Review: ’OMI total bromine monoxide
(OMBRO) data product: Algorithm, retrieval
and measurement comparisons’ - revised
manuscript (v4)
July 1, 2018
The manuscript ’OMI total bromine monoxide (OMBRO) data product: Algorithm,
retrieval and measurement comparisons’ describes the status of the operation
OMBRO data product, which provides BrO VCDs retrived from OMI
data. Comparisons to ground-based observations and other satellite observations
are presented and small case studies for salt-lakes are shown. The global
distribution of VCDs is qualitatively compared and found to be similar to those
of GOME-2. The data product was found to have problems in case of volcanic
eruptions due to interferences due to a sub-optimal choice of the SO2 absorption
cross-section.
The manuscript fits perfectly the scope of AMT and should be published for
two reasons: Once to have a solid reference for the spectral retrieval settings for
satellite retrieved BrO VCDs and also to provide a good reference for end users
of the OMBRO dataset. The end users finally need to know the errors which
are already introduced by approximations in the spectral retrieval and the conversion
to trace gas VCDs in order to estimate the significance of their findings.
Typical end-users cannot estimate the importance of different fit parameters of
the data product as already pointed out by referee Nr. 3.
A lot of points have been already presented by the two other reviewers,
therefore I’ll try to keep this short.
In my opinion a publication should represent the current state of the art of
the respective topic. I understand the limits of the operational data products
and I know that such a dataset cannot be reprocessed within short time. However,
a publication could list quantitatively uncertainties and also shortcomings
of the current data processing. By doing this there would be no need for reprocessing
the whole data set (at least for this publication) while providing the
end user with the necessary error analysis and already paving the way towards
a better and improved data product. This way, the manuscript would be also
helpful for other scientists working on the retrieval of weak trace gases in this
spectral region from satellite observations.
For the current manuscript however, I don’t see that this is the case here,
and I furthermore do not see substantial improvement towards this direction
from the intial to the revised manuscript.
Major points:
1. The absorption of the oxygen collision complex (short: O4) is not included
in the spectral fit settings (c.f. e.g. [Greenblatt et al., 1990, Hermans et al., 1999,
Thalman and Volkamer, 2013]. These are used also in [Richard et al., 2012]
from SAO ). Using a medium to large typical O4 SCD of 4 × 1043 molec2 cm−5
, we obtain an optical thickness of 3.6×10−3 at 344 nm [Thalman and Volkamer, 2013]
and 6×10−4 at 328 nm [Lampel et al., 2018]. As both of these absorption
peaks are larger than the BrO absorption shown in Figure 1, its impact
should be discussed and analysed thoroughly in this manuscript and last
but not least also included in Figure 1. These absorption are well known
and established (at least at 344 nm) as can be seen from the publications
listed. As the bulk of the O4 absorption is found close to the surface,
it’s particularly sensitive to albedo changes and topography for satellite
observation geometries. As mentioned by the other reviewers both factors
play a role at the salt-lake and the Dead Sea Valley.
2. The AMFs for BrO are calculated and included in the fit, while the significantly
stronger absorption of ozone is included without considering
radiative transfer effects. Here an approach like the one presented in
[Puk,¯ıte et al., 2010] could be applied, at least within the error analysis.
These effects do not only appear in DOAS approaches, but also in intensity
fitting or BOAS. [Vogel et al., 2013] showed that below 330 nm ozone
absorption can have a large impact on the BrO results, at least if not
considering these effects.
3. A table of potential sources of interferences (literature cross-sections, fit
settings, AMFs, uncertainties of other absorbers ..) and their magnitudes
should be included in the manuscript. An example can be found at http:
//www.tropomi.eu/sites/default/files/files/S5P-BIRA-L2-ATBD-HCHO_
400F_TROPOMI_v1p0p0-20160205.pdf and/or [De Smedt et al., 2012]. A
list of current literature absorption cross-sections can also be found there.
In general the observations on HCHO/BrO interferences from various
DeSmedt et al publications are missing here and could also be of importance
for BrO retrievals.
4. The spectral retrieval includes averaged residual spectra: The OMI
ATBD V4 states: ’Remaining systematic residuals which are, by definition,
uncorrelated to the trace gas spectra may be averaged and included in
the spectrum fitting as a “common-mode” spectrum, to reduce the fitting
rms and, proportionally, the fitting uncertainties, when they depend on
the rms(eq. 5-3)’.
This is not true as residuals are typically the result
of an unaccounted absorber or effect and can be partially compensated
for by the included absorptions, thus effectively resulting in erroneous
SCDs. As typical residuals include contributions from various sources and
due to various reasons, including them together as one spectrum links
basically their relative sizes together and can therefore lead again to erroneous
SCDs. The residual spectra can be used as a tool for data analysis
(the missing O4 absorption should be easily visible if the residual spectra
would have been inspected), but including the residual in the final fit lowers
RMS, but not the measurement errors itself. It would be instructive
to include a typical residual spectrum in the cross-section overview figure
Figure 1.
5. By mentioning tropospheric BrO from volcanoes and in polar regions it
seems as if the data set can also be used for analysis of tropospheric
BrO VCDs. However, as also stated in the manuscript, the AMF calculations
assumed a stratospheric profile. Assuming typical tropospheric
concentration profiles from the available literature error estimates could
be provided. These cannot be found in the current revised manuscript.
It is thus difficult if not impossible to use the OMBRO dataset for tropospheric
applications. Depending on albedo, geometry and aerosol load
could either under- or overestimate the real VCD. These errors need to be
quantified for further reasonable use of this dataset by end-users.
6. p6l21: You use the preflight instrument function from Dirksen et al 2006.
Is this for the BrO range consistent with the in-flight instrument function?
Is the instrument function of the respective OMI channels constant over
time? What is the impact of the findings presented by [Sun et al., 2017] on
such a weak absorber as BrO? For GOME-2 a north-south dependence was
found due to temperature issues [Munro et al., 2015, Beirle et al., 2017].
This could be of importance for weak absorbers being overlayed by large
absorptions and/or differences in instrument function between reference
and measurement and could explain the need to include residual spectra
in the fit (is that really needed?). Has this been studied? This should be
included in the error analysis.
Minor points:
1. mentioned also by other reviewers for the initial manuscript, again found
in the revised manuscript: p6l12: comparison to DOAS fitting methods:
Is this an advantage or disadvantage? What does this sentence add to
the content of the manuscript? Looking at current DOAS evaluations (see
e.g. [Wang et al., 2017]) you can see that high-pass filtering is often not
applied, apart from the so called DOAS polynomial in OD-space, which
is also used in the presented manuscript. Please check again if all points
of the initial reviews are considered in your revised manuscript.
2. The town Barrow is called Utqiagvik since 2016 (https://de.wikipedia.org/wiki/Utqiagvik).
3. [Vogel et al., 2013] should be mentioned as the choice of the fit interval
for BrO seems to be crucial for the reliability of the resulting data set. It
contains a list of previously used fit settings which is missing within this
manuscript.
4. How do the different OMBRO versions compare to each other? This could
be also used to estimate the overall error margins.
5. Please consider also more recent ozone literature cross-sections (e.g. [Serdyuchenko et al., 2014])
for future data products, as well as the temperature dependence of the
Ring effect [Volkamer et al., 2015] and the Ring’s wavelength dependence
[Wagner et al., 2009] which is often compensated for in air-borne or groundbased
analysis of scattered sun light spectra.
6. The number of validation/comparison opportunities is especially low outside
polar regions: How does the OMBRO data set compare quantitatively
to the data presented in [Hörmann et al., 2016] for the Rann of Kutch?
7. How do the VCDs from GOME2 compare quantitatively to those in OMBRO
on a global scale and not only at nadir viewing direction? How do
e.g. monthly means of both instruments compare to each other?
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