of ozone and references
therein. The ozonesonde is mated to a meteorological
radiosonde which provides simultaneous readings of timestamp, GPS geolocation,
ambient pressure and temperature. Soundings in the
20∘ S–20∘ N belt are generally made weekly or every other
week, at nine stations affiliated to NASA's Southern Hemisphere ADditional
OZonesondes programme and the Network
for the Detection of Atmospheric Composition Change (NDACC,
). Most sites launch their balloons at a fairly
fixed local solar time, but the median launch time does vary considerably
across the network, between 04:45 and 12:45 (Fig. S5 in the Supplement). We
considered rapid delivery data from the NOAA and KNMI archives and homogenised
version 6 data from the SHADOZ data archive
(https://tropo.gsfc.nasa.gov/shadoz, last access: 24 November 2021).
Ozonesonde profile data are screened as described in
. Additionally, Costa Rica soundings flying through a
plume of the nearby Turrialba volcano were discarded, as SO2
interferes strongly with the ECC cell, and this causes ozone readings that are too low
over a considerable portion of the troposphere . After
screening, the sonde ozone volume mixing ratio profiles are integrated using
Eq. () (see Appendix A) from the first measurement
level up to 270 hPa to obtain a tropospheric column over the same
vertical range as TROPOMI. The integration method implicitly assumes that
TROPOMI TrOC data have full and uniform sensitivity over the entire partial
column and no sensitivity at higher levels. In rare cases, the first reported
sonde reading occurs more than 100 m above the surface. If the
log(pressure) range sensed by the sonde misses 3 % or more of the
surface–270 hPa range, the sonde flight is discarded.
Over the past few years the ozonesonde community made significant progress in
refining the characterisation of errors and in correcting for inhomogeneities
in the data records across the network. The latter process references the
sonde records to a UV-absorption photometer
, which, in principle, eliminates
biases between sites as well as biases between different periods at a single
site. This approach has reduced residual systematic differences to about
5 % (or ∼1DU) in the troposphere
.
In the tropical troposphere, total uncertainty estimates typically lie in the
5 %–15 % range for pressures larger than 270 hPa. However, so far, the error components
are rarely separated in the data files, which makes it impossible to propagate
errors for post-processed sonde data in a correct fashion. The nature of the
error (random or systematic) for each domain (vertical, horizontal, time)
determines how it propagates through the post-processing routine of the data
user. This will be different for the integration into a tropospheric column,
for the computation of monthly averages or regional means.
The SHADOZ network pioneers the provision of a more complete error
decomposition. Unfortunately, the error due to the background current in the
sonde, which dominates the uncertainty budget in the tropical troposphere, is
not differentiated from the sensor current error in the V1 uncertainty data
files. Both error sources exhibit random behaviour over all domains
(vertical, between flights at a site, between sites) except for the background
current error which is systematic in the vertical domain. Since other large
sources of error also display systematic behaviour in the vertical, we assume
that the reported total error is fully correlated between pressure levels.
Hence, a slightly conservative error on the tropospheric column is obtained
by applying Eq. () (in Appendix) to the ozone volume
mixing ratio error profile. This procedure was also followed by
in their comparison of total column data. We find
tropospheric column errors of 0.9–1.5 DU or
5 %–8 %.
reported an unexplained 5 %–10 %
drop in stratospheric ozone levels around 2014–2016 at several sites that
launch ECC instruments manufactured by the ENSCI corporation. All but two
SHADOZ sites (Paramaribo and Kuala Lumpur) used in this work are affected.
The cause of the drop is not understood and not included in the published
uncertainty budgets e.g.. It is
possibly related to a change in ENSCI instrumentation, although other causal
factors are explored as well. But the artificial decline was not noticed in
the tropospheric part of the profiles at the impacted sonde sites with the
exception of Costa Rica. There, a drop-off occurred in early 2016 in the
troposphere . Efforts to correct for the recent low
bias in Costa Rica sonde data are promising, and a revised record is being
prepared . The comparisons over Costa Rica are therefore
not considered for the estimation of TROPOMI bias. Since the break point
occurred before the start of the TROPOMI data record, it does not alter
estimates of correlation, dispersion or temporal stability of the comparisons.
We therefore assume that the drop-off issue can be ignored in our analysis of
tropospheric ozone data.
OMI and GOME-2B
OMI and GOME-2B are part of the European family of UV–visible nadir-viewing
spectrometers pioneered in the mid-1990s with ERS-2 GOME, a family of which
TROPOMI is the youngest member. OMI resides on the Aura satellite launched in
2004, and GOME-2 is part of the MetOp-B platform deployed in 2012. These sensors
are in a sun-synchronous low Earth orbit with an Equator crossing local time
of 09:30 (GOME-2B, descending node) and 13:45 (OMI, ascending node). As part
of the European Space Agency's Climate Change Initiative (CCI), total ozone
columns have been retrieved for all European sensors using the GODFIT V4
algorithm , and these were subsequently
passed through the CCD algorithm to infer gridded tropospheric ozone columns
in the tropical belt .
Here, we use a slightly modified version of the OMI and GOME-2B CCI data
records spanning November 2017 to, respectively, February 2020 and January
2020. The spatio-temporal sampling resolution was aligned to that of TROPOMI
to reduce sampling and smoothing mismatch uncertainties in our comparison (see
Sect. ). The original OMI and GOME-2B CCI data
represent monthly-sampled monthly mean ozone columns between the surface and
10 km for 1.25∘×2.5∘ cells (latitude × longitude) between 20∘ S–20∘ N. The data used here are
daily-sampled 5 d mean ozone columns between the surface and
270 hPa in 1∘×2∘ cells. Grid cells and
GOME-2B total ozone pixel footprints are of similar magnitude
(Table ); therefore, the east–west overlap is used as a
weight in the gridding process for this sensor.
Sampling and smoothing properties of tropical tropospheric ozone column data
records (surface–270 hPa, 20∘ S–20∘ N). The
second column displays latitude by longitude for the tropospheric column, as well as
pixel footprint (along track by across track) at nadir of the total ozone columns used by the CCD algorithm. Data records are ordered by smoothing area.
SensorHorizontalTime (local solar)Otherozonesondeflight path, drift <5–15 km, nine sites30 min ascent, sampled 1–2×/month at 02:20–15:20all weatherTROPOMImean over 0.5∘×1∘ using 5.5×3.5km2 pixels*3 d mean, sampled daily at 13:30cloud-freeOMImean over 1∘×2∘ using 13×24km2 pixels5 d mean, sampled daily at 13:45cloud-freeGOME-2Bmean over 1∘×2∘ using 80×40km2 pixels5 d mean, sampled daily at 09:30cloud-free
* TROPOMI pixel footprints were slightly larger before 6 August 2019, 7 km along track.
Uncertainties reported in the GOME-2B and OMI data files are computed in the
same way as for TROPOMI. Ex ante random uncertainty for OMI TrOC data lies in
the 2.5–3.1 DU range, for GOME-2B these are slightly smaller
1.8–2.6 DU, but the latter exhibit a much more pronounced peak around
the South Atlantic Anomaly (4.5–6.5 DU compared to
3.5–4.5 DU for OMI). Systematic errors were not propagated through
the measurement process. Instead, systematic measurement uncertainty is
indirectly probed by comparison to ground-based and satellite data records, an
approach that is followed here as well. report a ∼3DU low bias of GOME-2B with respect to OMI; the authors suggest
this may be caused by the different local measurement time in the presence of
a diurnal cycle in tropospheric ozone. We discuss this further in
Sect. .
Co-location with TROPOMI
Table summarises the sampling and smoothing properties
of the considered TrOC data records. Thanks to the excellent spatio-temporal
coverage of TROPOMI TrOC data, tight co-location windows can be used without
loss of comparison pairs. Ozonesonde measurements at a given time and from a
given geolocation are compared to the corresponding satellite space–time
cells, which limits the mismatch between the TROPOMI grid cell centre and the
ozonesonde data to a maximum of ±1.5d, ±0.25∘ in
latitude and ±0.5∘ in longitude. Sonde TrOC data are averaged
whenever multiple launches occur at a single station in TROPOMI's 3 d
window, although this is very rare. Mismatch and smoothing uncertainty caused
by spatial and temporal variability in the tropospheric ozone field are
discussed in Sects. and .
Satellite intercomparisons are carried out on the coarser horizontal grid of
OMI and GOME-2B. For each OMI or GOME-2B spatial cell, four TROPOMI cells are
averaged with equal weight. The reported TROPOMI uncertainties are assumed
uncorrelated and propagated accordingly. TROPOMI data were not averaged over
the time domain, which leaves a difference in temporal smoothing (3 versus
5 d).
Comparison to ozonesondes and satellites
The first part of our analysis consists of the comparison of TROPOMI TrOC data
to co-located measurements by ozonesonde and satellite instruments. Its
purpose is to derive statistical indicators of TROPOMI data quality, such as
systematic error or random uncertainty, and to study their spatio-temporal
structure. Robust estimators are used to limit the impact of outliers in the
(relatively) short and sparsely sampled co-location data set. In subsequent
sections, we take a closer look at patterns in the TrOC maps in order to infer
additional uncertainties caused by sampling (Sect. )
and to verify the ability of TROPOMI to record known geophysical patterns
(Sect. ). Before going into the presentation of
TROPOMI TrOC comparison results, we highlight the importance of confounding
factors in the interpretation of these comparisons.
Comparison error budgetGeneral
The geophysical interpretation of atmospheric measurements requires proper
consideration of the random and systematic measurement uncertainty but also
understanding of the location and extent of the probed air mass. Indeed, a
measurement of the geophysical state at a four-dimensional point in space and
time even by a hypothetical, perfectly accurate instrument (i.e. zero
measurement error) will deviate from its true state since the information
probed by the measurement process is smeared out and/or displaced from the
targeted location. Such a deviation is often referred to as
representativeness (or mismatch) error, and it exhibits random and/or
systematic behaviour depending on the spatio-temporal structure in the
geophysical field and the sampling and smoothing properties of the measurement
system.
A primary objective in the validation of data record X is to quantify its
systematic (βX) and random (σϵX) measurement
uncertainty. The usual approach is to compare the data record to measurements
by another instrument Y. In order, then, to infer the measurement
uncertainty (βX,σϵX) from a set of differences
Δ=X-Y, we must know several nuisance parameters: systematic
(βY) and random (σϵY) measurement uncertainty of
Y, and the systematic (βrepr) and random
(σϵrepr) uncertainty due to the different
representativeness of the two measurements. These nuisance parameters,
furthermore, often depend on time and location. Their magnitude can be
similar to the measurement uncertainty of interest, and they are often
difficult to quantify. In some cases, however, representative observation
operators applied to modelled fields allow us to simulate – in an Observing
System Simulation Experiment (OSSE) – the measurement process sufficiently
well to close the comparison error budget e.g..
A common approach is to reduce the uncertainty from nuisance parameters as
much as possible, e.g. by using a well-calibrated data record as a reference
and/or by harmonising the representation of the data records
. Systematic and random measurement uncertainties for
sonde, OMI and GOME-2B are discussed in Sect. . Calculating
accurate measurement uncertainties generally remains a challenge. Reported
uncertainties (ex ante) are often first-order approximations although, at
times, they fail to include important, poorly understood sources of error. It
is therefore good practice to use ex ante uncertainty estimates with care.
Section also describes how vertically resolved ozonesonde
data are integrated to a partial column, how the data records are co-located
in space and time to reduce errors from differences in sampling, and how the
spatio-temporal resolution of the best resolved data is downgraded to that of
the coarser resolved record to reduce errors from differences in horizontal
smoothing. State-of-the-art models resolve global tropospheric ozone at best
at the scale of 100×100km2, which is
too coarse to simulate the representativeness errors due to geophysical
variability within the co-location window. Hence, a detailed closure of the
comparison error budget for tropospheric ozone records is currently out of
reach see also. In the following, we resort to a
qualitative discussion of representativeness uncertainty and its decomposition
in the temporal, horizontal and vertical domain.
Systematic representativeness uncertainty
A classical source of systematic representativeness error is the difference in
local measurement time in the presence of a diurnal cycle. Not much is known
about the strength of the diurnal cycle of ozone in the tropical troposphere.
Measurements around the globe show clear changes in surface ozone with local
time. The amplitude and timing of minimum/maximum ozone depend on parameters
like the strength of solar radiation driving photochemical reactions and the
presence of precursor emissions in the vicinity of the site
e.g.. Much fewer observations are
available in the boundary layer and, especially, the free troposphere.
Fig. 4 describe a correlation between local time
and ozone mixing ratios in the boundary layer from soundings at the
subtropical Irene station (25.9∘ S, 28.2∘ E, South Africa)
between 1999–2007. characterised the diurnal
variation of ozone close to the surface over Frankfurt (50.0∘ N,
8.6∘ E, Germany) using MOZAIC-IAGOS aircraft data. Both studies
conclude there is no evidence for a cycle in the free troposphere. Annually
averaged ozone columns between the surface and 300 hPa over Frankfurt
are at most ∼1.1DU larger at noon (12:00–18:00) than
during nighttime (21:00–09:00). The mismatch in measurement time with
respect to TROPOMI is negligible for OMI but not for sonde or GOME-2B
(Table , Fig. S5). If the diurnal cycle in the tropics
resembles the one over Frankfurt, we may expect a systematic error of about
+1 DU in comparisons of TROPOMI minus GOME-2B or ozonesonde at most
sites. Mismatch error would be smaller (∼0.5DU, or less) in
comparisons to Natal, Paramaribo (from January 2019 onward) and Ascension
Island sondes since these sites launch around noon, closer to the TROPOMI
overpass time (Fig. S5). Paramaribo flights prior to 2019 were launched
around 09:45 local solar time, leading to larger mismatch with TROPOMI.
Another potential source of systematic representativeness error is the
difference in vertical smoothing of the troposphere by the ozonesonde and by
TROPOMI. Since the vertical smoothing by TROPOMI, OMI and GOME-2B total ozone
retrieval is similar, this error term is negligible in the satellite
intercomparisons. In the absence of vertical smoothing diagnostics in the
TROPOMI data products, we integrated the sonde profiles with equal weight
between the surface and 270 hPa. In reality, TROPOMI does not have
full or uniform vertical sensitivity across the troposphere, but it is a
challenge to quantify its smoothing properties. The total ozone retrieval is
less than 50 % sensitive to ozone below about 700 hPa (∼3km); climatological data fill in the missing information
. Over cloudy scenes, on the other hand, the TROPOMI total
ozone retrieval is 20 % too sensitive in the region just above the
cloud top. Another complexity in deriving usable vertical smoothing
diagnostics lies in the fact that the CCD algorithm relies on averages of a
large number of total ozone scenes, each with their proper vertical smoothing
properties. It is outside the scope of this work to study how the vertical
smoothing properties for single TOC pixels in cloud-free and cloudy scenes
propagate into a regional multi-day mean tropospheric column. The outcome
will depend on the quality of the climatology and vary with the actual ozone
profile, the surface albedo and cloud properties. At this point, it is too
premature to give an estimate, its sign or even the nature (systematic,
random) of the smoothing error.
Random representativeness uncertainty
Spatial correlation length in the troposphere is about 500 km, which is larger
than the grid cells in the satellite products and much larger than the
horizontal distance travelled by ozonesonde. Temporal correlation length is
about 1.5–3.5 d, which is close to or smaller than the averaging
window used by the CCD algorithm (Table ). These
correlation lengths were estimated by from an analysis of
ozonesonde data in Europe and the USA. If these hold for tropical conditions
as well, we expect that temporal smoothing difference is the main contributor
to random representativeness error.
Satellite TrOC data are smoothed over a much larger time window and region
than ozonesonde data (Table ). Hence, random
representativeness uncertainty will be significantly larger in satellite-to-sonde comparisons. Again, state-of-the-art models are too coarse to simulate
their magnitude. Differences in smoothing between satellite sensors are much
smaller, and therefore the random representativeness errors in the satellite
intercomparisons will be smaller. The smallest random uncertainty is expected
in the comparison of TROPOMI and OMI. Pixel footprints of total ozone
retrievals for these two sounders are much smaller than the cell size of the
TrOC data record; therefore, the error due to horizontal smoothing differences
should be negligible. GOME-2B pixels, on the other hand, are resolved fairly
close (80×40km2) to the TrOC cell size (1∘ by
2∘). This effectively leads to horizontal smearing in GOME-2B TrOC
and a larger random uncertainty in TROPOMI–GOME-2B comparisons.
Temporal correlation
Figure shows time series of ozonesonde (red)
and TROPOMI (black) TrOC data over nine SHADOZ stations. Temporal co-location
is neglected in this figure to highlight the variability seen by TROPOMI over
the full range of timescales. Both records show a coherent picture of the
known large-scale spatio-temporal patterns in tropical tropospheric ozone:
zonal wave-one, annual cycle, biomass burning and convective activity. We
return to the verification of these geophysical signals in
Sect. . In the rest of
Sect. we consider TrOC data that are spatially and
temporally co-located.
Time series of spatially co-located TROPOMI (black) and ozonesonde (red) tropospheric ozone column data over nine SHADOZ sites.
Pearson correlation coefficients are estimated using the skipped correlation
measure r, which is robust against outliers . TROPOMI
correlates reasonably well with spatio-temporally co-located ozonesonde data;
r ranges between 45 %–75 % over the network and averages
at 61 % (Fig. ). Correlation with
satellite data records is (much) stronger, between 60 % and
95 %, and it traces the contours of variability in the TrOC field
(Fig. S2 in the Supplement). In regions with σTrOC>6DU
over the course of a year (e.g. South America), the correlation is more than
80 %. Correlation is weaker, less than 70 %, in regions of
low atmospheric variability (σTrOC<4DU, e.g.
equatorial western Pacific), also because TrOC values are generally lower at
these locations, and hence the relative importance of uncorrelated random
measurement error is larger.
The different correlation strength of TROPOMI with the three reference data
records reflects, in part, the difference in representativeness of the data
records. TROPOMI–OMI correlations are 2 %–4 % higher than
for GOME-2B, since the horizontal smoothing of the latter sensor differs more
from TROPOMI than OMI's does. The weaker correlation TROPOMI–ozonesonde
coincides with a large difference in spatio-temporal smoothing. Similar
correlations are found in our comparison analysis of OMI (50 %) and
GOME-2B (59 %) to ozonesonde (Figs. S3 and S4 in the Supplement). An
intercomparison of all GOME-type TrOC records generated within ESA's
Ozone_cci project indicates correlations with SHADOZ ozonesonde data between
52 %–63 %. Higher correlations
(80 %–90 %) are reported by between
IASI-A and ozonesonde or FTIR for tropical TrOC data. However, this is an
analysis of single-pixel TrOC data, and hence correlations are likely higher
since the random uncertainty due to co-location mismatch is smaller. Overall,
the reasonably high correlations indicate that TROPOMI TrOC data capture the
general temporal variability observed by the other data records.
Temporal stability
Comparisons of TROPOMI to sonde and satellite display stable statistical
dispersion over the 2-year record. However, the mean level in the satellite
intercomparisons exhibits a clear modulation with 0.7–1.2 DU
amplitude across the entire tropical belt
(Fig. ). Such a small effect can currently
not be seen in the sonde analysis but may be with longer time series
(Fig. ).
Figure suggests a seasonal pattern in the
anomaly of TROPOMI minus OMI (top) or GOME-2B (bottom) with respect to the
mean value over the entire period. Details of the bias anomaly pattern in
both satellite intercomparisons are reasonably consistent. A minimum occurred
in September–January 2018 and a maximum during March–July 2019. The phase of
the modulation is uniform across latitude in the GOME-2B results but varies
by ∼3 months in the OMI comparisons. Also, the modulation amplitude in
the latter analysis is 0.3–0.5 DU larger. Longer time series are
needed to confirm whether these changes in TROPOMI bias persist as a cyclic
pattern or whether it was merely a single episode, as well as whether a
coherent seasonal bias in GOME-2B and OMI data can be ruled out.
Latitude–time section of the anomaly of the TROPOMI tropospheric ozone column with respect to its mean bias with OMI (a) and GOME-2B (b) from April 2018 through February 2020. Zonal mean differences of the bias have been subtracted in order to better highlight the meridian coherence of the change over time.
Time series of the difference between spatially and temporally co-located TROPOMI and ozonesonde tropospheric ozone column data over nine SHADOZ sites. Positive values indicate that TROPOMI overestimates the ozonesonde value. Blue line and area show median (Q50) and 68 % interpercentile (IP68) over the entire period.
Two additional temporal features in the ground-based comparisons are
noteworthy. The most striking is that TROPOMI observes 5–10 DU
higher TrOC values than the Paramaribo ozonesonde during the 2018 and 2019
biomass burning seasons (Fig. ). Over this
site, the rapid increase (July) and decline (November) in TROPOMI data is not,
or is only weakly, present in the ozonesonde record. This discrepancy leads to
the low correlation of 45 % at this site. At other sites around the
Atlantic basin (Ascension Island, Nairobi and Costa Rica) TROPOMI bias
increases over the July–September 2018 period, like at Paramaribo, but the
pattern does not persist as clearly in 2019. A short high-bias episode at
Natal and Ascension Island occurred in October–November 2019, coinciding with
lower-than-usual (5–10 DU) sonde readings. Figures S3 and S4 in
the Supplement show the same temporal pattern in the comparisons of both OMI and
GOME-2B to ozonesonde, implying that it is not related solely to TROPOMI.
Further study is needed to understand whether this simultaneous temporal
change in bias around the Atlantic has a common source for the three satellite
sensors (e.g. instrument design, calibration, total ozone retrieval,
tropospheric ozone derivation through CCD), whether the ozonesonde records
were simultaneously affected by an offset in the measurements during 2018, or
whether the difference in vertical smoothing of the tropospheric column by
sonde and satellite has a larger impact during high-ozone conditions.
A second anomalous event occurred during the last months of 2019 in the
tropical South Pacific, coinciding with a period of widespread bush fires
across Australia. Three ozonesonde flights in October–December 2019 at Suva
recorded higher TrOC values than usual, while co-located TROPOMI data did not.
This leads to a temporary low bias of TROPOMI with respect to sonde that
deviates by 5–15 DU from the rest of the time series
(Fig. , bottom centre). Comparisons at Samoa
show a similar but less pronounced dip in December 2019
(Fig. , bottom right). Inspection of maps of
TROPOMI CO, a tracer for smoke plumes, around the ozone soundings did
not reveal clear evidence of a link with the bush fires ∼3000–4000 km away, in southeast Australia.
Bias
The 50 % quantile of the ΔTrOC distribution is used here as
robust estimator of TROPOMI bias with respect to the other data records. This
bias estimate is an indirect probe of TROPOMI's systematic measurement error
since other systematic error terms are at play (Sect. ).
The Costa Rica comparisons are not included in this part of the analysis due
to a known low bias (Sect. ).
Figures , c and d show
the bias of TROPOMI with respect to ozonesonde data at each SHADOZ site.
TROPOMI generally reports higher values than the ozonesondes by
2.3±1.9DU (or 11.2±9.0%) when averaged over the
network. The error bar represents the statistical dispersion (1σ) of
the bias estimates over the ground-based network. A closer look suggests
three clusters in the bias estimates: three sites (Suva, Kuala Lumpur and
Samoa) where TROPOMI bias is less than ∼1DU
(4 %–6 %), Natal with a bias of +2DU
(9 %) and a group of four (Hilo, Paramaribo, Ascension Island and
Nairobi) with a bias around +4DU (14 %–22 %).
A similar clustering is seen in the ground-based TrOC comparisons to OMI and
GOME-2B (Figs. S3 and S4). Differences in sampling of the diurnal cycle can
not explain this clustering. Instead, we suspect that residual
instrument-related biases exist between the ozonesonde stations. A similar
grouping in the bias between ozonesonde sites has also been noted in total
column and stratospheric profile comparisons between sonde and satellite
(Ryan M. Stauffer, personal
communication, 2021).
TROPOMI also overestimates GOME-2B TrOC data across the entire tropical belt
by 2.3±0.6DU (+13.2±5.2%) on average. Again, the
error bar represents the statistical dispersion (1σ) of the bias
estimates over the tropical belt. The agreement with OMI is much better, with
TROPOMI underestimating OMI data by 0.1±0.7DU
(-0.3±3.2%). The bias displays spatial structure that differs
from one instrument to another. When compared to OMI, TROPOMI biases exhibit
a marked meridian and zonal dependence (Fig. a and b).
Biases in the southern tropics are +(0.2–0.4) and
-(0.6–1.0) DU in the north. Also, biases are larger around the
Greenwich meridian (-0.8 DU) than around the date line (±0.2DU). It is unclear whether the latter finding is related to the
fact that the reference stratospheric ozone column used in the CCD retrieval
is derived in the Pacific sector. In any case, such a zonal structure is not
seen in the TROPOMI comparisons with GOME-2B. The latitudinal dependence in
the latter has the same sign but is much weaker, with only 0.2–0.4 DU
difference between the northern and southern tropics. The GOME-2B comparisons
also show signs of change in bias around coastlines or over high-elevation
terrain. This is not seen in the OMI results, so it may be a systematic
effect in the GOME-2B data or due to the larger difference in horizontal
smoothing. Interestingly, the sonde comparisons show a smaller bias in the
reference region (Suva, Samoa, Kuala Lumpur) like OMI. However, the
1.9 DU scatter in the sonde-based bias estimates and the sparsity of
the network challenge a third and more independent view on these spatial
patterns.
(a, b) Median difference between spatially and temporally co-located TROPOMI tropospheric ozone column and OMI (a) or GOME-2B (b) data; contours trace isolines of surface elevation. (c, d) Longitude (c) and latitude (d) section of mean and standard deviation (1σ) of the ΔTrOC map. Black markers display the median and 68 % interpercentile of co-located TROPOMI minus SHADOZ ozonesonde data. Positive values indicate that TROPOMI TrOC data are biased high with respect to the reference data. GOME-2B results are also offset (yellow dashed) compared to those found with OMI to facilitate comparison of the spatial structure. Mean and standard deviation (1σ) of the TROPOMI bias estimates over the ground-based network (sonde) or tropical belt (satellite) are displayed outside the axis on the right, together with minimum and maximum values (plus signs).
The TROPOMI bias and its structures could be the result of several systematic
terms, such as sampling mismatch of the diurnal cycle, biases in TROPOMI total
ozone and cloud retrievals, and vertical smoothing issues. It is not
straightforward to substantiate these potential sources of biases. In
Sect. we estimated that the difference in
measurement time by the ozonesondes and GOME-2B with respect to TROPOMI could
contribute 0.5–1 DU to the bias results. Correcting for the diurnal
cycle effect would reduce the observed TROPOMI bias relative to sonde from
2.3 to about 1.3–1.8 DU and with respect to the GOME-2B bias
from 2.3 to about 1.3 DU.
Other potential sources of bias are systematic errors affecting the TROPOMI
total ozone column and TROPOMI cloud data used by the CCD algorithm. An
absolute offset in total ozone column (TOC) does not affect TrOC data if it is
zonally invariant since such an offset would be removed by the CCD approach:
indeed, TrOC is essentially the difference between TOC over cloud-free scenes
and TOC over very cloudy, high cloud scenes. However, a cloud dependence of
the TOC bias could produce a systematic error in TrOC data. Ground-based
validation of TROPOMI TOC in concludes that, on average,
there is no clear dependence of TROPOMI TOC bias on cloud parameters, but this
study does not specify to what level of confidence this dependence can be
ruled out. Additional comparisons to Brewer network measurements in the
tropical belt have been performed, indicating less than 0.2 %
difference in TROPOMI TOC bias for cloud-free and very cloudy scenes
(Fig. ), which implies that this source of error leads to a
bias of at most ∼0.4DU in the TrOC data.
Dependence of TROPOMI total ozone column bias on the cloud fraction over the observed scene (TROPOMI L2_O3 OFFL V01.01.01–01.01.08). Comparisons are done over a 2-year period with respect to co-located total ozone column data from Brewer instruments within the tropical belt. The curve is normalised (on a per-station basis) on the mean difference over the 0.2–0.6 CF range, in order to reduce the station-to-station scatter of the mean bias. The two dashed vertical lines identify the cloud-free and cloudy subsets used by the CCD algorithm. Error bars represent the standard deviation (grey) and twice the standard error of the mean (black) satellite-ground difference per CF bin.
A second cloud-related effect comes from a bias in retrieved effective cloud
height. Retrievals of cloud height by the TROPOMI ROCINN_CRB algorithm tend
to be biased low , which leads to TrOC
values that are too high through the normalisation of the above-cloud ozone column to the
270 hPa reference level. It is challenging to validate cloud data
quality since ground-based instruments perceive clouds in a different way than
TROPOMI. report a negative bias in cloud height of
0.5–1.5 km with respect to CLOUDNET cloud mean height data, which
invokes a positive bias in TrOC data of up to ∼0.5DU.
Finally, a difference in vertical smoothing may introduce systematic mismatch
error in the satellite–sonde comparisons as well. Our assumption that
satellite CCD TrOC data have uniform sensitivity across the troposphere is a
first-order approximation. In reality, the total column retrieval is
undersensitive to ozone in the boundary layer and slightly oversensitive to
ozone just above the cloud top. Since no vertical smoothing diagnostics are
provided by the S5P team, it is not possible to verify whether this leads to a
systematic error in ozonesonde comparisons, nor what its sign or magnitude
would be. The provision of such diagnostics for CCD data is currently being
considered by the S5P MPC. This effect plays no role in the satellite
comparisons since the vertical smoothing properties of the sensors are
similar.
Dispersion in pairwise comparisons
Figures and show the
dispersion in comparisons of TROPOMI TrOC to co-located data from the three
reference data records. Dispersion is here estimated as half the interval
between the 16 % and 84 % quantiles of the ΔTrOC
distribution, which corresponds to 1 standard deviation of a Gaussian
distribution. At individual sonde stations the dispersion estimates range
between 3.0–7.4 DU (or 12 %–34 %),
while mean dispersion and its standard deviation across the network equal
4.6±1.3DU (23±7%). Dispersion for
TROPOMI–satellite intercomparisons is generally considerably smaller than the
sonde results: 2.6±0.5DU (14±5%) with respect to OMI and
2.9±0.6DU (19±7%) with respect to GOME-2B. Furthermore, a
clear meridian dependence is found in the satellite intercomparisons, with
minimal dispersion at the Equator and (more than) 1 DU larger values
in the outer tropics. Such a dependence is less clear, but at least not
inconsistent, in the ozonesonde comparisons. Also, satellite comparisons show
an interesting contrast between dispersion over land or ocean in some regions,
especially over South America and Central Africa. The effect is strongest for
TROPOMI–GOME-2B.
Like Fig. but for the 68 % interpercentile of the differences (dispersion). (c, d) Thin lines show combined ex ante uncertainty (1σ) for the satellite intercomparison. (d) Mean and standard deviation (1σ) of the dispersion estimates over the ground-based network (sonde) or tropical belt (satellite) are displayed outside the axis, together with minimum and maximum values (plus signs).
Differences in smoothing and sampling by the sensors explain the observed
difference in dispersion values and their spatial structure
(Sect. ). Dispersion values in the GOME-2B comparisons
are larger (by 0.2–0.4 DU) than for the OMI comparisons, most likely
due to the larger footprints of the GOME-2B total ozone retrievals.
Dispersion is even larger in the ozonesonde analysis since virtually
point-like sonde TrOC measurements are compared to space–time-averaged TROPOMI
data. In locations where the tropospheric ozone field is more homogeneous in
time and space, e.g. around the Equator, mismatch between sensors will not
contribute as much to their comparison than where variability is larger,
e.g. in the outer tropics. This explains (at least partially) the latitudinal
dependence of dispersion found in all analyses and further highlights the
challenge of assessing TROPOMI random uncertainty from pairwise comparisons.
Random measurement uncertainty from triple co-locations
To overcome the limitations in pairwise comparisons, such as the one faced in
the previous section, proposed analysing co-locations of
three data records {X,Y,Z}. Under certain assumptions, the triple
co-location technique (TC) allows the estimation of random measurement uncertainty for
each of these data sets {σϵX,σϵY,σϵZ}. The squared random measurement uncertainty, i.e. the
variance of the random measurement errors, of data record X can be derived
from a combination of variance and pairwise covariances of the measurement
triplets,
σϵX2=σX2-σXYσXZσYZ,
under the following assumptions: (1) the measurement is a linear function of the
true signal with additive zero-mean random measurement noise; (2) measurement
errors and true signal are stationary; (3) measurement errors and true signal
are independent; and (4) measurement errors ϵX, ϵY and
ϵZ are independent . The last
assumption is generally the more important one but also often difficult to
validate. For instance, if one sensor has a coarser spatio-temporal
resolution than the other sensors, then it will miss any small-scale
geophysical variability picked up by the higher-resolution pair. This
small-scale geophysical signal can be seen as a representation error with
respect to the lower-resolution perspective of the same state. Since these
errors are correlated for the high-resolution pair, a representation error
variance term should be added to Eq. (). Generally,
this term is challenging to quantify and therefore often neglected. In
contrast, if one sensor has a higher resolution than the other pair,
small-scale variability is picked up by just one instrument and the larger-scale variability by all three. Hence, all errors will remain uncorrelated,
and the representation term vanishes .
A second useful metric accessible through TC is the variance of the
geophysical signal θX as measured by sensor X, σθX2=σX2-σϵX2, which scales quadratically with the
multiplicative systematic error in the measurement process.
σθX2 can be further related to the variance of the random
measurement errors to obtain a signal-to-noise ratio (SNR). In regions of
low atmospheric variability (e.g. in the equatorial Indian and western
Pacific oceans, Fig. b) even a small measurement
uncertainty may be too high to pick up the geophysical variability of
interest, while higher natural variability at other locations eases
requirements on measurement uncertainty. Here, we rewrite the signal-to-noise
ratio (in decibel units) of data record X from Eq. 14
in terms of its variance and its error variance:
SNRX=10log10σX2-σϵX2σϵX2.
The three metrics derived through triple co-location (σϵX,
σθX and SNRX) provide complementary information
about the quality of data record X. Corresponding
estimates for data records Y and Z are obtained by appropriate permutation
of X, Y and Z.
We perform an analysis of co-located TrOC triplets TROPOMI, OMI and GOME-2B.
Prior to the calculation of the covariance matrices, outliers in the data
records were removed using the Hampel identifier. We argue that
representation errors due to differences in spatial and temporal smoothing of
the true TrOC field are negligible. Error cross-correlations in the spatial
domain may occur for the TROPOMI–OMI pair since the resolution of GOME-2B
total ozone column retrievals is markedly lower. However, these errors will be
diluted by the temporal smoothing applied by the CCD algorithm. In addition,
representation errors in the temporal domain are negligible since TROPOMI
resolution (3 d) is finer than for its two predecessors (both
5 d). We therefore neglect error correlations due to differences in
spatio-temporal resolution and apply Eq. () to estimate
random measurement uncertainty.
Figure shows estimates of random measurement
uncertainty (ex post), and these are compared to the mean value of the
measurement uncertainty reported in the data files (ex ante). Ex post
uncertainty estimates for TROPOMI (top right) increase from the Equator
(1.5 DU) to the outer tropics (2.2–2.7 DU). Spatial
structure and magnitude of the ex ante uncertainty is fairly similar.
Reported random uncertainties are slightly conservative in the southern
tropics but not more than 0.4 DU. Overall, the reasonable agreement
between ex ante and ex post uncertainties lends confidence that the
uncertainty values reported in the TROPOMI data files are indeed realistic.
Top left: TROPOMI random measurement uncertainty estimated from co-located TrOC triplets TROPOMI, OMI and GOME-2B. Other panels: latitudinal section of estimated (ex post) and reported (ex ante) random measurement uncertainty for the three sensors. Shaded areas indicate 1 standard deviation over the zonal domain.
TROPOMI ex post uncertainty is notably smaller than the estimates for OMI
(1.9–2.6 DU) and GOME-2B (1.9–3.0 DU), even though TROPOMI
grid cells are 4 times smaller. The difference in ex post estimates is
clearly larger over the South Atlantic Anomaly, especially for GOME-2B. The
better performance of TROPOMI compared to its predecessors is also clear from
the signal-to-noise ratio estimates (Fig. ). TROPOMI SNR
values are generally 1–1.5 and 2–3 dB larger than, respectively, OMI
and GOME-2B. The spatial structure of SNR is similar for all three
instruments: maximal over the southeastern Pacific, South America, and the
Atlantic basin and minimal over the Indian and western Pacific oceans in the
innermost tropics (coinciding with a minimum in natural variability of TrOC).
As a result of the larger measurement uncertainties, signal-to-noise ratios
for GOME-2B and OMI drop over the South Atlantic Anomaly region with respect
to TROPOMI.
Maps (a, c, e) and latitudinal sections (b, d) of
the signal-to-noise ratio for TROPOMI, OMI and
GOME-2B. Shaded areas indicate 1 standard deviation over the
zonal domain.
Figure (centre right) also shows that OMI ex ante
uncertainties are overestimated by 0.3–1.0 DU over the entire
tropical belt. Furthermore, sharp peaks were noticed in both OMI ex ante and
ex post uncertainty aligned with the orbital track (not shown here). These
are likely due to the row anomaly in OMI Level-1 radiance data; efforts are
ongoing to remove these from the TrOC data. Reported GOME-2B uncertainties,
on the other hand, are generally fairly realistic (within 0.5 DU from
ex post) except over the South Atlantic Anomaly where ex ante values are
clearly too high by more than 2 DU (this causes the larger spread in
the Southern Hemisphere in Fig. , bottom right).
The GOME-2B results also hint at a maximal contrast in ex ante versus ex post
uncertainty (i.e. optimistic versus conservative) aligned with coastlines
(not shown here).
Sampling errors at small scales
Some applications may require TrOC data at the finest possible spatio-temporal
resolution, in which case random errors can not be simply averaged out.
However, two sources of non-geophysical random uncertainty in TROPOMI data can
be (partially) dealt with or, at the least, should be considered in the
interpretation of analysis results. Both types of error originate in the
spatio-temporal sampling pattern resulting from the interplay between
TROPOMI's orbit and cloud coverage. These sampling errors are correlated at
small scales and can dominate the total error. The first type of sampling
error leads to a banded structure in TrOC (0.5–1 DU) along latitude,
which gradually moves north or south over several days. The second type of
error can be larger (up to 5 DU and more), leading to very localised
artificial gradients in time and space.
Sampling of deep convective cloud StOC scenes
Banded structure in the latitude domain can be noted in quite a few TrOC maps,
especially in the outer tropics. Such bands appear where the reference
stratospheric ozone column (StOC) has increased error, since this reference is
used to infer TrOC for the entire band (Sect ).
Increased error in StOC will, e.g., result from a limited sample of deep
convective clouds over the Pacific reference sector in the 5 d averaging
period. The location of these high, opaque clouds changes over time, thereby
leading to spatio-temporal correlations in the errors at small scales.
One way to estimate StOC sampling errors is to consider the difference in StOC
between consecutive 0.5∘ latitude bands (referred to as ΔStOC),
since this should be a fairly smooth function at small scales.
Figure illustrates ΔStOC values of about
2.5 DU close to 10∘ S (top right), which leave a visible
imprint on the TrOC map of 21 June 2019 (top left). Oscillations in
ΔStOC are clear for this particular case, but these are, in fact, seen
in most maps. The bottom left panel proves this point, as it shows the
difference between ΔStOC and the latitude- and time-smoothed
ΔStOC field (±2.5∘ by ±1d). The smooth
field acts as an approximation of the unknown true state, and hence the
difference can be interpreted as the error in ΔStOC. We find that the
StOC sampling error is strongly correlated in latitude and time across the
entire tropical belt, oscillations in latitude exhibit a period of
2–3∘, and these structures often persist over 1–2 weeks. These
scales are larger than the averaging window used to derive StOC (0.5∘
and 5 d). Larger errors are found in the outer tropics, especially
during wintertime, which is when the ITCZ is located in the opposite
hemisphere. About 10 % of the TROPOMI data at latitudes larger than
10∘ have a StOC sampling error larger than 0.6–0.8 DU
(Fig. d, thick red line). In contrast, in the inner
tropics, such large errors are found in just 1 %–2 % of the
data (thin green and red lines). The mean StOC sampling error is less than
0.05 DU, so this effect does not contribute to TROPOMI systematic
error.
(a) Example of banded structure around 10∘ S in the TROPOMI TrOC map of 21 June 2019. (b) Meridian gradient of StOC for 2 d in 2019 (ΔStOC). (c) Anomaly of ΔStOC with respect to latitude- and time-smoothed ΔStOC (May–October 2019). (d) Cumulative distribution function of the absolute value of ΔStOC anomaly (thin lines correspond to 5 %, 2 % and 1 % quantile).
TROPOMI users interested in fine-scale TrOC patterns are strongly advised to
consider fine-scale structure of StOC as well. This may help them reduce the
impact of the StOC sampling error on TrOC data or provide the contextual
information to better interpret TrOC patterns. Also, if users choose to relax
the QA screening threshold, then doing so will introduce considerably more
wintertime data in the outer tropics. But this will come at the cost of
additional banded structures of higher amplitudes than reported here, due to
the much larger corresponding StOC sampling errors.
Sampling of cloud-free TOC scenes
A second type of sampling error can have much larger magnitude but is also
more localised in time and space. The CCD algorithm considers total ozone
column (TOC) between the surface and top of atmosphere over cloud-free scenes,
which are then averaged over 3 d in 0.5∘ latitude by 1∘
longitude cells. Sampling in time depends on the location of the clouds,
which introduces inhomogeneity. In some cases, cloud-free TOC time stamps in
neighbouring cells will have a very different barycentre. In conjunction with
variability in the true TrOC field over the scale of the 3 d time window,
the obtained TrOC may differ by several DU. This TOC sampling effect
is often noticed in sequences of TrOC maps, as a localised front of unnatural
changes in TrOC oriented along and propagating with the set of TROPOMI orbits
during the 3 d window.
We infer estimates of this TOC sampling error by collecting the sampling time
of all quality-controlled, cloud-free TOC values used by the CCD algorithm for
a given TrOC map. For each spatial cell these time stamps are averaged and
referenced to the central time of the TrOC map. Figure
illustrates the temporal inhomogeneity in the sampled cloud-free scenes over
the Central Pacific used for the map of 20 January 2020 (top row). Mean sampling time
for nearby cells differs by the maximum possible amount, 3 d (Fig. c). This difference leaves an imprint on the TrOC map (Fig. a) which
becomes especially clear when the anomaly of TrOC is considered with respect
to the 1-week smoothed TrOC field (Fig. b). The colour scale shows that
TrOC anomalies of neighbouring cells differ by ∼5DU. The
spatial structure of the TrOC anomaly field follows that of the mean sampling
time (contours), which corroborates the causal relationship. It is difficult
to characterise TOC sampling error, since these need identification on a case-by-case basis. Inspection of a number of TrOC maps shows that the effect is
visible in many maps at random locations and of varying magnitude. Errors
are often 5 DU or more, so TOC sampling error dominates the total
error budget at these locations.
Illustration of sampling uncertainty in a region of the TROPOMI TrOC map (a, c) of 20 January 2020 (top row) and 8 October 2019 (bottom row). (b, e) Absolute TrOC anomaly relative to a 7 d moving mean; contours trace isolines of sampling time offset. (c, f) Mean sampling time offset relative to the centre of the averaging window for the clear-sky total ozone columns used by the CCD algorithm. The structure in the mean sampling time field agrees well with that of the TrOC anomaly.
TROPOMI users should understand this type of error to improve their
interpretation of TROPOMI TrOC patterns at the finest scales. A reasonable
indicator of increased TOC sampling error is a difference in TrOC anomaly for
neighbouring cells in conjunction with a difference in mean sampling time.
The latter information is scheduled to be part a future update of the TROPOMI
processor.
Verification of geophysical information
In the last section of this paper we explore how TROPOMI captures known
signals and patterns in the tropospheric ozone field. This analysis acts as a
verification of the ability of the instrument to detect and monitor
geophysical signals of interest and as a demonstration that it outperforms its
predecessors.
Zonal wave-one and surface topography
Figure a shows median TROPOMI TrOC over 2 years (1 May
2018–30 April 2020). The well-known zonal wave-one structure appears
clearly, with elevated columns over the Atlantic basin (due to lightning and
biomass burning) and depleted levels over the Pacific (due to strong
convection in combination with the Walker circulation)
. TROPOMI observes a
maximum median TrOC of 33.6 DU (12.25∘ S, 3.5∘ E)
and a minimum of 10.0 DU (2.25∘ N, 155.5∘ W),
resulting in a 23.6 DU peak-to-trough difference (often referred to as
the wave-one amplitude). The location and amplitude of the wave-one in
2-year-averaged OMI (24.0 DU) and GOME-2B (22.4 DU) data are
similar. inferred a smaller wave-one amplitude of
∼14DU from two decades of SHADOZ ozonesonde data integrated
between the surface and the tropopause. When TROPOMI data are subsampled to
the location of the SHADOZ sites, a more comparable amplitude of
15.2 DU is obtained, giving evidence that the lack of ozonesonde
stations in the deep TrOC trough in the western Pacific is responsible for the
smaller wave-one amplitude estimate from SHADOZ data. Zonal wave-one
amplitudes computed from monthly mean TROPOMI data exhibit a pronounced
seasonal cycle reflecting the varying intensity of biomass burning around the
Atlantic basin . The wave-one pattern is strongest
(∼41DU) during September–November and weakest during May–June
(∼26DU). Again, OMI and GOME-2B yield similar conclusions.
Depressions in tropospheric ozone are expected above high-altitude terrain.
TROPOMI's median TrOC field indeed traces surface elevation (red isolines show
500, 1000 and 2000 ma.s.l. in Fig. a). Topographic
effects are particularly clear over high mountain ranges (e.g. Andes in South
America and New Guinea Highlands), but lower-lying terrain (500 ma.s.l.)
also leaves a noticeable imprint on the TROPOMI TrOC field (e.g. around Gulf
of Aden, equatorial West Africa). This illustrates that averaging 2 years
of TROPOMI data reduces random TrOC error to almost negligible levels.
Biomass burning
Open fires of vegetation release large amounts of volatile organic compounds
and nitrogen oxides into the atmosphere . These
interact photochemically in the smoke plume and produce ozone which is
transported away from the burning area .
Such biomass burning events occur primarily during the dry season in tropical
regions with rainforest or savanna (Africa, South America, Indonesia)
and affect air quality on regional
scales. Monitoring the strength, spatio-temporal variability, and longer-term
evolution of precursor emissions and ozone due to, e.g., biomass burning is the
primary mission objective for TROPOMI, among others, to provide better
constraints for data assimilation and inverse modelling
e.g..
TROPOMI observes elevated levels of tropospheric ozone when and where expected
from biomass burning, with columns of more than 35 DU and up to ∼45DU during July–November across the Atlantic basin.
Figure shows median TrOC over 2-week periods in
2018 (left) and 2019 (right). Only cells with homogeneous temporal sampling and a value
above 30 DU are shown. In 2018, TrOC levels above 35 DU were
recorded from early July onward over the equatorial Atlantic, and these
increased to more than 40 DU across the southern Atlantic around mid-September until end October. The highest 2-week mean column of
48 DU was located off the Angolan coast in the second half of
September. Return to values below 35 DU occurred during late
November. In 2019, the season was less intense and started several weeks
later. TrOC values above 35 DU first appeared only around mid-September and lasted until early December, 2 weeks later than in 2018.
Maximal values of 45 DU were observed in the first 2 weeks of
October and the first half of November 2019 in the southern Atlantic. OMI and
GOME-2B TrOC data indicate similar timing and location of elevated ozone
levels (Fig. S6 in the Supplement); however, the sampling resolution of TROPOMI is
better than its predecessors, allowing for more finely resolved monitoring and
fewer missing data.
Median 15 d TROPOMI TrOC over the Atlantic basin between early July and mid-December (top to bottom) for 2018 (left) and 2019 (right). Grid cells with sparse or inhomogeneous temporal sampling or a value below 30 DU are blank. Contours indicate the 35 DU (dashed), 40 DU (solid) and 45 DU (red) isolines.
Unusually high numbers of fires were active in Brazil during August 2019 –
3 times more than in August 2018 and the highest fire count of the past
decade . However, tropospheric ozone measurements by
TROPOMI, OMI, GOME-2B and ozonesonde do not reveal a clear link with the 2019
Brazilian fires. Observed TrOC levels over South America were comparable to
or lower than 2018 levels. The exception is perhaps the first half of
November 2019 when satellite ozone columns above 40 DU occurred across
the entire southern Atlantic basin. This contrasts somewhat with a sudden
episode of low sonde readings at Natal and Ascension Island around this period
(Fig. ). More detailed analyses will be
needed to verify whether these high columns are related to the unusual fire
activity in Brazil a few months earlier.
Seasonal cycle and Madden–Julian Oscillation
The 2-year data record of TROPOMI should allow the detection of geophysical
signals with periods ranging from 2 years down to twice the averaging window
of the CCD algorithm, i.e. about a week for TROPOMI. Analyses of interannual
variability caused by the El Niño–Southern Oscillation (ENSO) or the
Quasi-Biennial Oscillation (QBO), of decadal variability caused by the solar
cycle, and of long-term trends will be
possible later on in the mission or when TROPOMI data are merged with the
European time series that started with GOME in 1995
. Focusing here on shorter timescales, we
searched for periodic signals using the Lomb–Scargle periodogram, a
Fourier-like power spectrum for irregularly sampled data and references
therein. We used the fast algorithm by
and tested significance at the 1 % level.
Long gaps in the time series reduce spectral power in the periodogram. For
CCD-derived TrOC data records, such gaps reoccur every winter in the outer
tropics when insufficient numbers of convective clouds are present in the
Pacific reference sector to obtain a reliable estimate of the stratospheric
column (Sect. and Fig. S1 in the Supplement). As a
result, periodograms at Hilo (19.7∘ N) and Suva (18.1∘ S)
show less overall power than at lower latitudes.
Figure a shows that the two most powerful spectral peaks
generally lie around 12 and 6 months. The annual and semi-annual cycles
are significant for, respectively, 88 % and 75 % of the
TROPOMI grid cells. Both are detected simultaneously over about two-thirds of
the tropics. There is no coherent picture for the presence of additional
overtones of the annual cycle. Spectral analysis of OMI and GOME-2B TrOC data
restricted to the TROPOMI time range shows significant results for similar
periods and locations (Fig. b and c).
Lomb–Scargle periodogram for 2 years of tropospheric ozone columns over nine SHADOZ sites (coloured) for TROPOMI (a), OMI (b) and GOME-2B (c). Markers locate spectral peaks that cross the 1 % significance threshold (red line). Annual and semi-annual cycles appear clearly over most of the sites.
The four significant peaks between 40 and 60 d in the TROPOMI
periodograms over Kuala Lumpur triggered further analysis into a possible
causal link with the Madden–Julian Oscillation (MJO). In the tropics, the
MJO is the dominant component of intra-seasonal variability. MJO events
consist of an eastward-moving large-scale pattern of strong deep convection
and precipitation, flanked to east and west by regions of weak deep convection
and precipitation and references therein. Such events
reoccur irregularly every 30–90 d, primarily over the warm pool of
the equatorial Indian and western Pacific oceans (where Kuala Lumpur is
situated). An active (inactive) MJO phase brings enhanced (suppressed)
convection and therefore reductions (increases) in tropospheric ozone levels
.
We consider three complementary MJO indices since conclusions related to
timing, strength and even the presence of an MJO event may differ depending on
the index used . All three indices result from an EOF
analysis of dynamical or convective proxies: (1) the NOAA Climate Prediction
Center (CPC) index based on 200 hPa velocity potential
(https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_mjo_index/mjo_index.shtml, last access: 24 November 2021); (2) the OLR MJO index (OMI*) based on outgoing
longwave radiation ; and (3) the Real-time Multivariate
MJO (RMM) index based on outgoing longwave radiation, 850 hPa zonal
wind and 200 hPa zonal wind . The latter two
indices are usually displayed as amplitude–phase diagrams, where amplitude
scales with convective activity and phase is (approximately) linked with
longitude.
Figure a shows a Hovmöller diagram of 15 d moving
mean TROPOMI TrOC anomaly averaged over the inner tropics
(5∘ S–5∘ N). Superimposed are contours of the NOAA CPC
index (arbitrary units) between -0.5 (white) and -2 (black) in steps of
0.5. Very negative CPC index values indicate strong convective activity. The
bottom panels display TROPOMI TrOC anomaly (black) and CPC index (purple) time
series at four longitudes across the equatorial Indian and Pacific oceans.
Gaps in the RMM (orange) and OMI* (yellow) time series occur when the
considered longitude is not located in their phase sector. For sake of
visibility, all MJO indices are scaled by a factor of 2, and their sign is
such that strong convective activity corresponds to more negative values.
(a) Hovmöller diagram of 15 d moving mean TROPOMI tropospheric ozone column anomaly between September 2018 and August 2019 over the inner tropics (5∘ S–5∘ N). Contours show the NOAA/CPC Madden–Julian Oscillation index, ranging from -2 (black) to -0.5 (white) in steps of 0.5. (b) Time series of TrOC anomaly (black) and CPC index (purple) at four longitudes in the equatorial Indian and western Pacific oceans. Markers represent amplitude derived from the RMM (red) and OMI (orange) indices, in the phase quadrant corresponding to the shown longitude. All MJO indices are scaled by a factor of 2, and their sign is such that strong convective activity corresponds to more negative displayed values.
Two strong events with a build-up followed by a depletion of tropospheric
ozone are noted over the Indo-Pacific warm pool (labelled no. 5 and 7 in the
Hovmöller diagram, Fig. ). TROPOMI TrOC increases and then reduces by about
5 DU in March 2019 between 100–180∘E and by more
than 8 DU in June 2019 over the Indian Ocean
(40–120∘E). In between these events, in May (no. 6), an
excursion of ∼5DU is noted over a smaller area
(80–110∘E). During each of these periods the CPC, RMM and
OMI* indices point to suppressed convective activity during the TrOC build-up
phase and strong convective activity during the TrOC depletion phase, thereby
linking these changes in TROPOMI TrOC levels to MJO. At other times, depleted
TrOC levels are noted as well, but these are of smaller magnitude and over a
smaller area. Further analysis is needed to find out whether these events are
related to MJO.
Conclusions
Since October 2017, TROPOMI aboard Sentinel-5 Precursor has been the newest member
of the European family of polar-orbiting nadir UV–visible sounders that
started with ERS-2 GOME in 1995. A suite of ozone and ozone precursor data
products are retrieved at unprecedented spatial resolution and made available
to the Copernicus atmosphere monitoring and climate change services (CAMS and
C3S), to the scientific community, and to the general public. In this paper
we assessed the quality of the first 2 years of TROPOMI tropical
tropospheric ozone columns obtained with the convective cloud differential
technique (product identifier: S5P L2_O3_TCL V01.01.05–08). This data
product is daily gridded at 0.5∘ latitude by 1∘ longitude
resolution between 20∘ S and 20∘ N and represents the
3 d moving mean tropospheric ozone column between the surface and
270 hPa in cloud-free conditions. The quality of the TROPOMI data
record was assessed using three complementary methods. We first compared
TROPOMI data to co-located ozonesonde flights and satellite data by OMI and
GOME-2B. Small-scale patterns due to sampling errors were then inferred from
detailed inspection of sequences of individual maps. And, finally, we
explored how TROPOMI perceives known geophysical signals and patterns.
All quality indicators in the comparison analysis are based on robust
statistical estimators, such that outliers do not skew our conclusions from
the short and sparse comparison time series. Since the OMI and GOME-2B
sounders have similar instrument design and spectral coverage and their
tropospheric ozone data are also derived using the CCD technique, these
satellite records are less independent from TROPOMI's CCD data record than the
measurements by ozonesonde. TROPOMI captures the spatial and temporal
variability seen by the other instruments reasonably well. Typically,
correlation with the SHADOZ ozonesonde network is strong (61 %) and
with satellite data even stronger (80 %–90 %). Correlation
coefficients drop considerably (by ∼10%–20 %) in
regions with low natural variability and smaller ozone column (e.g. western
Pacific Ocean). This does not point to a poor performance of the instrument;
rather, it is the combined result of the poor dynamical range in TrOC offered
by the atmosphere and the larger relative contribution of uncorrelated random
measurement errors.
Statistical dispersion in the TROPOMI comparisons is stable in time and ranges
between 2.6–4.6 DU when averaged over the tropical belt. The spread
in satellite intercomparisons increases by 1 DU (and more) between the
Equator and the outer tropics, in line with expectations from increased
natural variability at higher latitudes. In fact, like in many other
analyses, natural variability within the co-location window represents an
important challenge to infer random measurement uncertainty directly from the
scatter in the difference time series. We reduced its impact by using the
triple co-location technique to disentangle the measurement and
representativeness components in the random error budget. Using triplets of
TROPOMI, OMI and GOME-2B columns we obtain single-measurement TROPOMI random
uncertainties of 1.5 DU around the Equator and 2.2–2.7 DU in
the outer tropics. Uncertainties reported in the data files exhibit a similar
meridian structure, and they are only slightly larger than those inferred from
triple co-locations (but not more than 0.4 DU). The reasonable
agreement lends confidence that the reported uncertainties in the data files
are indeed realistic. When compared to other satellite sensors, TROPOMI random
uncertainties are 0.3–0.5 DU (20 %–25 %) smaller
and signal-to-noise ratios are 1–2 dB larger, while offering 6 times better spatio-temporal resolution. In addition, we noticed that the
reported uncertainty for OMI is generally too high (by 0.3–1.0 DU)
and for GOME-2B as well in the South Atlantic Anomaly (by more than
2 DU).
Two sources of error that are correlated at small spatio-temporal scales were
investigated in detail, in order to assist users desiring to exploit the
TROPOMI record at maximal sampling resolution. Both types of error originate
in the sampling pattern resulting from the interplay between TROPOMI's orbit,
cloud coverage and geophysical variability in the ozone field. The first type
is related to the sampling of deep convective cloud scenes needed to estimate
the stratospheric ozone column. It leads to a banded structure in the
latitude domain that persists over 1–2 weeks and is especially prominent in
the outer tropics. About 10 % of the measurements outside
15∘ latitude exhibit a TrOC error of at least 0.6–0.8 DU,
while in the innermost tropics such errors occur in less than
1 %–2 % of the record. The second type of error originates
in the sampling of cloud-free scenes and results in artificial correlated
patterns in tropospheric ozone close to the synoptic scale. Inspection of
animated sequences of TROPOMI maps revealed that these errors appear across
the entire tropical belt and that they are often oriented along and
progressing with the TROPOMI orbit. Although difficult to characterise, errors of
more than 5 DU are seen regularly, and these will clearly dominate the
error budget for the affected measurements.
The high sampling resolution of TROPOMI allows the aggregation of the data in time
and/or space and the reduction of random errors to negligible levels while still
preserving a resolution on par with other operational satellite sensors.
Systematic error will fairly rapidly dominate the error budget for regionally
or temporally averaged data. The median difference between TROPOMI and
co-located reference data varies depending on the instrument. When averaged
over the network or the entire tropical belt, we find that TROPOMI has a
positive bias with respect to sonde (+2.3±1.9DU) and GOME-2B
(+2.3±0.6DU), while an insignificant negative bias is seen versus
OMI (-0.1±0.7DU). Error bars represent the statistical
dispersion (1σ) of the bias estimates over the ground-based network or
tropical belt. The intercomparison with OMI furthermore suggests a meridian
pattern (1 DU larger TrOC in the northern tropics than in the south)
and a zonal pattern (1 DU larger TrOC in the Pacific compared to the
Atlantic) in TROPOMI bias. The GOME-2B results show a similar meridian
dependence although weaker, but they show no zonal structure. The sparsity of the
ozonesonde network in combination with systematic differences between stations
impedes an independent confirmation of spatial patterns in TROPOMI bias at the
1 DU level.
The causes of the TROPOMI bias and its dependence on reference instrument are
not fully understood at the moment and will be subject of further work. One
reasonable explanation for part of the differences in bias is the systematic
difference in measurement time of the instruments in the presence of a diurnal
cycle. However, not much is known about the strength and character of a
diurnal cycle in tropical tropospheric ozone. Diurnal variations were noticed
in measurements at the surface and in the boundary layer but not in the free
troposphere. If the cycle resembles the one observed over Frankfurt, then it
would contribute 0.5–1 DU to the positive bias seen in GOME-2B and
sonde comparisons. Biases in TROPOMI total ozone and cloud retrieval
potentially lead to a TrOC bias as well. Tropospheric columns derived using
the CCD technique are especially sensitive to a cloud dependence of total
ozone bias. Detailed analysis of Brewer comparisons shows such a TOC bias
dependence is less than -0.2 %, leading to at most a
-0.4DU contribution to the TrOC bias. The negative bias in cloud
height retrieval may impart an additional +0.5 DU.
No gradual drift with respect to the ozonesondes, OMI or GOME-2B was noted
during TROPOMI's first 2 years of operation. However, the record is still
fairly short, and continued monitoring will be important, also because hints of
two shorter-term temporal patterns were observed. TROPOMI–satellite biases
across the entire tropical belt were 1.5–2.5 DU higher during
March–July 2019 than during September–January in 2018 and 2019. The pattern
seems to continue in 2020 as well, with anomalies starting to increase again
in the first few months. This pattern could not be confirmed from the sparse
ozonesonde intercomparisons. In addition, the three satellite records
overestimated ozonesonde data around the Atlantic basin by 5–10 DU
during July–November 2018. A similar high bias reappeared the next year over
Paramaribo but not as clearly at the three other sites. Longer time series
will be needed to clarify whether both temporal patterns in TROPOMI bias
persist, whether these were episodic periods, or whether these can be
attributed to the reference data record or mismatch uncertainty. The latter
could be caused by the first-order approximation that satellite CCD data have
a uniform vertical averaging kernel over the tropospheric column, a hypothesis
which is under investigation by the TROPOMI Mission Performance Centre.
Besides performing comparisons to other data records we also demonstrated the
ability of TROPOMI to capture several known geophysical signals and patterns.
The permanent zonal wave-one structure is clearly present in time-averaged
tropical maps with a mean amplitude of 23.6 DU between the Atlantic
highs and the Pacific lows. The strength of this pattern in TROPOMI data
modulates seasonally, following the biomass burning season, and has a maximum
amplitude in September–November and minimal values in May–June. The 2018
and 2019 biomass burning seasons are well recorded by TROPOMI, at superior
sampling resolution than OMI and GOME-2B. Record-high fire counts in Brazil
in August 2019 do not appear to lead to record numbers in tropospheric ozone.
On the contrary, 2018 ozone levels were generally higher than in 2019 across
the Atlantic basin. Analysis of Lomb–Scargle periodograms unveiled
significant spectral peaks for the annual and semi-annual cycles in TROPOMI
data over a large part of the tropical belt. Additional peaks in the
30–60 d range were discovered over Kuala Lumpur in the Indo-Pacific
warm pool, which correspond to periodic dips of 5–10 DU in TROPOMI
time series that may be attributed to enhanced convective activity associated
with the Madden–Julian Oscillation.
Our estimates of the bias (0.1–2.3 DU or
0.3 %–13 %) and single-measurement uncertainty (<1.5–2.5 DU or ∼8%–13 %) demonstrate that
the studied TROPOMI tropospheric ozone column data meet the pre-launch mission
requirements of <25% on the systematic error and on the
precision. TROPOMI captures known patterns and variability in the
tropospheric ozone field accurately, with better precision and at higher
spatio-temporal resolution than its predecessors. It is therefore a
particularly valuable addition to the global monitoring system – one that will
allow new and more refined analyses of ozone and its precursors. With
slightly longer time series and a better view on whether the temporal features
unveiled in this study persist or dissolve, the TROPOMI record has clear
potential to contribute to the long-term tropospheric ozone data records
required by the Global Climate Observing System and by the
second Tropospheric Ozone Assessment Report of the International Global
Atmospheric Chemistry project (TOAR-II, and references
therein). On top of its data quality and horizontal
resolution, its daily coverage over the tropical belt and sampling resolution
make TROPOMI well suited to serve as the travelling standard interconnecting
regional tropospheric ozone measurements by the constellation of geostationary
air quality satellites (GEMS, TEMPO and Sentinel-4). To accomplish this
interoperability objective and to further characterise and improve its data
products, an important next step will be to investigate its mutual coherence
with satellite tropospheric ozone data inferred using different retrieval
techniques (e.g. cloud slicing and optimal estimation profiling) and also
from instruments with different spectral ranges and sensitivities.
Tropospheric column from sonde profile
The partial column of ozone TrOC, expressed in Dobson units, between the
surface and the 270 hPa level (≃ 10.5 km in the
tropical belt) is obtained by integrating the screened sonde ozone volume
mixing ratio profile Xi over i=1…N pressure levels
Pi:
TrOC=NAkBμdT0P0∫Psurface270hPaX(P)g(P)dP,A1≃NAkBμdT0P0g0∑i=1NXi-1+Xi2(Pi-1-Pi),
where NA and kB are the Avogadro and Boltzmann
constants; μd is the molar mass of dry air; and T0, P0 and g0
are the standard temperature, standard pressure and standard gravitational
acceleration. The factor in front of the summation equals
0.7891DUhPa-1ppmv-1 for Xi and Pi expressed in parts per million by volume (ppmv)
and hectopascals (hPa). A derivation can be found in Appendix B of .
The volume mixing ratio at the 270 hPa level is interpolated from the
original sonde profile data. The partial column below the first sonde
measurement is not included and assumed negligible since the first returned
reading usually occurs within 100 m from the surface. In rare cases,
the first reading is higher. If the log(pressure) range sensed by the sonde
misses 3 % or more of the surface–270 hPa range, the sonde
tropospheric ozone column is discarded.
Data availability
Sentinel-5 Precursor TROPOMI data are available from the Copernicus Open Access Hub at https://scihub.copernicus.eu. This data set is open for use by the public, subject to the data policy. Also subject to data use policies, the ozonesonde data are publicly available from the SHADOZ data archive at https://tropo.gsfc.nasa.gov/shadoz. Sonde data for the NOAA and KNMI stations were obtained directly from the data provider (Bryan J. Johnson and Marc Allaart, personal communication, 2020). The GOME-2B and OMI data were processed by BIRA-IASB and DLR in the framework of ESA's Ozone_cci project and are available upon request (DLR). Madden–Julian index time series were retrieved from https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_mjo_index/pentad.shtml, from https://psl.noaa.gov/mjo/mjoindex and from http://www.bom.gov.au/climate/mjo.
The supplement related to this article is available online at: https://doi.org/10.5194/amt-14-7405-2021-supplement.
Author contributions
DH conceived, coded and performed the analysis. TV analysed the TOC comparisons for CF dependence. JCL, AK, TV and SC contributed input and discussion at all stages of the analysis. KPH, FR and DL lead the algorithm development of TROPOMI, OMI and GOME-2B tropospheric ozone data used in this work. CL leads the algorithm development of the offline total ozone retrieval. DEK, AMT, JCW, BJJ and MA maintain and provide access to ozonesonde data archives. AMT, RMS, BJJ, PDC, AP, MA, SM, HV, HS, MM, CF and FRS supervise and/or carry out ozonesonde measurements. JPV, CZ and AD manage the Copernicus S5P mission, the S5P MPC and/or the S5PVT. DH wrote the manuscript, JCL the introduction and TV the TOC parts. All authors revised and commented on the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “TROPOMI on Sentinel-5 Precursor: first year in operation (AMT/ACP inter-journal SI)”. It is not associated with a conference.
Acknowledgements
Part of the reported work was carried out in the framework of the Copernicus Sentinel-5 Precursor Mission Performance Centre (S5P MPC), contracted by the European Space Agency and supported by the Belgian Federal Science Policy Office (BELSPO), the Royal Belgian Institute for Space Aeronomy (BIRA-IASB), the Netherlands Space Office (NSO) and the German Aerospace Centre (DLR).
Part of this work was also supported by the S5P Validation Team (S5PVT) AO project CHEOPS-5p (ID no. 28587, co-PIs Jean-Christopher Lambert, Daan Hubert and Arno Keppens, BIRA-IASB). The authors express special thanks to José Granville and Olivier Rasson for satellite- and ground-based data post-processing and for their dedication to the S5P operational validation.
This work contains modified Copernicus Sentinel-5 Precursor satellite data
(2018–2020) processed by DLR and post-processed by BIRA-IASB in the framework
of the S5P MPC. This work also contains modified GOME-2B and OMI satellite
data processed by BIRA-IASB and DLR in the framework of ESA's Ozone_cci
project (http://cci.esa.int/ozone, last access: 24 November 2021) and post-processed by BIRA-IASB. The ozonesonde data used in this publication were obtained as part of NASA's
Southern Hemisphere ADditional OZonesondes programme (SHADOZ,
https://tropo.gsfc.nasa.gov/shadoz, last access: 24 November 2021) and the Network for the Detection of
Atmospheric Composition Change (NDACC, https://ndacc.org, last access: 24 November 2021) and are publicly available. The PIs and staff at the ozonesonde stations are warmly thanked for their sustained effort on maintaining high-quality measurements and for valuable scientific discussions. NOAA/CPC/PSL and BOM are acknowledged for the provision of daily Madden–Julian Oscillation indices.
Financial support
This research has been supported by ESA/ESRIN (contract no. 4000117151/16/I-LG) and by BELSPO through the ESA PRODEX project TROVA-E2 (PEA 4000116692).
Review statement
This paper was edited by Helen Worden and reviewed by two anonymous referees.
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