AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-2753-2016The STRatospheric Estimation Algorithm from Mainz (STREAM):
estimating stratospheric NO2 from nadir-viewing satellites by weighted convolutionBeirleSteffensteffen.beirle@mpic.dehttps://orcid.org/0000-0002-7196-0901HörmannChristophJöckelPatrickhttps://orcid.org/0000-0002-8964-1394LiuSongPenning de VriesMarloeshttps://orcid.org/0000-0002-2257-1037PozzerAndreahttps://orcid.org/0000-0003-2440-6104SihlerHolgerhttps://orcid.org/0000-0001-9492-8499ValksPieterWagnerThomasMax Planck Institute for Chemistry, Mainz, GermanyDeutsches Zentrum für Luft- und Raumfahrt (DLR), Institut
für Physik der Atmosphäre, Oberpfaffenhofen, GermanyDeutsches Zentrum für Luft- und Raumfahrt (DLR), Institut
für Methodik der Fernerkundung (IMF), Oberpfaffenhofen,
GermanySteffen Beirle (steffen.beirle@mpic.de)4July2016972753277923December201518January201620May20161June2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/2753/2016/amt-9-2753-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/2753/2016/amt-9-2753-2016.pdf
The STRatospheric Estimation Algorithm from Mainz (STREAM) determines
stratospheric columns of NO2 which are needed for the retrieval of
tropospheric columns from satellite observations. It is based on the total
column measurements over clean, remote regions as well as over clouded scenes
where the tropospheric column is effectively shielded. The contribution of
individual satellite measurements to the stratospheric estimate is controlled
by various weighting factors. STREAM is a flexible and robust algorithm and
does not require input from chemical transport models. It was developed as a
verification algorithm for the upcoming satellite instrument TROPOMI, as a
complement to the operational stratospheric correction based on data
assimilation. STREAM was successfully applied to the UV/vis satellite
instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts
of previous algorithms, as it is capable of reproducing gradients of
stratospheric NO2, e.g., related to the polar vortex, and reduces
interpolation errors over continents. Based on synthetic input data, the
uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm-2, in accordance with the typical deviations between
stratospheric estimates from different algorithms compared in this study.
Introduction
Beginning with the launch of the Global Ozone Monitoring
Experiment (GOME) on the ERS-2 satellite in 1995 , several
instruments (SCIAMACHY, OMI, GOME-2; see Table for
acronyms and references) perform spectrally resolved measurements of sunlight
reflected by the Earth's surface and atmosphere. With differential absorption
spectroscopy (DOAS) , the column densities (denoted as “columns” henceforth)
of numerous important atmospheric absorbers can be determined by their
characteristic spectral “fingerprints”, amongst others
nitrogen dioxide (NO2).
UV/vis satellite instruments compared or discussed in this study
AcronymInstrumentLaunchFootprint (km2)Earth coverage per dayInstrument referenceData product used in this studyData referenceGOMEGlobal Ozone Monitoring Experiment199540 × 3201/3TEMISSCIAMACHYSCanning Imaging Absorption spectroMeter for Atmospheric CHartographY200230 × 601/6MPI-C MainzGOME-2Global Ozone Monitoring Experiment-22006a40 × 80b2/3O3M SAFOMIOzone Monitoring Instrument200413 × 26c1dNASA v003 / SP2DOMINO v2TROPOMITROPOspheric Monitoring Instrument20167 × 7c1Sentinel 420217 × 7–e
a On Metop-A. A second GOME-2 instrument was launched 2012 on Metop-B, and a third is planned to be launched on Metop-C in 2018.b Switched to 40 × 40 km2 for GOME-2/Metop-A in Metop-A and Metop-B tandem operation.c At nadir.d Reduced coverage after row anomaly in 2007; see http://projects.knmi.nl/omi/research/product/rowanomaly-background.php.e Geostationary orbit: hourly coverage over Europe.
Nitrogen oxides (NOx=NO2+ NO) play a key role in the chemistry
of both the stratosphere and the troposphere. Stratospheric NOx has
been a research topic for several decades due in particular to its role in
ozone and halogen chemistry.
Satellite measurements provide long-term global information on
spatiotemporal patterns of stratospheric NO2e.g.,. During the last decades, the analysis of
tropospheric trace gases from nadir-viewing satellite instruments moved more
and more into focus, supported by the availability of longer time series and
improved spatial resolution. Tropospheric NO2 columns derived from
satellite are nowadays widely used by the scientific community to deduce
spatial patterns, source type and strength, and trends of
NOx emissions from fossil fuel combustion, biomass burning, soil
emissions, and lightning. Overviews over the wide range of scientific
applications of satellite-based tropospheric NO2 products are given
in, e.g., or .
The retrieval of tropospheric NO2 columns from total column
measurements requires the estimation and removal of the stratospheric column,
a procedure we refer to as “stratosphere–troposphere separation” (STS) as
in .
One of the first STS algorithms is the reference sector method (RSM), which
estimates the global stratospheric NO2 fields from measurements over
the remote Pacific , based on the
assumptions of (a) longitudinal homogeneity of stratospheric NO2 and
(b) negligible tropospheric contribution over the reference region in the
Pacific. This procedure is quite simple, transparent, and robust. A further
side effect is that any systematic bias in the NO2 columns, which
might be introduced by the instrument (e.g., degradation or spectral
interference caused by the diffusor plate used for measurements of the solar
reference; ) or sub-optimal spectral analysis
, is classified as stratospheric signal and thereby
removed from the tropospheric column.
The RSM was applied by different groups to different satellite instruments
and generally performs well. However, the resulting tropospheric
NO2 columns are affected by systematic biases caused by the
following simplifying assumptions.
The tropospheric background column in the Pacific is very low (compared
to columns over regions exposed to significant NOx sources) but not
0. Neglecting the tropospheric background results in tropospheric columns
that are biased low by about some 1014 molec cm-2. Some algorithms explicitly correct for this tropospheric
background: perform a correction based on GEOS-CHEM, while
assume a constant background of 0.1 × 1015 molec cm-2. Other algorithms prefer to stick to the tropospheric “excess”
columns, which are slightly biased low but do not need any model input
.
The assumption of longitudinal homogeneity is generally reasonable, at
least in temporal means when small-scale stratospheric dynamic features
cancel out. However, large longitudinal
variations can occur in particular close to the polar vortex, as already discussed by ,
, and . Thus, tropospheric columns derived by
RSM can be off by more than 1015 molec cm-2 in winter at latitudes
from 50∘ polewards, thereby affecting scientific interpretations of
tropospheric columns over North America or northern Europe. Note that also at
low latitudes, systematic artifacts show up in tropospheric columns resulting
from RSM, in particular over the Indian ocean, which are related to
longitudinal inhomogeneities.
To overcome the artifacts caused by the assumption of longitudinal
homogeneity, several modifications of the RSM have been proposed in recent
years, while the basic approach of using nadir measurements over clean
regions for STS has been retained. We refer to this group of algorithms as
“modified RSM” (MRSM). MRSMs generally define a “pollution mask” of
regions with potentially non-negligible tropospheric columns. Measurements
over these regions are skipped within the stratospheric estimation. Thus, in
order to define stratospheric columns over the masked areas, interpolation is
required. For this purpose, and applied
“normalized convolution” , an efficient algorithm which
combines interpolation and smoothing. realized
interpolation by fitting harmonics (wave-2) over the “clean” areas.
applied a zonal boxcar filter of 30∘ width.
All of these algorithms applied a rather conservative masking approach for
potentially polluted pixels. Continents were masked out almost completely. At
northern midlatitudes, the masked area is often even larger than the area
used for the stratospheric estimation, and over the Eurasian continent the
MRSM algorithms miss any supporting measurement points over about 10 000 km. This can lead to significant errors during interpolation. In
particular the wave fitting approach can lead to large biases
.
estimated the stratospheric fields based on clouded measurements
over the ocean and subsequent interpolation. The focus on clouded
observations provides a direct stratospheric measurement, as the tropospheric
column is mostly shielded; thus, no further correction of the tropospheric
background should be needed. However, clouded pixels possibly
contain NOx produced by lightning e.g.,.
Therefore, changed the Heidelberg STS algorithm by
switching from clouded to cloud-free observations as input for the
stratospheric estimate
This aspect will be discussed in detail in
Sect. .
.
Recently, proposed an MRSM which defines “unpolluted”
pixels not with a fixed mask but according to the a priori expected
tropospheric contribution to the total column for each individual satellite
observation. This is determined from radiative transfer calculations based on
a monthly mean NO2 profile from a chemical transport model (CTM) and
the actual cloud conditions. This procedure results in additional supporting
points over continents in cases of clouds shielding the tropospheric column
and thereby largely reduces potential interpolation artifacts.
Apart from (modified) reference sector methods, there are two further
completely different approaches used for STS, which are based on (a) independent measurements or (b) CTMs.
Coincident, but independent, stratospheric measurements are available for
SCIAMACHY . It was operated in alternating nadir/limb
geometry, such that the stratospheric air masses sensed in nadir were scanned
in limb shortly before (“limb–nadir matching”, LNM). This unique
instrumental setup allowed for a direct stratospheric correction, although
systematic offsets between limb and nadir measurements still had to be
corrected empirically. STS by LNM was successfully applied for NO2 and ozone . However,
such direct coincident measurements of total columns (nadir) and
stratospheric concentration profiles (limb) are not available for other
satellite instruments, and merging measurements from different sensors always
faces the problem of spatiotemporal mismatching, requiring interpolation and
photochemical corrections compare, and thus cannot
be easily used for consistent long-term operational retrievals.
Stratospheric NO2 concentrations provided by CTMs can be used
directly for STS after empirical correction of systematic offsets between
satellite and model columns, e.g., by matching both over the Pacific
. A more sophisticated way to incorporate CTMs in STS is data assimilation
, in which modeled 3-D distributions of NO2 are
regularly updated such that the modeled stratospheric column is in close
agreement with the satellite measurement when the tropospheric contribution (as
forecasted by the CTM) is low.
In 2016, the ESA's Sentinel 5 precursor (S5p) satellite will be launched, carrying the TROPOspheric Monitoring Instrument
(TROPOMI)
. The operational (“prototype”) tropospheric column product
of NO2 from TROPOMI will be derived by a STS using data assimilation
, based on the expertise of the Koninklijk Nederlands
Meteorologisch Instituut (KNMI) as demonstrated by a 20-year record of
tropospheric columns from different satellite sensors provided by the
Tropospheric Emission Monitoring Internet Service (TEMIS, www.temis.nl;
).
Within the S5p level 2 project, for each prototype product a “verification”
product was developed in order to verify the prototype algorithms, detect
possible shortcomings, and reveal potential improvements. The TROPOMI
verification algorithm for NO2 STS, the STRatospheric Estimation
Algorithm from Mainz (STREAM), was developed at the Max Planck Institute for Chemistry (MPI-C), Mainz. It is an MRSM, requiring no further model
input, and can thus be considered as a complementary approach to data
assimilation.
STREAM does not apply a strict discrimination of “clean” vs.
“polluted” satellite pixels. Instead, weighting factors are defined for
each satellite pixel determining its impact on the stratospheric estimate
(similar to data assimilation). In particular, clouded observations are
weighted high, as they provide direct measurements of the stratospheric
field. This approach dampens the small but systematically high bias of
stratospheric columns estimated from total column measurements and the
resulting low bias of tropospheric columns.
The paper is organized in the following way. In Sect. , the STREAM algorithm is described in detail.
Section provides information on the satellite and model
data sets used in this study. Section analyses the
performance of STREAM and its sensitivity to input parameters based on both
actual satellite measurements and synthetic data. In Sect. , the STREAM results are discussed in comparison to other
STS algorithms, including the TROPOMI prototype algorithm. A general
discussion on the challenges and uncertainties of STREAM in particular, and
STS in general, is given, followed by conclusions (Sect. ). Several additional images and tables are provided in
the Supplement and referenced by a prefix “S”.
Methods
STREAM is in the tradition of MRSM algorithms that estimate the
stratospheric field directly from satellite measurements for which the
tropospheric contribution is considered to be negligible. For this purpose,
measurements over remote regions with negligible tropospheric sources, as
well as cloudy measurements, are used. In contrast to other MRSMs, however, no
strict pollution mask is applied. Instead, weighting factors are used.
Terms and abbreviations related to STREAM used in this study.
SymbolAbbrev.TermDescriptionAAMFAir-mass factorFactor relating vertical to slant column densityCDUColumn density unitUnit of column densities: 1 × 1015 molecules cm-2GCKConvolution kernelKernel used for weighted convolution; here 2-D GaussianpcldCPCloud pressurecCRFCloud radiance fractionLNMLimb–nadir matchingStratospheric correction based on coincident stratospheric measurements in limb geometry (SCIAMACHY).MRSMModified reference sector methodAn STS estimating stratospheric columns based on the total columns over “clean” regions, but allowing for longitudinal variationsPPollution proxysee Sect. S2.2RSMReference sector methodAn STS estimating stratospheric columns based on the total columns over a reference sector (here 180 to 140∘ W), assuming longitudinal homogeneitySSCDSlant column densityConcentration integrated along mean light pathSTSStratosphere–troposphere separationThe procedure of separating the total column into stratospheric and tropospheric fractionsT∗TRTropospheric residueDifference of total and stratospheric column∗, Eq. ()wWeighting factorVtropTVCDTropospheric vertical column densitySee Eq. ()VVCDVertical column densityVertically integrated concentrationV∗Total VCD∗VstratStratospheric VCDϑlatLatitudeφlongLongitude
∗ based on stratospheric AMF
STREAM consists basically of two steps:
A set of weighting factors is calculated for each satellite pixel:
a “pollution weight” that reduces the contribution of potentially polluted
pixels, a “cloud weight” that increases the contribution of cloudy observations,
and the “tropospheric residue (TR) weight” that adjusts the total weight in case of
exceptionally large or negative TRs. The product of these
weighting factors determines to what extent the associated NO2 total
columns contribute to the estimated stratospheric field (Sect. ).
Global maps of stratospheric NO2 are determined by applying weighted convolution (Sect. ).
Before describing the details of the STREAM algorithm, however, we first
define the investigated quantities and abbreviations used hereafter, as
summarized in Table .
TerminologyNO2 column densities and units
With differential optical absorption spectroscopy (DOAS; ),
so-called slant column densities (SCDs) S, i.e., concentrations integrated
along the mean light path, are derived. SCDs are converted into VCDs
(vertical column densities, i.e., vertically integrated concentrations) V
via the air-mass factor (AMF) A: V=S/A. The AMF A depends on radiative
transfer (determined by wavelength, atmospheric absorbers, viewing geometry,
surface albedo, clouds, and aerosols) and the trace gas profile. For the
stratospheric column of NO2, A is basically determined by viewing
geometry.
In this study, all column densities are given in column density units (CDU) to increase readability:
1CDU:=1×1015 molecules cm-2.
Total vertical column V∗
We define V∗ as “total” vertical column, given by
the SCD S divided by the stratospheric AMF Astrat:
V∗=S/Astrat.
The application of the stratospheric AMF basically removes the dependencies
of S on viewing angles. Over clean regions with negligible tropospheric
columns, V∗ represents the actual total VCD and can be used for the
estimation of stratospheric fields. In case of tropospheric pollution,
however, V∗ underestimates the actual total VCD, as the AMF is generally
smaller in the troposphere than in the stratosphere (see also next section).
These situations are, to the best of our knowledge, excluded from the
stratospheric estimate by the definition of appropriate weighting factors
(see Sect. ).
Stratospheric vertical column and tropospheric residue
STREAM yields an estimate for the stratospheric VCD Vstrat based on
the assumption that V∗ can be considered as proxy for Vstrat in
“clean” regions and over cloudy scenes.
In order to evaluate the performance of the stratospheric estimation, we
define the TR as the difference of total and
stratospheric VCDs (based on a stratospheric AMF):
T∗=V∗-Vstrat.
Tropospheric VCDs (TVCDs), which are the final product of
NO2 retrievals used for further tropospheric research, are connected
to T∗ via the ratio of stratospheric and tropospheric AMF:
Vtrop=T∗×AstratAtrop.
For cloud-free satellite pixels, the ratio
Astrat/Atrop typically ranges from about 1 above
clean oceans at low and midlatitudes to ≈ 2–3 above moderately
polluted regions and up to > 4 at high latitudes and over strong
NOx sources, where NO2 profiles peak close to the ground, causing
low Atrop. Figure S1 in the Supplement displays monthly mean
ratios Astrat/Atrop for cloud-free scenes based
on AMFs provided in the NASA OMNO2 product.
In this study, we focus on the tropospheric residue T∗ instead of Vtrop for several
reasons.
As only stratospheric AMFs are applied, biases in the stratospheric estimation can directly be related (factor -1) to the respective biases in
T∗.
The comparison of TRs among different algorithms instead of TVCDs isolates the effect of the different STS and excludes differences in tropospheric AMFs (which are beyond the scope of this
study).
T∗ can be determined and is of high interest for the evaluation of
STS performance also for clouded scenes with very low tropospheric AMFs.
Definition of weighting factors
(a)wpol as a function of the pollution proxy P (Eq. ),
(b)wcld as a function of the cloud radiance fraction (Eq. 6) for a cloud pressure of 500 hPa,
(c)wcld as a function of the cloud pressure (Eq. 6) for a cloud radiance fraction of 1, and
(d)wTR as a function of the tropospheric residue (Eq. ).
Version
The description given in this paper and the definition of a priori
settings refer to STREAM version v0.92.
Definition of weighting factors
MRSMs usually flag satellite pixels as either clean or
(potentially) polluted and skip the latter for the stratospheric estimation.
In STREAM, instead, weighting factors for individual satellite pixels
determine how strongly they are considered in the stratospheric estimation.
Satellite measurements which are expected to have low/high tropospheric
contribution are assigned a high/low weighting factor, respectively.
Pollution weight
In order to estimate the stratospheric NO2 field
from total column measurements, only “clean” measurements where the
tropospheric column is negligible should be considered. In cases of very
high total columns (V∗ > 10 CDU), which clearly exceed the domain of
stratospheric columns, a tropospheric contribution is obvious, and these
measurements are excluded by assigning them a weighting factor of 0.
In most cases, however, the tropospheric contribution to the total column is
not that easy to determine. We thus define a pollution weight wpol based on our a priori knowledge about the mean spatial distribution of
tropospheric NO2, reflecting potentially polluted regions. For this
purpose, we make use of the multiannual mean tropospheric NO2 column
as derived from SCIAMACHY . Based on this climatology, a
“pollution proxy” P is defined as function of latitude ϑ and
longitude φ. P indicates the regions affected by tropospheric
pollution plus a “safety margin” in order to account for possible
advection, while it is undefined for remote unpolluted regions. Details on
the definition of P are given in the Supplement (Sect. S),
and P is displayed in Fig. S2d.
Maps of the weighting factors for 1 January 2005 for OMI:
(a) pollution weight wpol,
(b) cloud weight wcld,
(c) tropospheric residue weight wTR, and
(d) product of all weighting factors (Eq. ).
The pollution weight wpol is then defined as
wpol=0.1/P(ϑ,φ)3,
where P is defined, and wpol=1 elsewhere. Hence, the higher the
pollution proxy P, the lower the weighting factor and the less the
measurement contributes to the stratospheric estimate.
Equation () is displayed in Fig. a, and the
resulting map for wpol is shown in Fig. a.
Large continental regions are assigned with a weight ≤0.1. Strongly
polluted regions like the USA, Europe, or China have weights of 0.01 down to
below 0.001. Note that the additional application of the tropospheric residue
weight (Sect. ) further decreases the weight of satellite
measurements containing high tropospheric pollution.
Cloud weight
In addition to measurements over remote regions free of tropospheric sources,
clouded satellite measurements, where the tropospheric column is
shielded, also provide a good proxy for the stratospheric column. Thus, the factor
wcld is used to increase the weight of clouded satellite pixels.
This is achieved by the following definition:
wcld:=102×wc×wpwithwc:=c4andwp:=e-12(pcld-prefςp)4.wc reflects the dependency on the cloud radiance fraction (CRF) c. Due
to the exponent of 4, only pixels with large cloud radiance fraction obtain a
high weighting factor and contribute strongly to the stratospheric
estimation.
wp describes the dependency on cloud pressure (CP) pcld. It is
defined as a modified Gaussian (with exponent 4 instead of 2, making it
flat-topped) centered at pref=500 hPa with a width
ς=150 hPa; i.e., only cloudy measurements at medium
altitudes are assigned a high weighting factor, while high clouds
(potentially contaminated by lightning NOx) as well as low clouds
(where tropospheric pollution might still be visible) are excluded.
As both wc and wp yield values in the range from 0 to 1, the factor
of 2 in the exponent of Eq. (6a) sets the maximum value of
wcld to 102, which would compensate for pollution weights down to
10-2.
The dependencies of wcld on CRF and CP, as defined in
Eq. (6), are displayed in Fig. b and c,
respectively. The spatial pattern of wcld is shown exemplarily for
OMI CP and CRF on 1 January 2005 in Fig. b. wcld reaches values up to 100 in several parts of the world, including
regions which were pre-classified as potentially polluted, thus competing
with a low wpol (Fig. a).
Tropospheric residue weight
STREAM yields global fields of stratospheric VCDs Vstrat, explained in detail below (Sect. ), which allow us to
calculate tropospheric residues T∗ according to Eq. (). While
the “true” tropospheric fields are not known, the resulting T∗ can
still be used in order to evaluate the STS performance and improve the
stratospheric estimate in a second iteration, whenever T∗ clearly
indicates an under- or overestimation of Vstrat.
A high value of T∗ likely indicates tropospheric pollution, in particular over potentially polluted regions.
The respective satellite pixels should not be used for the stratospheric estimation.
As negative columns are nonphysical, T∗ < 0 indicates that the stratospheric field has been overestimated.
This happens when the weighted convolution with neighboring pixels with high
total columns causes the estimated stratosphere to be even higher than the
local total columns. In order to avoid this effect, consequently, the
respective local total columns should be assigned a higher weighting factor
such that they contribute more strongly to the stratospheric estimate.
We thus define a further weighting factor wTR, which weights down/up
the pixels associated with a large positive/negative TR, respectively. It
turned out, however, that the stratospheric estimate is very sensitive to the
definition of wTR, and a simple definition based on the TR of
individual satellite pixels can easily result in systematic artifacts. This
results from T∗ being defined as the difference of V∗ and Vstrat (Eq. ), i.e., two quantities of the same order of magnitude
with non-negligible errors. Thus, the resulting statistical distribution of
T∗ inevitably includes negative values. These negative values caused by
statistical fluctuations must not be excluded from the probability density
function in order to keep the mean unbiased, but they should also not be used
as a
trigger for weighting up the respective measurement within the stratospheric
estimation. Thus, wTR should be only applied to significant and
systematic deviations of T∗ from 0. This is achieved by the following
settings.
In contrast to wcld, which is defined for each individual satellite measurement, wTR is defined based on the TRs averaged over 1∘× 1∘
grid pixels; i.e., first the values of T∗ within one grid pixel are averaged, reducing statistical noise, before wTR is calculated, and the resulting
weight is then assigned to all satellite measurements within the grid pixel.
wTR is only applied when the absolute value of the mean grid box T∗ exceeds a threshold of 0.5 CDU, which is typically larger than the spectral fitting error:wTR:=10-2×T∗if |T∗|>0.5 CDU1,else.
wTR is only applied for grid pixels where the adjacent grid pixels exceed the
threshold as well. By this additional condition it is guaranteed that a single outlier in the satellite
measurements cannot trigger wTR, as every
satellite measurement is assigned to exactly one
grid pixel (see Sect. 2.3).
wTR < 1, which is meant to decrease the weight of polluted pixels,
is only applied over potentially polluted regions with wpol < 1. Without
this additional condition, patterns of erroneously enhanced TR caused by stratospheric dynamics
would even be amplified by wTR.
wTR could in principle be tuned in multiple iterations. In
STREAM v0.92, only one iteration is performed, as a second iteration turned
out to have marginal effect (see Sect. S4.2.5).
The dependency of wTR on TR (grid pixel average), as defined in
Eq. , is displayed in Fig. d, and the
resulting map for wTR on 1 January 2005 is shown in
Fig. c. After the initial stratospheric estimate,
STREAM yields high values for T∗ over parts of the USA, Europe, central
Africa, and China, resulting in low wTR. Observations over these
regions are already associated with a low pollution weight. However, due to
the additional application of wTR, the net weight is lowered further
by orders of magnitude, and the respective satellite pixels will hardly
contribute to the stratospheric estimate in the next iteration, even in the case
of high wcld.
In the initial STREAM run, the resulting TR is systematically < 0 over east Canada and Greenland, caused by the asymmetric polar vortex. Over the
Labrador Sea, initial values for T∗ are systematically below the
threshold of 0.5 CDU and thus trigger a high wTR, and the respective
observations of low total VCDs contribute strongly to the stratospheric
estimate in the next iteration.
Note that, due to the threshold of 0.5 CDU (criterion 2), wTR cannot
correct small biases such as the expected low bias in TR caused by estimating
the stratospheric column from total column measurements.
Total weight
The total weight of each satellite pixel is defined as the product of the
individual weighting factors:
wtot:=wpol×wcld×wTR
(i.e., the logarithms as shown in Fig. a–c are simply
added, resulting in Fig. d). The a priori pollution
weight can still be recognized in the global pattern but is significantly
modified by wTR (further reducing the overall weight over,
e.g., the USA and China) and wcld, which competes with the
pollution weights < 1. In some regions (e.g., west of the Great
Lakes, Scotland, or the Himalayas) the cloud weight shifts the initially low
wpol to a net weight > 1.
The concept of the combination of different weighting factors is easily
extendible by further weights, e.g., based on fire or flash counts in order to
account for NOx emissions from biomass burning or lightning.
Weighted convolution
Global daily maps of the stratospheric column are derived by
applying “weighted convolution”, i.e., a spatial convolution which takes the
individual weights for each satellite pixel into account. This approach is an
extension of the “normalized convolution” presented in .
Weighted convolution at the same time smoothes and interpolates the
stratospheric field. A similar approach was used by , who applied
the fitting errors of NO2 SCDs as single weights.
The algorithm is implemented as follows.
A lat/long grid is defined with 1∘ resolution. Each satellite pixel is sorted
into the matching grid pixel according to its center coordinates. At the jth
latitudinal/ith longitudinal grid position, there are K
satellite pixels with the total columns Vijk(k=1…K) and the weights wijk. We defineCij:=∑wijk×VijkandWij:=∑wijk.In the case of measurement gaps (i.e., K=0), both Cij and Wij are set to 0.
The weighted mean VCD for each grid pixel is then given asVij=CijWijfor K > 0 and undefined for K=0 (gaps).
A convolution kernel (CK) G is defined (see below). Spatial convolution is applied to both C and W (taking the dateline into account appropriately, i.e., i=1 and i=360 are adjacent grid pixels):C‾:=G⊗C,W‾:=G⊗W.
The smoothed stratospheric VCD for each grid pixel as derived from weighted convolution is then given asV‾ij:=C‾ijW‾ij.
We illustrate this procedure for a simple 1-D example in the Supplement
(Sect. S and Fig. S3).
The degree of smoothing is determined by the definition of the CK G, which
is defined as a 2-D Gaussian in STREAM v0.92 with the
longitudinal/latitudinal variances σφ2 and
σϑ2, respectively. Generally, information on the
stratospheric column over polluted regions should be taken from clean
measurements at the same latitude. Thus, σφ has to be
sufficiently large, while σϑ has to be low as gradients in
latitudinal dimension should be mostly conserved. For high latitudes,
however, the longitudinal extent of the CK has to be small enough as well in
order to be able to resolve the strong gradients caused by the polar vortex.
In order to meet these requirements, we implement the convolution in the following way:
Two CKs are defined in order to meet the different requirements for polar vs. equatorial regions (see Fig. S4):Gpol:=G(σφ=10∘,σϑ=5∘)Geq:=G(σφ=50∘,σϑ=10∘).Note that the difference of the CKs, which are defined on a regular degree grid, is even more drastic in kilometer space.
Stratospheric VCDs Vstrateq and Vstratpol are derived for both CK according to Eqs. ()–().
The final stratospheric VCD is defined as the weighted mean of both, depending on latitude ϑ:Vstrat:=cos2(ϑ)Vstrateq+sin2(ϑ)Vstratpol.
By this method, spatial smoothing is wide enough at the equator (needed to
interpolate, e.g., the stratosphere over central Africa) but small enough at
the polar vortex.
In latitudinal direction, this procedure can cause small, but systematic,
biases when stratospheric NO2 shows significant latitudinal gradients
on scales of σlat or smaller. To overcome this, STREAM provides
the (default) option to run the weighted convolution on
“latitude-corrected” VCDs; i.e., the mean dependency of V∗ on latitude
is (1) determined (again over the Pacific), (2) subtracted from all individual
Vijk, and (3) added again to the stratospheric estimate from weighted
convolution. By this procedure, latitudinal gradients are largely removed for
the convolution (but not from the final stratospheric fields), and the
systematic biases vanish (as shown in Sect. S2.3).
Data processing
STREAM estimates stratospheric fields and tropospheric residues for
individual orbits, using NO2 measurements of the dayside of the
orbit. Note that the effect of changes of local time on stratospheric
NO2 across orbit is generally low (see Sect. S2.4) and is thus neglected within STREAM. For each orbit under
investigation, the orbit itself plus the seven previous and subsequent orbits
(corresponding to about ±12 h in time, or ±180∘ in space
(longitude), for the investigated satellite instruments in polar
sun-synchronous orbits) are used for the calculation of V∗, weighting
factors, and thus Vstrat via weighted convolution. For the daily
means presented in this study, all orbits where the orbit start date matches
the day of interest are averaged.
Alternatively, STREAM can be run in “near-real time” (NRT) mode, in which
the 14 past, but no future, orbits are included in the weighted convolution.
We discuss the performance of STREAM NRT for the example of GOME-2 in Sect. .
STREAM v0.92, implemented as a MATLAB script at MPI-C, requires about 10 s for processing one orbit of OMI data on a normal desktop
computer
(3.4 GHz). Time-consuming steps are, at about equal parts, the sorting of the
satellite pixels on the global grid Vijk and
the convolution process, while the time needed for the calculation of
weighting factors is negligible.
Data setsSatellite data sets
Several UV/vis satellite instruments provide column measurements of
atmospheric NO2. Table summarizes the
characteristics of the instruments and provides references to the data
products used in this study, from which the total NO2 SCD, the
stratospheric AMF, and the cloud fraction/cloud top height are taken as input
for STREAM. Below we provide details on the satellite characteristics and the
data sets used in this study, starting with OMI (as STREAM was optimized for
OMI within TROPOMI verification) and GOME-2, followed by older instruments
with particular challenges such as poor spatial coverage (SCIAMACHY) or
resolution (GOME).
OMI
In this study we mainly focus on OMI for two reasons.
OMI provides daily coverage with small ground pixels. While this already results in a high number
of available satellite pixels per day (> 106), the number of clouded pixels
matching the requirements to cause a high wcld is also high (more than 105 pixels have a wcld > 5).
STREAM is the STS verification algorithm for TROPOMI. Algorithm testing within TROPOMI
verification and comparisons to the TROPOMI prototype algorithm are performed based on actual OMI measurements.
STREAM basically requires V∗ (=S/Astrat) as input. For OMI,
we use the level 2 “OMNO2” data product (version 3) provided by NASA
and labeled as “Standard Product 2” (SP2) therein,
which provides de-striped NO2 SCDs and stratospheric AMFs
In
the DOMINO v2 product, total SCDs are not de-striped, and stratospheric AMFs
are only provided up to a solar zenith angle (SZA) of 80∘
. In addition, quality proxies are
used to exclude dubious measurements (like those affected by the “row
anomaly”
).
Also information on CRF and CP,
which is needed for the calculation of wcld, is provided by the
OMNO2 v003 hdf files, based on the “improved OMI O2–O2 cloud
algorithm” OMCLDO2.
The NASA v003 product involves a STS algorithm based on an MRSM as well. The
resulting tropospheric residues of STREAM and NASA v003 are compared and
discussed in detail in Sect. .
In addition to the NASA product, we also extract the DOMINO (version 2) level 2 data as provided by TEMIS, for two
purposes.
The TROPOMI “prototype algorithm” is developed by
KNMI based on model assimilation similar to the DOMINO v2 algorithm.
Due to the high computational effort of data assimilation, no dedicated TROPOMI
verification data set is available for verification. Instead, we compare the results
of STREAM directly to DOMINO v2 (Sect. ).
DOMINO provides TM4 model profiles of NO2 (needed for the
calculation of DOMINO tropospheric AMFs). Here, we use the TM4 data in order
to construct synthetic total columns of NO2 for performance tests of
STREAM (see Sect. ).
Both OMI products are based on the same spectroscopic analysis; i.e., both
start with the same NO2 SCD. Note that this SCD is biased high by
about 1 CDU due to shortcomings in the spectral retrieval (see
and references therein). Recent algorithm refinements have removed this bias
, but updated NASA or TEMIS products are not
available yet. However, such an overall bias will be interpreted as
stratospheric feature by STREAM and thus does not affect its performance (the
same holds for the operational NASA and TEMIS STS algorithms). Still, the
resulting TRs are expected to decrease slightly as the bias decreases for
larger SCDs Fig. 3 therein.
GOME-2
The GOME-2 instruments on the Metop-A and B satellites provide a time series
of almost 10 years with the perspective of continuation until 2025 due to the
upcoming instrument on Metop-C. GOME-2 provides a good spatial coverage with
moderate satellite ground pixel size.
We applied STREAM to total NO2 columns from the operational product
(GDP 4.7), as provided by DLR in the framework of the Ozone Satellite
Application Facilities (O3M SAF), for Metop-A.
The operational product uses an MRSM for STS as
well. We compare the results of STREAM and the GDP 4.7 algorithm in Sect. .
SCIAMACHY
STREAM was applied to the SCIAMACHY VCDs retrieved at MPIC Mainz
. While OMI provides daily global coverage, the
coverage of SCIAMACHY is rather poor (only about one-sixth of the Earth per day),
and ground pixels are larger than for OMI (except for swath edges).
Consequently, also the number of total (about 60 000) and cloudy (about
4000) pixels per day is much lower than for OMI. Thus, SCIAMACHY can be
considered as extreme test case for the performance of STREAM.
One reason for the poor spatial coverage of SCIAMACHY is the measurement mode
alternating between nadir and limb geometry. This, however, provides the
unique SCIAMACHY feature of a direct measurement of the stratospheric column.
We thus compare the TR resulting from STREAM to the MPI-C SCIAMACHY product
based on LNM , using the MPI-C retrieval scheme for
NO2 concentration profiles from limb measurements
(Sect. ).
GOME
GOME was the first nadir-viewing spectrometer in the UV/vis spectral range
with a spectral resolution enabling DOAS analyses. Due to large ground pixel
size (320 km across track), only a low number of (total as well as clouded)
satellite pixels per day is available. We nevertheless included GOME in this
analysis in order to investigate to what extent STREAM can be applied within
homogenized retrievals for multiple satellite instruments, as planned within
the QA4ECV (Quality Assurance for Essential Climate Variables)
project
http://www.qa4ecv.eu/
. We apply STREAM to the VCDs
provided by TEMIS and compare the resulting TRs to a
simple RSM
(Sect. ).
Model data
For comparisons, and for the calculation of synthetic
total columns for performance tests of STREAM, we make use of stratospheric
NO2 as provided by the ECHAM5/MESSy Atmospheric Chemistry (EMAC)
model, which is a modular global climate and chemistry simulation system
.
We use the results from simulation RC1SD-base-10a of the ESCiMo
(Earth System Chemistry integrated Modelling)
project as detailed by .
Here, only basic information on this specific simulation is summarized.
The model results were obtained with ECHAM5 version 5.3.02
and MESSy version 2.51 at T42L90MA resolution, i.e.,
with a spherical truncation of T42, corresponding to a quadratic Gaussian grid
of approx. 2.8∘ by 2.8∘ in latitude and longitude,
and 90 vertical hybrid pressure levels up to 0.01 hPa.
The dynamics of the general circulation model was nudged
by Newtonian relaxation towards ERA-Interim reanalysis data .
Simulation RC1SD-base-10a was selected from among the various ESCiMo
simulations for several reasons:
it has been nudged to reproduce the “observed” synoptic
situations;
its stratospheric resolution is, with ≃65 levels, finer
compared to other simulations from the ESCiMo project;
the simulated total column and tropospheric partial column ozone compare
well with observations ; and
the precursor emissions from the land transport sector are most realistic
in comparison to other simulations.
In conclusion, this simulation represents the state-of-the-art in terms
of numerical simulation of the atmospheric chemistry.
Moreover, the applied nudging technique allows a direct comparison with
observational data, since the simulated meteorological situation corresponds
to the observed.
Specifically for this study, the submodel SORBIT was used
to extract NO2 mixing ratios along the sun-synchronous orbit of the
Aura satellite, thus matching the local time of OMI observations.
Stratospheric VCDs were calculated by vertical integration of the modeled
NO2 mixing ratios between the tropopause height (as diagnosed
according to the WMO definition based on lapse rate equatorwards of
30∘ north/south and as iso-surface of 3.5 PVU potential vorticity
poleward of 30∘ latitude) and the top of the atmosphere.
In this study, we make use of the modeled stratospheric
columns for two purposes.
We perform a simple model-based STS for comparison.
To remove systematic biases between satellite measurements and EMAC, a latitude
dependent offset is determined in the Pacific and corrected for globally, similar as
in and . We refer to this EMAC-based STS as
STSEMAC and applied it to OMI data (Sect. ).
Stratospheric VCDs from EMAC are used to construct a synthetic data set
of total NO2 VCDs for performance tests of STREAM (see next section).
Total OMI VCD V∗ (top) and the resulting stratospheric
estimate Vstrat from RSM (second row) and STREAM (third row) for
1 January (left) and 1 July (right) 2005. Resulting Vstrat from other algorithms
are included as well for comparison (see Sect. ).
Synthetic VCD
We test the performance of STREAM on synthetic VCDs,
which allows a quantitative comparison of the estimated TR to the a priori
“truth”. The input to STREAM, i.e., synthetic total columns of NO2,
should realistically represent (a) stratospheric chemistry and dynamics, (b) tropospheric emissions, transport, and chemistry, (c) cloud fields, and
(d) the satellite sampling.
For these purposes, we construct synthetic NO2 column densities V∗ based
on
stratospheric VCDs from EMAC
Stratospheric columns are taken from EMAC rather than TM4, as the latter does not represent a free model run of stratospheric chemistry and dynamics but uses the satellite measurements for assimilation.
at AURA overpass time (Sect. ),
modeled tropospheric VCDs from TM4 (Sect. ), and
measured cloud properties and the respective tropospheric AMFs from OMI as provided in the DOMINO NO2 product.
Synthetic TRs are given as T∗=Vtrop×Atrop/Astrat (compare Eq. ). Synthetic total columns V∗ are then
calculated as Vstrat+T∗ (Eq. ) and fed into STREAM.
The resulting fields of stratospheric VCDs and the respective TRs can then be
compared to the a priori “truth”. Synthetic Vstrat, TVCD, and
T∗ are displayed in Fig. S7 for 2 selected days.
Algorithm performance
In this section we analyze the performance of STREAM. As the true
stratospheric VCD is not known, the error of any STS algorithm is not easily
accessible. Still, the STS performance can be evaluated based on the
properties of the resulting TR: in remote regions without substantial
NOx emissions, T∗ should generally be low but still positive
(about 0.1 CDU; ). Also the variability of T∗ over both
space and time should be low in regions free of tropospheric sources.
Below, we investigate the characteristics of T∗ from STREAM
(Sect. ) and its dependency on a priori settings
(Sect. ) for OMI measurements. In
addition, the error of T∗ is quantified based on synthetic data
(Sect. ). Application of STREAM to other satellite
instruments and the comparisons between STREAM and other STS algorithms are
provided in Sect. .
OMI tropospheric residues T∗ based on RSM for January (left)
and July (right) 2005 for the first day of the month (top) and the monthly
mean (bottom).
OMI tropospheric residues T∗ based on STREAM for January
(left) and July (right) 2005 for the first day of the month (top) and the
monthly mean (bottom).
Performance of STREAM for OMI compared to RSM
Figure displays the OMI daily mean VCD
V∗ (top) as well as the respective stratospheric field from RSM
(second row) and STREAM (third row) for 1 January (left) and 1 July 2005 (right), respectively. The overall latitudinal as well as
longitudinal dependencies are clearly reflected in the stratospheric fields,
while small-scale stratospheric features are lost by the spatial convolution. Figures
and display the resulting TRs, respectively, for both daily (top) and monthly (bottom) means. Figure summarizes the daily and monthly statistical
properties of TR, i.e., the median as well as 10th/90th and 25th/75th percentiles (light/dark bars) for different regions (see Fig. S8
for an illustrative sketch of the meaning of the percentile bars, as well as
the definition of regions).
Regional statistics of OMI tropospheric residues T∗ from
RSM and STREAM for January (top) and July (bottom) 2005.
Light and dark bars reflect the 10–90th and 25–75th percentiles, respectively. The median is indicated in white.
Narrow bars show the statistics for the first day of the month, wide bars those of the monthly means (see also
Fig. S8 left for illustration).
The regions are defined in Fig. S8 right.
“High latitudes” refer to the respective hemispheric winter only.
Overall, spatial patterns of TR are similar for RSM and STREAM, in particular
the enhanced values reflecting tropospheric pollution over, e.g., the USA,
central Africa, or China. However, RSM reveals several artifacts of both
enhanced as well as systematically negative TR as a consequence of the simple
assumption of zonal invariability of stratospheric NO2. For instance,
on 1 January 2005, VCDs over northern Canada are lowered due to the polar
vortex (Fig. top left). Consequently, the simple
RSM results in negative TRSM∗ down to -0.7 CDU (Fig. ). In contrast, TRSM∗
over northeastern Russia is quite high (> 0.5 CDU). This pattern is
slightly reduced but still present in the monthly mean (see the statistics
of TRSM∗ for high latitudes in Fig. ).
This artifact is largely reduced by STREAM (Fig.
top left). The spread of T∗ at high latitudes is more than 3 times lower
than that of TRSM∗ (Fig. ). Also for July,
systematic structures showing up in TRSM∗ (in polar regions, but
also in the Indian ocean at 30–60∘ S) are largely reduced in
STREAM.
Over the Pacific, TRSM∗ is, by construction, 0 on average.
T∗ is systematically higher by about 0.1 CDU (Fig. ). This results from the emphasis of clouded
pixels used for STREAM, which directly reflect the stratospheric rather than
the total VCD. This additional advantage of STREAM over RSM is further
discussed below (Sect. ).
As both RSM and STREAM generally assume stratospheric patterns of
NO2 to be smooth, i.e., do not resolve longitudinal variations at all
(RSM) or on scales <σφ (STREAM), the small-scale variations
in daily total VCDs (Fig. top) are transferred to the
TR, resulting in “patchy” daily TRs ranging from about -0.1 up to
+0.4 CDU in remote regions (10th–90th percentiles). In the monthly means,
however, these patchy structures have mostly vanished (both for RSM and
STREAM), as the spatial patterns of different days at variable locations
cancel each other out. The remaining systematic patterns in monthly mean
T∗ have generally larger spatial scales and are within 0 up to
+0.25 CDU in remote regions.
On 1 July, a band of enhanced V∗ shows up around 20–30∘ S,
where (a) V∗ is higher in the Indian Ocean compared to the Pacific and
(b) the structure of enhanced V∗ is “tilted” in the Pacific (see
Fig. top right); i.e., the RSM assumption of zonal
invariance is not fulfilled. Consequently, the RSM results in extended
horizontal structures (“stripes”) of low/high-biased T∗ over South
America and the Indian Ocean, respectively, ranging from -0.5 up to almost
1.0 CDU (Fig. top right). Again, temporal
averaging reduces the amplitude, but systematic patterns of about ±0.4 CDU remain in the monthly mean (Fig. bottom
right). As STREAM also assumes a weak variation of Vstrat with
longitude, in particular at low latitudes, the artifacts in T∗ are very
similar to those of TRSM∗ at 20–30∘ S. Note that this
artifact is particularly strong in July 2005 (as compared to 2010; see
Sect. ).
In Sect. , TRs from STREAM are investigated for other
satellite instruments and compared to other STS algorithms, and the
advantages and limitations of STREAM are discussed further.
Impact of a priori settings
STREAM determines the stratospheric NO2 VCD Vstrat based on
weighting factors as described in Sect. . The resulting TRs
thus depend on the weighting factor definition and convolution settings. We
performed runs of STREAM with one-by-one modifications of each parameter and
compared the results to the baseline setting. Overall, the effects of
a priori settings on T∗ have been found to be rather small (of the order
of 0.1 CDU), and the STREAM results are thus robust with respect to the
parameters chosen in v0.92.
Below, we summarize the main findings of the performed sensitivity studies.
Figures and details are provided in the Supplement.
Impact of cloud weight
The cloud weight wcld was varied (a) by setting it to 1 (i.e., not accounting for cloud properties at all),
(b) increasing wcld by a factor of 10 for clouded pixels,
(c) including high-altitude clouds in the calculation of wcld,
and
(d) including low-altitude clouds in the calculation of wcld.
When no wcld is applied, the tropospheric estimate over the
Pacific is ≈ 0, as for the classical RSM, instead of about
0.1 CDU for the baseline. This difference corresponds to the order of the
tropospheric background of NO2. Over potentially polluted regions,
however, the difference to the baseline is larger (0.2 CDU). Here, the
stratospheric estimate is additionally biased high due to missing supporting
points over continents.
The “high wcld” scenario is achieved by modifying
Eq. (6a) from wcld:=102×wc×wp to wcld:=103×wc×wp; i.e., wcld is increased by a factor of 10 for
cloudy pixels of mid-altitude but stays unchanged for cloud-free pixels. In
this scenario, measurements over clouds by far dominate the stratospheric
estimate, yielding lower Vstrat, and thus higher T∗,
compared to the baseline. However, the difference is very small
(< 0.05 CDU). In addition, the variability of T∗ is
generally slightly higher in case of a 10 fold increased wcld.
When high-altitude clouds are included in the calculation of wcld,
the resulting TR hardly changes at all, indicating that the impact of
lightning NOx on NO2 satellite observations is generally
small.
The inclusion of low-altitude clouds has almost no effect as well, as
expected over clean regions. Over potentially polluted regions, however, it is
expected that low-altitude clouds result in increased total columns V∗
as soon as there is significant NO2 above or within the cloud,
causing high tropospheric AMFs. Consequently, Vstrat is expected to
be biased high, and T∗ biased low over potentially polluted regions,
when
low clouds are included in the calculation of wcld. This effect was
indeed found, but the absolute change is rather small (< 0.1 CDU in winter,
almost 0 in summer). This weak dependency on the inclusion of low-altitude
clouds probably results from the conservative definition of wpol, which is already very low over regularly polluted regions.
Following the argument that cloudy observations provide a direct measurement
of the stratospheric column, a higher cloud weight would be expected to be
more favorable and to result in higher tropospheric background over the
Pacific. This is indeed observed for OMI. For other satellite instruments,
however, results are somewhat contradictory (see Sects. , , and ). Thus,
the definition of wcld in Eq. (6) is kept as a compromise in
order to have common algorithm settings across different satellite platforms.
Impact of convolution
In STREAM, two different CK are applied, yielding two stratospheric
estimates, and the final Vstrat is calculated as weighted mean of
both (see Sect. 2.3 and Eqs. 15 and 16). We tested the impact of the
choice for CK by applying both the polar (“narrow”) and equatorial
(“wide”) CK globally.
The narrow CK, and thus the potential range of influence of satellite
pixels with high weights, is limited to about 2×σφ=20∘ in longitude. This potentially leads
to biases over continents caused by spatial interpolation. Thus, the resulting T∗ is
(too) low over central Africa. Overall, median T∗ over potentially polluted regions
is lower compared to the baseline settings by about 0.1 CDU.
For wide CK, however, the longitudinal gradients at high latitudes
are not resolved anymore. Consequently, the spatial variability of daily
T∗ at high latitudes is increased by a factor of 2. We conclude that our
choice of the combined CK for high and low latitudes is a good compromise for
realizing weighted convolution.
Impact of latitude correction
When the initial correction of the latitudinal dependency of V∗ over the
Pacific is omitted, the resulting TR reveals global stripes with negative
values around the equator and maxima (≈ 0.5 CDU) at about
30∘ N/S, both in winter and summer.
Impact of the number of considered orbits
In STREAM baseline settings, for each orbit, stratospheric estimation is
based on the previous and subsequent seven orbits, corresponding to full global
coverage for OMI. Switching this parameter to either ±14 or ±3 orbits
has almost no impact on the resulting TR.
In case of NRT application of STREAM, no subsequent orbits are available, and
the previous 14 orbits have to be considered. This setup also results in
essentially the same T∗ statistics (compare Sect. ).
Impact of tropospheric residue weight
In STREAM v0.92, one iteration for wTR is applied. When wTR is omitted, the spread of T∗ slightly increases for high latitudes.
A second iteration does not yield a further improvement. Lowering the
threshold in Eq. () from 0.5 to 0.3 CDU results in a slightly lower
spread of T∗ at high latitudes in summer.
Impact of pollution weight
The impact of pollution weight is investigated by multiplying wpol
(where different from 1, compare Fig. 2a) by 0.1 (“low wpol”) or
10 (“high wpol”). In the first case, the resulting pollution
weight over most continents is below 0.01, while in the second case it is
increased to 1 (meaning that wpol is switched off) except for
industrialized pollution hotspots.
In remote regions, the change of pollution weight has almost no impact. In
potentially polluted regions, the impact is only moderate as well. Low
wpol does not differ much from the baseline, as the latter already
assigns rather low weighting factors to potentially polluted pixels; a
further decrease by factor 0.1 thus does not change much.
Only for high wpol can a significant change of TR be seen; in this
case, the inclusion of more partly polluted observations causes a high bias
in the stratospheric estimate and the resulting TRs are biased low by almost
0.1 CDU in winter.
Performance for synthetic data
In order to estimate the uncertainties of the STREAM stratospheric estimate
(and thus tropospheric residues), we apply the algorithm to synthetic input
data, as defined in Sect. , for which the “true”
stratospheric fields and TR are known. Again, a simple RSM is applied as well
for comparison.
Figure displays the statistics of the error of
T∗, i.e., the difference ΔT∗ of estimated and a priori TR, which
equals the difference between the true and the estimated stratospheric VCD,
for different regions. The spatial patterns of ΔT∗ are shown in the
Supplement (Fig. S20).
Over the Pacific, RSM results in TR biased low by 0.1 CDU. With STREAM, the
bias is reduced (0.05 CDU) but not completely removed. On 1 January 2005,
ΔT∗ shows a variability of almost 0.4 CDU (10th to
90th percentile) for both algorithms. This is mainly caused by the
small-scale structures of stratospheric NO2 in EMAC over the Pacific,
in particular at southern latitudes (see Fig. S7), which are resolved by
neither STREAM nor RSM. The respective spatial variability of the monthly
mean, however, is much lower (about 0.1 CDU).
Regional statistics of the error of T∗ from STREAM, i.e., the
difference of estimated and a priori TR (based on synthetic total columns as
defined in Sect. ).
Again, the simple RSM results in large biases and high variability of
ΔT∗ at high latitudes, which are largely reduced by STREAM.
Overall, the agreement of a priori and estimated T∗ from STREAM is very
good, in particular for monthly means. Remaining systematic biases are about
-0.1 CDU over potentially polluted regions; i.e., resulting TRs are slightly
underestimated, as expected due to the general approach of using total column
measurements as proxy for the stratospheric estimation.
The application of STREAM to synthetic data thus provides a valuable estimate
of the algorithm's accuracy. One might think that using the synthetic data for
optimizing the definition of weighting factors is the next step forward.
However, we refrain from doing so due to some contradictory results for
different instruments. Concretely, the remaining bias in TR for synthetic
data of about 0.1 CDU could be further reduced by increasing wcld.
This, however, has adverse effects on SCIAMACHY and GOME results (see
Sects. 5.3 and 5.4).
Comparison to other algorithms and discussion
In this section, we apply STREAM to different satellite instruments, compare
the results to various existing STS algorithms, and discuss the challenges,
limitations, and uncertainties of STS in general and STREAM in detail.
OMI
As shown in the previous section, STREAM as applied to
OMI data generally shows a good performance
(Figs. and ). The
systematic artifacts of a simple RSM, such as the large variability of
T∗ at high latitudes, are largely removed by STREAM. In addition, the
application of wcld emphasizes cloudy observations which directly
reflect the stratospheric column. Mean T∗ over the Pacific is thus not 0
anymore as in RSM, and an additional correction for the tropospheric
background is not required in STREAM.
The sensitivity of STREAM on a priori parameters has been found to be small.
Remaining monthly mean TRs in clean regions and their variability are of the
order of 0.1 CDU.
Below, we compare the OMI results for 2005 to other algorithms, i.e., the
operational DOMINO (Sect. ) and NASA
(Sect. ) data products as well as a simple model-based
correction using EMAC (Sect. ). Figure summarizes the statistics of regional T∗ from the
different algorithms. Note that only coincident measurements where all four data
products exist are included in Fig. in order to allow
for a meaningful comparison; in particular, high latitudes in hemispheric
winter are skipped, as DOMINO data are not provided for SZA > 80∘. Thus,
the statistics for STREAM slightly differ from those shown in
Fig. .
Regional statistics of OMI tropospheric residues T∗ from
different STS algorithms for January (top) and July (bottom)
2005.
Note that the values for STREAM slightly differ from Fig. , as here only coincident satellite pixels of STREAM, DOMINO, and NASA are included.
Monthly mean difference of tropospheric residues T∗ from
DOMINO and STREAM for OMI measurements in January (top) and July (bottom)
2005.
Comparison to DOMINO
STREAM is part of the TROPOMI verification activities. The operational
TROPOMI (“prototype”) algorithm for STS of NO2
was developed by KNMI, based on the DOMINO data processor for OMI (Boersma et al., 2007, 2011)
. The STS therein is done by assimilating the satellite
measurements in the CTM TM4 .
For TROPOMI verification, we compare STREAM results for OMI to the respective
DOMINO product as shown in Fig. S21. On a daily basis, “patchy” patterns of
enhanced as well as negative TR show up over remote regions (Fig. S21), which
result from the dynamical features already present in total VCDs
(Fig. ) combined with the respective dynamics
prognosed by the model; spatial mismatch of these patterns can easily cause
biases of the estimated TRs in both directions. Interestingly, some patterns
look even reversed as compared to STREAM (Fig. ), for instance southeast from South
Africa (around 50∘ S, 50∘ E). In the monthly means, these patches
again are mostly canceled out.
Mean regional TRs (Fig. ) are very similar between
STREAM and DOMINO. However, the variability of T∗ is slightly higher for
DOMINO, in particular at high latitudes, as well as in the Pacific and in
remote regions in July.
Figure displays the differences of the monthly mean
TR between STS_EMAC and STREAM for January and July 2005. Overall, the differences are quite small (below
±0.1 CDU for 65 % of the world between 60∘ S and 60∘ N). Nonetheless, the monthly means reveal systematic regional deviations of more than
±0.3 CDU (for less than 3 % of the world).
In January, TRs over East Asia at high latitudes are systematically higher
for STREAM. This is probably related to an underestimation by DOMINO, as the
DOMINO TRs are very low and partly negative in this region. Over North
America, TRs from STREAM are higher than from DOMINO at the east coast but
vice versa over western Canada. In both cases, the lower TR is slightly
negative, indicating an overestimation of Vstrat from DOMINO/STREAM
at the east/west coast, respectively.
In July, the TR reveals a “stripe”-like structure at about 30∘ S, as already discussed
in Sect. . In DOMINO, similar bands of enhanced
tropospheric residue are found around 30∘ S, in particular in the
Indian ocean. As the amplitude and width of these bands is different for
STREAM and DOMINO, this feature is most striking in the difference map; TRs
around 30∘ S are generally higher for DOMINO.
DOMINO reveals some patches of systematically enhanced TRs that are not
observed by STREAM and thus show up in the difference map as well (west of
the USA, west of the Sahara, Himalaya). Reasons for these regionally enhanced
TRs (and thus low-biased stratosphere) have to be investigated in future
studies.
Comparison to NASA
The official OMI NO2 product provided by NASA uses an MRSM for STS as
well, as described in . Daily and monthly maps of TR from
NASA (OMNO2 v003/SP2) are shown in Fig. S22.
The NASA STS corrects for the tropospheric background based on a “fixed
model estimate” . Consequently, TRs are about 0.1 to 0.3 CDU over clean regions throughout the world.
TRs from NASA are impressively smooth even on a daily basis. This results from
the STS algorithm which, over clean regions, interprets the difference
between the total column and the (small) modeled tropospheric column as
stratospheric column whenever the quotient of the modeled tropospheric
slant column and stratospheric AMF (matching our definition of T∗) goes
below a threshold of 0.3 CDU. Thus, at southern high latitudes in July
(completely classified as unpolluted in the NASA algorithm), the TR is almost 0 ± 0,
i.e., shows no variability at all (compare Fig. ) just
by construction, as all the variability present in the total column was
assigned to the stratospheric column (compare
Fig. ).
While this is probably a reasonable procedure over completely clean regions,
we would like to point out the following.
The smoothness of NASA TR over oceans is not surprising, as it is
reached by construction. In particular, the smooth patterns of
TR over oceans allow no conclusion on the NASA STS performance
over polluted continental regions, where TRs are based on interpolated stratospheric fields, just as in STREAM.
The NASA procedure of assigning the total column variability in
clean regions completely to the stratospheric estimate also removes
any cloud dependency of the TR, which affects applications such as profile
retrievals by cloud slicing e.g.,.
The NASA procedure runs the risk of labeling episodical NO2 transport
events over oceans (Zien et al., 2014) as stratospheric pattern. perform an
automatic “hotspot” identification and elimination scheme to avoid this.
Nonetheless, on 1 January, a NO2 transport event can be seen
in the total VCD east of Canada (Fig. top left)
which is similar to the “meteorological bomb” described in .
This event is clearly visible in T∗ from STREAM (Fig. top left) but only weakly in T∗ from NASA
(Fig. S22 top left). The reason for this discrepancy is that the local
enhancement of NO2 is partly classified as a stratospheric feature in the NASA product, as illustrated in
Fig. S23 (left).
Figure displays the differences of the monthly mean
TR for January and July 2005. Again, overall agreement is very good: in
January, both products agree within 0.1 CDU for 69 % of the Earth and within
0.3 CDU anywhere. In July, agreement within 0.1/0.3 CDU is found for
64 %/94 % of the Earth (for latitudes below 60∘), respectively. Again,
the band at 30∘ S sticks out in the difference map as discussed above.
Highest deviations of up to 0.5 CDU, however, are observed over the Sahara.
Within the NASA STS, the Sahara is masked out completely, as the high albedo
and low cloud fractions result in high tropospheric AMFs, such that even low
tropospheric VCDs could contribute significantly to the total column. In
STREAM, however, large parts of the Sahara are treated as unpolluted and are
assigned with w=1. A close check of the stratospheric estimates from
STREAM and NASA over the Sahara reveals that the large deviation probably
results from both a high-biased Vstrat by STREAM and a low-biased
Vstrat by NASA (see Fig. S23 right).
Comparison to STSEMAC
We have used the stratospheric 3-D mixing ratios provided by EMAC in order to
perform a simple model-based STS, similar to . First, the
latitude-dependent offset between EMAC and OMI VCDs is estimated over the
Pacific (when a multiplicative adjustment is performed, results hardly change).
Second, the offset-corrected stratospheric NO2 VCDs is used for
global STS. No additional correction for the tropospheric background is
performed, such that the mean TR over the Pacific is 0 by construction.
Monthly mean difference of tropospheric residues T∗ from
NASA and STREAM for OMI measurements in January (top) and July (bottom) 2005.
Daily and monthly maps of TR from STSEMAC are shown in Fig. S24.
Daily maps reveal patches of TR from -0.3 CDU up to 0.4 CDU resulting from
mismatches in actual and modeled stratospheric dynamics. In the monthly
mean, these fluctuations largely cancel out. Overall, variability
(10th–90th percentiles) of T∗ in remote regions was found to be
about 0.3–0.4, similar to that for DOMINO.
Figure displays the differences of the monthly mean TR
for January and July 2005. The overall negative values over ocean are a
result of the neglect of the tropospheric background in STSEMAC.
Besides this, the most striking features are
positive deviations (i.e., TR from STSEMAC being higher than from STREAM) over North America and Eurasia in January (up to 0.45 CDU, north from 35∘ N),
negative deviations over North America and Eurasia in July (down to -0.45 CDU, north from 35∘ N), and
positive deviations over the Sahara, Middle East, India, and western China in July (up to 0.38 CDU).
The systematic deviations north from 35∘ N (1 and 2) are caused by the
longitudinal dependency of stratospheric NO2 from EMAC which differs
from the pattern in total column (see Fig. 3). In detail, stratospheric
NO2 over Siberia is quite low in EMAC, resulting in high-biased TR
(similar as for RSM) and indicating that the mean longitudinal dependency of
stratospheric NO2 is not fully reproduced by EMAC. Deviations in July
over Sahara and southern Asia (3), however, are at least partly caused by a
low bias of T∗ from STREAM as discussed in the previous section.
Overall, deviations are moderate, and STSEMAC still improves the
statistics of TR for high latitudes as compared to a simple RSM. It thus
might be considered as a simple alternative STS with the advantage that it
can be expected to work with the same performance for any satellite
instrument, independent of spatiotemporal coverage.
Monthly mean difference of tropospheric residues T∗ from
STSEMAC and STREAM for OMI measurements in January (top) and July
(bottom) 2005.
OMI after row anomaly
In 2005, OMI measurements were performed with good
instrumental performance, providing daily global coverage. This has changed
since summer 2007, when radiance measurements of poor quality regularly
occurred at particular cross-track positions (“row anomaly”). We thus also
tested STREAM on OMI data after the onset of the row anomaly: Figs. S25 and S26
show T∗ for 2010. While the daily maps reveal gaps due
to the exclusion of measurements affected by the row anomaly, the monthly
mean patterns as well as the statistical properties are comparable to the
results for 2005. The row anomaly thus does not impact the performance of
STREAM (or DOMINO or NASA retrievals).
Regional statistics of GOME-2 tropospheric residues T∗ from
different algorithms for January (top) and July (bottom) 2010.
Conventions as in Fig. .
GOME-2
STREAM has been applied to GOME-2 (Metop-A) data for the year 2010. The
resulting daily and monthly mean maps are shown in Fig. S27. Again,
statistical properties are summarized in Fig. .
The overall performance of STREAM, i.e., median and variability range of TR,
is generally similar to that found of OMI. However, while OMI TRs are about
0.1 CDU over the Pacific, lower values (0.05 CDU) are found for GOME-2. This
might be related to differences of cloud statistics due to pixel size, in
particular a lower number of fully clouded pixels for GOME-2, as well as
differences in local time, cloud products, or systematic spectral
interferences caused by clouds in either retrieval algorithm.
On 1 July 2010, GOME-2 is operated in narrow swath mode, causing poor
global coverage. This, however, does not affect STREAM performance.
On 15 January 2010, STREAM results in extraordinarily high TR over the ocean
(Fig. S28), which turned out to be caused by a solar eclipse (Espenak and Anderson, 2008). Removing the
affected orbit results in normal performance for this day. We recommend that
screening of solar eclipses be done automatically (as done for OMI) before
running any STS algorithm.
Comparison to NRT mode
STREAM is foreseen to be implemented in an update of the
operational GOME-2 data processor as operated in the framework of the O3M
SAF. This requires a slight modification of STREAM in order to work on NRT
data.
In STREAM v0.92, the stratospheric fields are estimated for each orbit based
on the total column measurements, including seven previous and seven subsequent
orbits. In NRT, however, no subsequent orbits are available. Thus, STREAM has
to be operated on the current plus 14 previous orbits instead.
We ran STREAM in NRT mode. The resulting maps are shown in Fig. S28, and the
statistics of TR are included in Fig. . The deviations
between baseline and NRT are marginal. Thus, STREAM can be operated in NRT
with stable performance.
Comparison to operational product (GDP 4.7)
In the current operational data processor (GDP 4.7), STS for NO2 is
done by an MRSM as described in . Basically,
polluted regions (defined by monthly mean TVCDs from the MOZART-2 model being
larger than 1 CDU) are masked out. Global stratospheric fields are derived by
low-pass filtering in zonal direction by a 30∘ boxcar filter.
Figure S30 displays daily and monthly mean maps of T∗ in January and
July 2010. The respective regional statistics are included in
Fig. .
Overall, TRs from GDP are relatively low. Over the Pacific, mean T∗ is
close to 0 in January, despite the applied tropospheric background correction
of 0.1 CDU. Over potentially polluted regions, median TR from GDP is
systematically lower (by 0.2 CDU in July) than from STREAM, and almost a
quarter of all TRs are even negative.
Figure displays the differences of the monthly mean TR
from GDP 4.7 and STREAM for January and July 2010, again pointing out the
systematically lower values of GDP TR over continents in July. The systematic low bias of GDP TR probably results from moderately polluted
pixels over regions labeled as “unpolluted”, which still might imply MOZART-2 TVCDs of up
to 1 CDU. These measurements cause a high bias of the estimated
stratospheric field around polluted regions; by the subsequent low-pass
filtering, this high bias is passed over the polluted regions and results in
low-biased TR. Further investigations are needed to find out why this effect
is stronger in July than in January.
Monthly mean difference of tropospheric residues T∗ from GDP
4.7 and STREAM for GOME-2 measurements in January (top) and July (bottom)
2010.
Regional statistics of SCIAMACHY tropospheric residues
T∗ from different algorithms for January (top) and July (bottom)
2010.
Conventions as in Fig. .
SCIAMACHY
We have applied STREAM to SCIAMACHY VCDs from the MPI-C NO2 retrieval
. The resulting daily and monthly mean maps for 2010 are
shown in Fig. S31. Regional statistics are provided in
Fig. , compared again to the simple RSM and, additionally,
to the results of LNM.
Though SCIAMACHY provides poorer daily spatial coverage, STREAM overall still
works well. Again, a clear reduction of the variability of T∗ is found
at high latitudes as compared to RSM. Over the Pacific, mean TR from
STREAM is higher than for the RSM (=0) but, similar to GOME-2, not as high
as for OMI. Again, this could be related to the low number of cloudy
satellite pixels and spectral interferences, affecting the DOAS analysis,
related to clouds. Overall, regional statistics of T∗ are similar to OMI
or GOME-2. However, a systematic latitudinal dependency of T∗ remains,
showing positive values in hemispheric summer and negative values in
hemispheric winter. This results from the latitudinal dependencies of
V∗ being different for clouded and cloud-free observations, as shown in
Fig. S33, for reasons not yet understood.
Monthly mean difference of tropospheric residues T∗ from LNM
and STREAM for SCIAMACHY measurements in January (top) and July (bottom)
2010. Gaps at high latitudes in January are caused by respective gaps in the
FRESCO cloud product.
Comparison to LNM
Within the SCIAMACHY MPIC NO2 retrieval , STS is
performed based on LNM. This can be considered as a completely different STS
approach, based on actual measurements but not involving CTMs or large-scale
interpolation, and thus provide a valuable information on STREAM performance.
Figure S32 displays the daily and monthly mean TR from LNM in 2010.
In the LNM STS, the latitude-dependent offset between nadir and limb is
determined over the Pacific and corrected for globally; i.e., mean TR in the
Pacific is 0 by construction. Overall, regional statistics of T∗ from
LNM are very similar to those from STREAM. Figure
displays the monthly mean difference of both algorithms. The deviation is
dominated by the latitudinal dependency of T∗ from STREAM (see above);
when this is removed, both algorithms agree within 0.2 CDU for
most parts of the globe.
GOME
GOME was the first instrument of the investigated series of UV/vis
spectrometers suited for DOAS analyses of tropospheric trace gases. The
comparably small number of GOME pixels and the large across-track footprint
(320 km) required a modification of STREAM: we have switched the resolution
of the global grid used for weighted convolution from 1 to 5∘,
i.e., wider than the GOME across-track width at moderate latitudes. Thereby it
is ensured that a grid pixel usually contains multiple satellite pixels and
that also adjacent grid pixels are not empty (as would be the case for an
1∘ grid), which is the prerequisite for the calculation of wTR.
We have applied STREAM to GOME data as provided by TEMIS. The resulting TRs
are again compared to the simple RSM. Figure displays the
regional TR statistics for GOME in January and July 1999. The respective maps
are provided in the Supplement (Fig. S34).
Overall, STREAM yields reasonable results for GOME as well.
However, some systematic biases are observed:
over the Pacific, TRs from STREAM were found to be negative, which can only be explained when the measured columns for cloudy
pixels are higher than for cloud-free pixels;
over potentially polluted regions, T∗ from STREAM is systematically
lower than from RSM (by 0.2 CDU in July). This might be a consequence
of the applied cloud weight, which has obviously different effects on GOME
than on OMI.
Regional statistics of GOME tropospheric residues T∗ from
different algorithms for January (top) and July (bottom) 1999.
Conventions as in Fig. .
This explanation would be consistent with previous findings: while
base the STS on cloudy pixels, switched the
Heidelberg STS to cloud-free pixels after noticing that GOME columns are
higher instead of lower over clouds. relate this to the
contribution of lightning NOx. However, as (a) the impact of
lightning NOx on satellite observations is generally small
and (b) lightning activity over the remote Pacific used
for the RSM is very weak, we rather suspect that a different effect is
responsible for this finding, most probably related to the specific
instrumental features of GOME , in particular the dichroic
mirrors causing polarization dependent spectral structures. It might thus be
worth re-checking the DOAS analysis of NO2 for GOME for spectral
interferences related to clouds. A second possible effect, which might in
particular contribute to the large discrepancy over polluted regions, is that
cloud properties are averaged over the large GOME ground pixel; i.e., in an
extreme case, low and high cloud layers, which would both be skipped in
wcld if resolved by the satellite pixel, might yield, on average, an
effective cloud height with a high wcld. Any tropospheric
pollution within (or directly above) the low cloud layer would then bias high
the stratospheric estimate and bias low the TR.
Future instrumentsTROPOMI
TROPOMI on S5p will be
launched in 2016. Instrumental setup and spatial coverage are similar to
OMI, but TROPOMI will provide a better spatial resolution of 7 × 7 km2 at nadir.
STREAM was developed as a verification algorithm for TROPOMI STS and was tested
and compared to the TROPOMI prototype algorithm based on OMI measurements
(see above). Though no TROPOMI measurements are available yet, it can be
expected that the performance of STREAM on TROPOMI will be even better than
for OMI, because, due to the better spatial resolution, more individual
satellite pixels are available and among them a higher fraction of clouded
pixels. Thus, more sampling points over potentially polluted regions will be
available, further decreasing interpolation errors.
Sentinel 4 (S4)
The satellite instruments investigated so far are all operated in low,
sun-synchronous orbits, providing global coverage at fixed local time. In the
near future, a new generation of spectrometers on geostationary orbits will
be launched by different space agencies. Over Europe, S4 will be the first mission providing a spectral
resolving UV/vis instrument on a geostationary satellite. The spatial
coverage is focussed on Europe. Thus, no “clean” reference regions are
regularly available. STREAM might overcome this problem by using clouded
observations where the tropospheric pollution is effectively shielded.
We simply evaluate the expected performance of STREAM on S4 measurements by
clipping OMI measurements to the area covered by S4 as given
in. The STREAM settings are identical to v0.92, except for the
a priori removal of the overall latitude dependency in the reference sector,
as no Pacific measurements are available for S4. Figure
displays the resulting TR (top) and the difference of TR between clipped and
global OMI data (bottom) for January 2005.
Performance of STREAM on “S4 data” (i.e., OMI measurements clipped
to the area covered by S4) for January 2005. The top panel displays the
resulting TR, the bottom panel shows the difference to the TR resulting from
full OMI data as shown in Fig. .
The area covered by S4 in winter has been taken from
.
Though tropospheric pollution over Europe and the Middle East is evident,
i.e., an extended clean reference region is actually not available, STREAM is
still capable of yielding an accurate stratospheric estimate. Only at the
northern and southern borders are systematic biases observed, which can be
caused by the overall latitudinal dependency of the stratospheric VCD and
border effects of the weighted convolution and can probably be reduced by
dedicated optimization of the algorithm for S4.
Situation will probably be improved for real S4 measurements due to the
higher number of clouded pixels in S4 compared to OMI. Thus, this first check
is highly encouraging to further investigate the applicability of STREAM to
S4 and possible improvements.
Advantages and limitations of STREAM
STREAM was successfully applied to various
satellite measurements with a wide range of spatial resolution and coverage.
STREAM is an MRSM and does not need any model input. It can thus be considered
as a complementary approach to data assimilation, as chosen for the TROPOMI
prototype algorithm.
As (M)RSMs usually estimate the stratospheric column based on total column
measurements over clean regions, they generally miss the (small) tropospheric
background of the order of some 0.1 CDU. Several (M)RSMs explicitly correct
for this effect based on a priori tropospheric background columns
. In case of STREAM, however, cloudy
pixels, which allow a direct measurement of the actual stratospheric column
(except for the small tropospheric column above the cloud), are emphasized.
Thus, an additional tropospheric background correction should be unnecessary.
Accordingly, in case of OMI, TRs from STREAM are about 0.1 CDU over clean
regions, similar as for TRs from DOMINO and NASA. This is close to the
a priori value chosen by but below the values given in
(about 0.15–0.3 CDU, assuming a tropospheric AMF of 2) and
(0.1 up to > 0.6 CDU
Note, however, that the high values are
only reported at higher latitudes in winter, when the ratio Astrat/Atrop is almost 2 (Fig. S1); thus the large discrepancy is at
least partly resolved when the TR is transferred in a TVCD via
Eq. ().
).
In case of other satellite instruments, however, the TR over the Pacific was
found to be lower (GOME-2 and SCIAMACHY) or even negative (GOME-1). The
latter can only be explained by cloudy measurements being systematically
higher than cloud-free measurements. Further investigations are needed to
infer this discrepancy between OMI and GOME-1/2/SCIAMACHY and find how it is
related to differences in the cloud products and/or the spectral analysis of
NO2.
STREAM assumes stratospheric NO2 fields having low zonal variability,
in particular at low latitudes. This is reflected by the choice of a wide
convolution kernel at the equator. STREAM is thus not capable of resolving
diurnal small-scale patterns caused by stratospheric dynamics. These
patterns, however, largely cancel out in monthly means.
Whenever actual stratospheric fields do not match the a priori assumption of
zonal smoothness, e.g., in case of “tilted” structures or actual large-scale
zonal gradients like differences in the stratospheric column over Pacific
and Indian ocean, the TR resulting from STREAM can show artificial
“stripes”. Further investigations might lead to additional sophisticated
algorithm steps to remove these artifacts. However, it has to be taken care
that the benefit really outbalances the drawbacks (added complexity) and
that no other artifacts/biases are introduced.
The dependencies of TR on STREAM parameter settings have been found to be low
(≲ 0.1 CDU). The application of STREAM on synthetic data results in
deviations to the a priori truth of the same order. These deviations are
systematic, i.e., the stratospheric patterns estimated by STREAM are slightly
biased high, which can be expected, as they are based on total column
measurements, which are always higher than the stratospheric column.
Overall, STREAM uncertainty is well within the general uncertainties of STS
(see next section). Note that systematic changes of the NO2 columns
of the same order of 0.1 CDU can also result from changes of the settings for
the DOAS analysis, like fit interval, inclusion of additional absorbers in
the analysis, or the treatment of rotational and vibrational Raman
scattering, creating overall biases as well as spatial patterns, e.g., over
oligotrophic oceans (E. Peters, personal communication, 2016).
General uncertainties and challenges of stratosphere–troposphere separation
The uncertainty of STS can often not be directly quantified, as the “true”
stratospheric 4-D concentration fields are not known. One approach to assess
the STS performance is the usage of synthetic data, as in Sect. . In addition, the TR can be used to evaluate the
plausibility of the stratospheric estimate and to derive realistic
uncertainties:
Negative TRs are nonphysical. Thus, the occurrence of on average
negative T∗ (exceeding the values/frequencies explainable by noise) clearly indicates a positive bias of the estimated stratosphere.
Tropospheric background columns over regions free of NOx
sources are expected to have low spatiotemporal variability. Thus, the
observed variability of T∗ over clean regions serves as proxy of the uncertainty (precision) of the STS.
From different algorithms (MRSMs as well as model-based methods),
typical variabilities of T∗ over remote regions are about 0.5 CDU for
daily means and about 0.2–0.3 CDU for monthly means. For a simple RSM, much higher values (≈ 1 CDU) are found at high latitudes.
Systematic biases (accuracy) of STS can be estimated from the
intercomparison of TRs from different algorithms. Figure
displays the standard deviation of monthly mean TR from the algorithms shown
in Fig. and discussed in Sect. ,
i.e., two different, independent MRSM approaches (STREAM and NASA) as well as
two STS based on models, a simple one (STSEMAC) and a complex data
assimilation setup (DOMINO). Note that the upper range of the color bar was
lowered to 0.3 CDU.
Overall, the standard deviation of TR from different STS is low (typically
< 0.1 CDU and below < 0.2 CDU for most parts of the world). It is thus
consistent with the uncertainty estimates of stratospheric columns given in
literature (: 0.15–0.25 CDU (SCD); :
0.15–0.3 CDU (VCD); : 0.2 CDU (VCD)) and with the
magnitude of systematic deviations found in the study on synthetic data
(Sect. ).
With respect to the final NO2 TVCD product, which is higher than TR
by the ratio of stratospheric and tropospheric AMFs (Eq. ),
uncertainties of this order are completely negligible over polluted regions
such as the US east coast, central Europe, or eastern China. Nonetheless, a
regional bias > 0.2 CDU (e.g., over Russia in January) can contribute
significantly to the relative uncertainty of TVCDs aside the pollution
hotspots. Thus, the uncertainty of STS has to be kept in mind in studies
focusing on NOx emissions from, e.g., biomass burning or soil emissions
over regions like Siberia, the Sahel, or Australia.
Standard deviation of monthly mean T∗ from different
algorithms (STREAM, DOMINO, NASA, and STSEMAC) for January (top)
and July (bottom) 2005 (OMI).
Other trace gases
STREAM was developed as STS algorithm for NO2. However, several other
trace gas satellite retrievals face problems which are similar to STS from an
algorithmic point of view, i.e., that a small-scale tropospheric signal has
to be separated from a smooth background (e.g., caused by stratospheric
columns or, in particular in case of trace gases with low optical depth,
shortcomings of the spectral analysis, introducing artificial dependencies
on,
for example, SZA or ozone columns). Thus, the concept of weighted convolution could
be used within the satellite retrievals of, for example, SO2, BrO, HCHO, or
CHOCHO, with appropriately chosen and optimized weighting factors.
Conclusions
The separation of the stratospheric and tropospheric column is a key step in
the retrieval of NO2 TVCDs from total column satellite measurements.
As coincident direct measurements of the stratospheric column are usually not
available (except for SCIAMACHY), current STS algorithms either use CTMs
(directly or via data assimilation) or follow a modified reference sector method (MRSM) approach, where the stratospheric columns are basically
estimated from total column measurements over clean regions.
We have developed the MRSM STREAM. Weighting factors determine how far
individual satellite pixels contribute to the stratospheric estimate. Over
potentially polluted regions (according to an NO2 climatology),
weights are lowered, whereas measurements over mid-altitude clouds are
assigned with a high weighting factor. Global stratospheric fields are
derived by weighted convolution and subtracted from total columns to yield
tropospheric residues (TRs). In a second iteration, weighting factors are
modified based on the TR: high TR indicates tropospheric pollution, and the
respective satellite pixels are assigned with a lower weight. For
systematically negative TR, however, weighting factors are
increased. The concept of multiplicative weights can easily be extended by
additional factors, e.g., based on fire counts in order to explicitly exclude
biomass burning events.
STREAM results are robust with respect to variations of the algorithm
settings and parameters. With the baseline settings, the errors of STREAM on
a synthetic data set have been found to be below 0.1 CDU on average.
STREAM was successfully applied to satellite measurements from GOME 1/2,
SCIAMACHY, and OMI. The resulting TRs over clean regions and their variability
have been found to be low. However, systematic “stripes” can appear in
STREAM TR when the basic assumption that the stratospheric column varies
smoothly with longitude is not fulfilled, e.g., in case of “tilted”
stratospheric patterns.
The emphasis of clouded observations, which provide a direct measurement of
the stratospheric rather than the total column, should supersede an
additional correction for the tropospheric background, which successfully
worked for OMI but less so for GOME and SCIAMACHY. This might be related to
differences in pixel size or local overpass time, both potentially affecting
cloud statistics, or differences in the cloud algorithms. However, the
detailed reasons are not yet fully understood and require further
investigations.
STREAM, which was developed as TROPOMI verification algorithm, was optimized
for OMI measurements. Within an O3M SAF visiting scientist project, it was
also applied to GOME-2, and STREAM is foreseen to be implemented in an
upcoming GDP update.
Results from STREAM were compared to the TROPOMI prototype algorithm, as
represented by the DOMINO v2 product, in which STS is implemented by data
assimilation. Differences between monthly mean TRs from STREAM and DOMINO are
found to be low (almost 0 on average with regional patterns up to about
±0.1–0.2 CDU). A comparison to other state-of-the-art STS schemes yields
deviations of similar order.
The impact of STS is thus generally negligible for TVCDs over heavily
polluted regions. However, the remaining uncertainties still contribute
significantly to the total error of TVCDs over moderately polluted regions
and have to be kept in mind for emission estimates of area sources of
NOx such as soil emissions or biomass burning.
Data availability
STREAM has been tested on NO2 retrievals from different satellite
instruments as listed in Table 1. The input data sets are publicly
accessible; the respective links to the data sets are included in the
references provided in Table 1.
Information about the Supplement
Additional images, tables, and text are provided in the Supplement. All
references to tables and figures in the Supplement are indicated by a prefix
“S”. For readability, the Supplement is structured analogously to the
paper; i.e., additional material to Sect. 2.3 can be found in Sect. S2.3 of
the Supplement.
The Supplement related to this article is available online at doi:10.5194/amt-9-2753-2016-supplement.
Acknowledgements
We would like to thank the agencies providing the satellite data:
The OMI OMNO2 product is archived and distributed from the Goddard Earth Sciences Data & Information Services center (NASA).
We acknowledge the free use of GOME and OMI (DOMINO) level 2 data from www.temis.nl.
GOME-2 GDP 4.7 level 2 data is provided by DLR within the framework of EUMETSAT's O3M SAF.
SCIAMACHY level1 data is provided by ESA.
This study was supported by the following projects:
Within TROPOMI verification, funding was provided by DLR Bonn under contract 50EE1247 for the development of STREAM and the comparison to the TROPOMI prototype algorithm.
Within an O3M SAF visiting scientist project, STREAM was adopted to GOME-2 and compared to the current GDP 4.7 data product.
Steffen Beirle acknowledges funding from the FP7 Project QualityAssurance for Essential Climate Variables (QA4ECV), no. 607405.
We acknowledge fruitful discussions with and valuable feedback from many
colleagues, in particular during the TROPOMI verification and QA4ECV
meetings, especially Andreas Richter, Andreas Hilboll (both IUP Bremen,
Germany), Folkert Boersma, and Henk Eskes (both KNMI De Bilt, the
Netherlands). Andreas Stohl (NILU, Kjeller, Norway) is acknowledged for
discussions on NO2 transport.The article processing charges for this open-access publication were covered by the Max Planck Society.
Edited by: B. Veihelmann
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