the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Apr 2025
Mapping seasonal nitric acid (HNO3) patterns in the extratropics with nadir-viewing infrared sounders – a retrieval perspective
Abstract. With this paper, we aim to shed light on the extent to which nadir-viewing hyperspectral infrared (IR) sounders can support the study of stratospheric chemical processes and ozone loss in the extratropics. We use CLIMCAPS (Community Long-term Infrared Microwave Combined Atmospheric Processing System) retrievals from JPSS-1 (Joint Polar Satellite System) CrIS (Cross-track Infrared Sounder) measurements as the baseline case. CLIMCAPS retrieves a suite of Earth system variables that includes atmospheric temperature (Tair), water vapor (H2Ovap), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), ozone (O3), and nitric acid (HNO3). Unlike the Rodgers (2000) Optimal Estimation (OE) retrieval approach, CLIMCAPS regularizes its Bayesian inverse solution dynamically using singular value decomposition (SVD) at run-time to separate measurement signal from noise. We illustrate how the CLIMCAPS approach enables stable retrievals of stratospheric HNO3 under highly variable conditions, allowing characterization of seasonal patterns. Nitric acid is typically used as an indicator species for heterogeneous chemical processing inside the winter stratospheric polar vortices. This paper summarizes our diagnostic evaluation of CLIMCAPS observing capability during the Northern Hemisphere winter of 2019/2020. We contrast CLIMCAPS HNO3 retrievals with those from the limb-viewing MLS (Microwave Limb Sounder) to illustrate the capability of this retrieval approach and lay the foundation for in-depth validation studies in future.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-1569', Anonymous Referee #1, 30 Jul 2025
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AC1: 'Reply on RC1', Nadia Smith, 02 Oct 2025
Citation: https://doi.org/10.5194/egusphere-2025-1569-RC1
[Reviewer]: The authors present a progress report on the development of the CLIMCAPS HNO3 retrieval, building on their own preceding work. Background to these efforts is the foreseeable “limb gap” caused by the imminent discontinuation of current limb sounders operation. The aim of the development is to obtain a data product that represents conditions in the stratosphere, specifically temporal and spatial patterns and developments. This goal is attempted to be achieved through the appropriate selection of spectral ranges and dynamic regularization adapted to local atmospheric conditions.
The technical part of the paper, i.e., the investigation into the selection of a suitable parameter set, is carefully presented. However, there are some weaknesses in terms of clarity and structure in the general presentation and also in the presentation and critical discussion of the comparisons with MLS data. Some aspects deserve better or more detailed presentation and discussion.
My general comments are as follows:
[Reviewer]: The introduction should be better structured, possibly with the help of subsections, since some of the information it contains seems scattered and disjointed. Some later, and more generic, passages could be inserted here (example see below).
[Response]: Noted. We have re-evaluated and edited the introduction for clarity, with the suggested passage now incorporated.
[Reviewer]: The use of the term averaging kernel (AK) is inconsistent and in some cases does not follow standard terminology.
[Response]: We have clarified our use of the AK term upon first mention (Line 227) and paid close attention to consistency through the paper.
[Reviewer]: The curves in Figure 2 [now Figure 3] do not represent the AK profiles as stated, but rather the profiles of the diagonal values of the averaging kernel matrix (AKM). More accurately, the rows of this matrix would be called averaging kernels, as it is done, e.g., in Figure 4 of Smith & Barnet (2020). There, the profiles of the diagonal values in Figure 5 are referred to as “averaging kernel diagonal vectors.”
[Response]: We have updated the caption for Figure 3 (previously Figure 2) on Lines 438–442 as follows: “A statistical summary of CLIMCAPS signal-to-noise ratio (SNR) for all the temperature (left), O3 (middle) and HNO3 (right) retrievals north of 40˚N latitude on 2 February 2020. The profiles represent the average of the respective averaging kernel matrix diagonal vectors (AKD) with standard deviation error bars to indicate the degree to which CLIMCAPS SNR varies across retrieval pressure layers within the study region. CLIMCAPS retrieves temperature on 31 pressure levels, O3 on 11 layers, and HNO3 on 8 layers, hence the difference in the number of error bars across the three variables.”
[Reviewer]: Figure 2 [now Figure 3]: Instead of the profiles of the diagonal values of the AKM, it would be desirable and instructive to see the AKs (i.e., the rows of the matrix). By their shapes, one can get an impression of the altitude range which contributes to the HNO3 profile at a given pressure level.
[Response]: We have added a new figure (Figure 2) on Line 257 to depict the individual AKDs for each of the experimental configurations.
[Reviewer]: p.7ff, l.202ff Please do not present the mathematical relations between the quantities in the running text, but rather use equations with numbering. This also facilitates references in the text.
[Response]: We have changed the presentation of equations accordingly.
[Reviewer]: A more detailed description of the retrieval quantities and their mathematical relations, or at least references to where the respective relations are documented, would be beneficial. Perhaps a brief recapitulation of the retrieval approach and the related quantities in a separate section earlier in the text would be appropriate.
[Response]: We have added Equation 1 as overview of the generalized Bayesian signal inversion equation on Line 189 that we then discuss in the following paragraph.
[Reviewer]: I have the most problems with the presentation of the comparison between VLIMCAPS-x and MLS HNO3 in Figure 4. Only for November and February do the spatial patterns seem to match well, while the same cannot be said for the other three months. There, the spatial patterns are clearly different. Based solely on this initial comparison, one would conclude that the desired/attempted reproduction of spatial gradients (see p.17, l.446) is not working reliably. A critical discussion of this comparison is virtually completely missing, and therefore has to be supplied in the revised manuscript. In particular, the question arises as to how future data users can recognize when the data adequately reflects stratospheric conditions and when it does not.
[Response]: We have added the following text to the discussion of Figure 5 (previously Figure 4) on Lines 557–569: “Note how the spatial patterns of CLIMCAPS HNO3 strongly align with those from MLS at the onset of the vortex in November, and again as HNO3 reaches its first distinct seasonal feature in February. By March, however, the CLIMCAPS HNO3 feature weakens relative to that from MLS and by April is largely absent as the temperatures in the vortex start to rise. By May, the vortex has dissipated (as seen in the CLIMCAPS temperature and O3 maps), along with the distinct seasonal HNO3 feature in both products. It is worth noting that CLIMCAPS HNO3 registers a strong low-HNO3 feature (such as that visible on 2 February 2020) only when coincident with wintertime minima in both temperature and O3, never outside of the conditions indicating the presence of the winter polar vortex (not shown). Additionally, note the strong agreement in spatial patterning between MLS HNO3 and CLIMCAPS O3 throughout the season, while the same cannot be said for the colocated CLIMCAPS HNO at this stage. It is worth reminding the reader here that a mature, optimal CLIMCAPS HNO3 product does not exist yet. Figure 5 presents CLIMCAPS retrievals produced by the experimental R4 configuration, which is only a first step towards achieving a viable stratospheric nadir IR HNO3 product in future. It will be interesting to determine the degree to which the CLIMCAPS HNO3 retrieval can be optimized for a better correlation with MLS HNO3 throughout the lifetime of the Arctic vortex.”
[Reviewer]: There are the following comments regarding the technical implementation of the comparison shown in Figure 4 [now Figure 5]: First of all, it is not clear whether and how the MLS data belong to one specific fixed level or whether they have been averaged over several levels. In general, for the eye it is difficult to compare a map of values, such as those used for CLIMCAPS-x data, with the sampled MLS data. A purely visual comparison is easier to do when the CLIMCAPS-x HNO3 data is sampled, and shown, at the locations of the MLS measurements.
[Response]: We added text to clarify our presentation of the two stratospheric HNO3 products. Lines 539–543: “. CLIMCAPS HNO3 has global coverage (90˚S to 90˚N) and is presented here as spatial averages on a 4˚ equal-angle grid. The MLS product is the Level 2 V005 HNO3 mixing ratio (Manney, 2021) that has near-global coverage (82˚S to 82˚N) and profile retrievals spaced 1.5˚ along the orbital track with roughly 15 orbits per day. We integrated both profile products across their retrieval layers spanning 30–100 hPa for ease of comparison.”
We note the Reviewer’s preference here, but this paper does not aim to present a one-to-one comparison between colocated observations. Adding such a figure (and discussion) would distract from the overall message. Instead, we present the two products in their native form to contrast their differences in spatial coverage and vertical resolution. We have made this clear in the communication of the goals throughout the revised paper.
[Reviewer]: Of course, I understand that this presentation is mainly concerned with the development of the retrieval and not meant to anticipate a validation that is still to be carried out in future. On the other hand, the two HNO3 datasets are so different in terms of altitude resolution that I would strongly advise to transform the MLS data (regarded as "the truth"), using AKM and a priori of the CLIMCAPS-x retrieval, to that profile that would be seen by the nadir instrument/retrieval combination. I think there is no other reasonable way for a sound and robust comparison.
[Response]: Noted, but we wish to argue against employing this technique typically reserved for traditional validation studies. As stated above, this paper does not aim to present a validation of an optimized CLIMCAPS HNO3 product with MLS HNO3 as “the truth”, because in this paper the CLIMCAPS HNO3 product is not yet optimized. Instead, this paper presents the evidence that CrIS measurements (and those made by other nadir IR sounders such as AIRS and IASI) can be decomposed into eigenfunctions to allow the separation of their stratospheric and tropospheric HNO3 signals. This has never been demonstrated before.
Moreover, we cannot convince funding agencies in the US to support the maturation and validation of a CLIMCAPS HNO3 product without evidence that such a product is viable in the first place. This paper, therefore, presents the first and necessary step towards the maturation of a CLIMCAPS HNO3 product that can be ready for validation and public release in future. Discussion has been revised throughout the manuscript to better emphasize this point.
And finally, there are these specific points:
[Reviewer]: p.1, ll.1-2 Shouldn't the title already contain, or refer to, “stratospheric”?
[Response]: We have changed the title to “Mapping seasonal stratospheric nitric acid (HNO3) patterns in the extratropics with nadir-viewing infrared sounders – a retrieval perspective”[Reviewer]: p.3 l.83 "MLS-like" I think this can be stated more general (e.g. "limb-viewing instrument")
[Response]: After some consideration, we maintain that the suggested change would not be suitable, since the operational and scientific success of MLS in general is not merely tied to the instrument’s limb-viewing capability, but also to its spectral range and resolution. Here, we specifically mean an MLS-like instrument capability.
[Reviewer]: p.4 l.102 Strictly speaking, contrary to FORLI, MLS is no retrieval system.[Response]: The wording has been changed to “has been widely adopted in many retrieval systems, including the one used for MLS and FORLI”.
[Reviewer]: p.5 l.147 Parentheses seem to be unnecessary.[Response]: The parentheses have been deleted.
[Reviewer]: Table 1 Please give the relation between lambdac and Bmax and their definitions before the quantities are used.[Response]: We have added Equation 2 to Line 218 for an introduction of these quantities ahead of their presentation in Table 1.
[Reviewer]: p.7, l.192 & p.8, l.215 How can a SNR be destabilized?[Response]: We added the following explanation to lines 277–281: “SNR can be destabilized when the noise is high relative to the signal, or when the noise fluctuates dramatically relative to the signal from scene-to-scene. Additionally, SNR can be destabilized when the noise (random and systematic) is not well-characterized and quantified such that it is wrongly interpreted as signal instead. Similarly, SNR can be destabilized when signal is wrongly interpreted as noise.”
[Reviewer]: p.7, l.193 RTAERR=0 is not an underestimation? That means that destabilization cannot occur?
[Response]: On lines 282–288 we added the following text as clarification: “So while RTAERR = 0 is technically an under estimation, it is close in magnitude to the real RTAERR and therefore not destabilizing. Historically, RTAERR was installed as an attempt to lower the weight of channels that had poor spectroscopic laboratory measurements or a large RTAERR value. In 1995, in the pre-launch AIRS era, this term was expected to be rather large — on the order of ~1˚ K for many channels — especially in the water band region. After the AIRS launch, the RTA fitting procedure was improved and more recent laboratory spectroscopy measurements were incorporated so that over time, the RTAERR term was reduced to very low values (<0.01˚ K for most channels). We now simply ignore the few remaining channels that have high RTA errors so that setting RTAERR = 0 is no longer deemed an issue for stability.”
[Reviewer]: p.8, l.205 I think that i is meant to be an index for the height level. Is it counted from top to bottom, as implied by the order of the pressure values in Table 1, or from bottom to top?
[Response]: We have added a clarification of index i on line 201.
[Reviewer]: p.9, l.246 Why do the DOFS represent the sum of all Rfac_i values? Is there a reference?[Response]: We have removed this sentence to avoid confusing the reader.
[Reviewer]: p.12, l.333 This would be a good place to refer to the (newly introduced) equation number from Section 3.[Response]: We have added references to equations 1–4 wherever appropriate throughout the revised text.
[Reviewer]: p.15, ll.421,422 mix-up of East & West?[Response]: This error has been corrected.
[Reviewer]: p16,17 ll454-451 This would fit nicely into the introduction as part of a general motivation subsection.[Response]: We have reworked the introduction accordingly.
[Reviewer]: In Figure 3 (now Figure 4), the individual maps would benefit from clearer visibility of the longitude and latitude grid.[Response]: We agree with the reviewer in principle but wish to be careful not to over-complicate the figures. After careful deliberation, we decided to add four longitudinal labels to each map.
[Reviewer]: Why is Greenland so clearly visible in Figure 3 (now Figure 4) for R3-R5, both in dof and in stddev(dof)? And (roughly) the Pyrenees and the Bering Sea?[Response]: We added the following text on Lines 567–573 for the sake of clarification: “One aspect that needs further investigation is how Earth surface conditions affect the HNO3 retrieval. The 850–900 cm-1 spectral region sensitive to HNO3 (Figure 1) is colloquially known as the IR window region because it is predominantly sensitive to Earth surface conditions, and specifically to surface emissivity and skin temperature. This means that CLIMCAPS needs to accurately account for Earth surface conditions as a source of geophysical noise during each HNO3 retrieval. That the HNO3 retrievals are consistently elevated over some parts of Greenland (~45˚W), relative to the surrounding HNO3 retrievals over ocean, throughout most of the season (Figure 5) is evidence that the R4 configuration is not yet optimized. This indicates that we need to investigate how icy land surfaces are represented in the retrieval.”
[Reviewer]: p.19, l83 "that the resulting HNO3 product generally reflects real atmospheric variations" is not supported by Figure 4: It is not generally true, but only under certain conditions (see my general comments).[Response]: We have edited the sentence on Lines 643–641 to read as follows: “In a series of CLIMCAPS retrievals throughout the northern hemisphere winter of 2019/2020 using the R4 configuration, we illustrated how CLIMCAPS HNO3 compares against MLS HNO3 and demonstrated that it reflects real stratospheric patterns under some conditions.”
Citation: https://doi.org/10.5194/egusphere-2025-1569-AC1
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AC1: 'Reply on RC1', Nadia Smith, 02 Oct 2025
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RC2: 'Comment on egusphere-2025-1569', Anonymous Referee #2, 04 Aug 2025
Review of "Mapping seasonal nitric acid (HNO3) patterns in the extratopics with nadir-viewing infrared sounders - a retrieval perspective", Smith et al., submitted to Atmos. Meas. Tech.
This manuscript explores information content and seasonal patterns of HNO3 in the upper Arctic atmosphere, as retrieved by several experimental CLIMCAPS versions applied to data collected from CrIS and ATMIS on the JPSS-1 satellite. While HNO3 is part of the operational CLIMCAPS retrieval, that portion of the retrieval has not been optimized as well as other trace gas species, and this paper outlines potential algorithm parameter updates for improved HNO3 retrieval. Due to the nearing end of life for the NASA Aura mission, other sources of stratospheric composition retrieval are needed and this study helps illustrate how IR sounders could partially fill this future information gap for HNO3.Overall, this study is relevant and appropriate for AMT. The subject is timely given the status of NASA Aura.
I find the overall message of the paper unclear, and several aspects need improved explanation. I recommend major revisions, as I think the unclear aspects are crucial to the message of the paper.
Major revisions:The manuscript title suggests a general discussion of nadir IR sounders applied to HNO3 retrieval, and in the initial sections the description is aimed toward comparing nadir IR sounders to a microwave limb sounder (specifically, Aura-MLS). However, in the main body of the paper, most of the comparison is done between FORLI and CLIMCAPS, two retrieval systems applied to nadir IR sounders (IASI and CrIS, respectively). Thus the title and abstract do not seem to match the bulk of the paper, which I find primarily focused on CLIMCAPS HNO3. I would suggest changing the title and abstract to reflect that message.
In much of the descriptions contrasting the FORLI and CLIMCAPS approach, it is stated that the FORLI system only retrieves a total column HNO3. However, the Ronsmans 2016 and 2018 clearly state that FORLI retrieves a profile for HNO3. It was simply a choice made in Ronsmans 2018 to post process the FORLI data to total columns for the analysis done in that paper. Thus, it seems misleading to characterize FORLI as is done in the manuscript; other researchers could simply choose to post process FORLI to extract partial columns in the stratosphere, which would be similar to the CLIMCAPS approach proposed here.
Throughout the manuscript, the dynamic regularization approach of CLIMCAPS is claimed to more effectively separate the stratospheric and tropospheric portions of the HNO3 profile. It is unclear why this is necessariily true, and it is simply stated in the manuscript without any direct analysis or evidence to demonstrate improved separation. I think a more detailed comparison of FORLI and CLIMCAPS retrieval output, compared to MLS, would be needed to demonstrate that CLIMCAPS is an improvement relative to FORLI in this way. Such a comparison is argued to be "beyond scope" for the present paper (line 104) - which is fine, but without such analysis I don't think the strat/troposphere separation is demonstrated.
Similarly, there are some statements made about the R3, R4 configurations being the most optimal. This argument is based purely on the DOFS and AK plots. I am not familiar with the CLIMCAPS dynamic regularization approach, but within the OE framework, there is a similar trend in the strength of regularization vs information content. Increasing the prior variance, or reducing measurement variance in OE, would have a similar effect as increasing Bmax in the CLIMCAPS system: the DFS would increase. The tradeoff that occurs is that we cannot increase the DFS too far as the retrieval can become unstable, and overly sensitive to measurement noise or forward model error; however, that could only be detected by examining the actual retrieved profiles, or via comparison to "truth" or validation data. In other words, the DFS would increase but the RMSE (compared to validation data) would also increase. In the present manuscript, there is no analysis of the quality of the retrieval in any way, all arguments seem to be made solely from the perspective of the information side (DFS). Thus it is unclear as to why R3 or R4 are the most optimal configurations.
To state this another way, we could view the main aim of the R2 - R5 tests is to "tune" the Bmax parameter. As Bmax increases, the DOFS increase - but there is no demonstration of any metric that is getting worse - only that "we argue that it is preferable for HNO3 DOFS to approximate 1.0 " (Line 402), but it is unclear WHY that is preferable.
Minor suggestions:One point that stood out was the relative difference between the prior (0.1 - 1 ppb, line 174) and the retrievals (on average - often > 14 ppb, from Figure 4) - this is more than an order of magnitude. It would be interesting to show Jacobians for these two cases: is the total optical depth of HNO3 small enough, that the Jacobian has a similar shape across this entire concentration range? Similarly, does the DOFS also change drastically across that concentration range?
In Table 1, the right column that lists the pressure grid seems to suggest the trapezoid functions were different in the R1, R2 experiments, versus the R3, R4, and R5. Or, maybe this listing two different ways to describe the 8 trapezoid functions. If it is the former (that there was a change in the trapezoids as part of the experimental versions), that needs more explanation. If the latter, I would suggest splitting out the pressure level information into a separate table. Also, a figure showing the trapezoids would be helpful.
Some minor technical correction:
In line 184 "... seven of the eight broad retrieval layers" - which one was omitted, and why?
Line 220: each term here is written as "\delta x_n x_n^T", is this a typo? I think it should always be "\delta x_n \delta x_n^T"?
Line 235: On the fact that O3 has such a large eigenvalue, I assume the CLIMCAPS retrieval uses a targeted retrieval spectral window on the O3 band (1000-1100 1/cm)? I think that must play a role in "accentuating" the eigenvalues.
Figure 3 & 4: the color maps here seem like they might be saturated, meaning many of the data points are beyond the max value in the color bar. (particularly for upper right panel of Fig 3, and the CLIMCAPS HNO3 maps in Figure 4). Can these be replotted with the max color bar values increased?
Figure 4: Is this the R4 CLIMCAPS test version? I don't see that listed anywhere - would help to state that directly in the figure caption.
What pressure range is sampled with the MLS retrievals?Citation: https://doi.org/10.5194/egusphere-2025-1569-RC2
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The authors present a progress report on the development of the CLIMCAPS HNO3 retrieval, building on their own preceding work. Background to these efforts is the foreseeable “limb gap” caused by the imminent discontinuation of current limb sounders operation. The aim of the development is to obtain a data product that represents conditions in the stratosphere, specifically temporal and spatial patterns and developments. This goal is attempted to be achieved through the appropriate selection of spectral ranges and dynamic regularization adapted to local atmospheric conditions.
The technical part of the paper, i.e., the investigation into the selection of a suitable parameter set, is carefully presented. However, there are some weaknesses in terms of clarity and structure in the general presentation and also in the presentation and critical discussion of the comparisons with MLS data. Some aspects deserve better or more detailed presentation and discussion.
My general comments are as follows:
The introduction should be better structured, possibly with the help of subsections, since some of the information it contains seems scattered and disjointed. Some later, and more generic, passages could be inserted here (example see below).
The use of the term averaging kernel (AK) is inconsistent and in some cases does not follow standard terminology. Specifically:
The curves in Figure 2 do not represent the AK profiles as stated, but rather the profiles of the diagonal values of the averaging kernel matrix (AKM). More accurately, the rows of this matrix would be called averaging kernels, as it is done, e.g., in Figure 4 of Smith & Barnet (2020). There, the profiles of the diagonal values in Figure 5 are referred to as “averaging kernel diagonal vectors.”
Figure 2: Instead of the profiles of the diagonal values of the AKM, it would be desirable and instructive to see the AKs (i.e., the rows of the matrix). By their shapes, one can get an impression of the altitude range which contributes to the HNO3 profile at a given pressure level.
p.7ff, l.202ff Please do not present the mathematical relations between the quantities in the running text, but rather use equations with numbering. This also facilitates references in the text. A more detailed description of the retrieval quantities and their mathematical relations, or at least references to where the respective relations are documented, would be beneficial. Perhaps a brief recapitulation of the retrieval approach and the related quantities in a separate section earlier in the text would be appropriate.
I have the most problems with the presentation of the comparison between VLIMCAPS-x and MLS HNO3 in Figure 4. Only for November and February do the spatial patterns seem to match well, while the same cannot be said for the other three months. There, the spatial patterns are clearly different. Based solely on this initial comparison, one would conclude that the desired/attempted reproduction of spatial gradients (see p.17, l.446) is not working reliably. A critical discussion of this comparison is virtually completely missing, and therefore has to be supplied in the revised manuscript. In particular, the question arises as to how future data users can recognize when the data adequately reflects stratospheric conditions and when it does not.
There are the following comments regarding the technical implementation of the comparison shown in Figure 4: First of all, it is not clear whether and how the MLS data belong to one specific fixed level or whether they have been averaged over several levels. In general, for the eye it is difficult to compare a map of values, such as those used for CLIMCAPS-x data, with the sampled MLS data. A purely visual comparison is easier to do when the CLIMCAPS-x HNO3 data is sampled, and shown, at the locations of the MLS measurements.
Of course, I understand that this presentation is mainly concerned with the development of the retrieval and not meant to anticipate a validation that is still to be carried out in future. On the other hand, the two HNO3 datasets are so different in terms of altitude resolution that I would strongly advise to transform the MLS data (regarded as "the truth"), using AKM and a priori of the CLIMCAPS-x retrieval, to that profile that would be seen by the nadir instrument/retrieval combination. I think there is no other reasonable way for a sound and robust comparison.
And finally, there are these specific points:
p.1, ll.1-2 Shouldn't the title already contain, or refer to, “stratospheric”?
p.3 l.83 "MLS-like" I think this can be stated more general (e.g. "limb-viewing instrument")
p.4 l.102 Strictly speaking, contrary to FORLI, MLS is no retrieval system.
p.5 l.147 Parentheses seem to be unnecessary.
Table 1 Please give the relation between lambdac and Bmax and their definitions before the quantities are used.
p.7, l.192 & p.8, l.215 How can a SNR be destabilized?
p.7, l.193 RTAERR=0 is not an underestimation? That means that destabilization cannot occur?
p.8, l.205 I think that i is meant to be an index for the height level. Is it counted from top to bottom, as implied by the order of the pressure values in Table 1, or from bottom to top?
p.9, l.246 Why do the DOFS represent the sum of all Rfac_i values? Is there a reference?
p.12, l.333 This would be a good place to refer to the (newly introduced) equation number from Section 3.
p.15, ll.421,422 mix-up of East & West?
p16,17 ll454-451 This would fit nicely into the introduction as part of a general motivation subsection.
In Figure 3, the individual maps would benefit from clearer visibility of the longitude and latitude grid.
Why is Greenland so clearly visible in Figure 3 for R3-R5, both in dof and in stddev(dof)? And (roughly) the Pyrenees and the Bering Sea?
p.19, l83 "that the resulting HNO3 product generally reflects real atmospheric variations" is not supported by Figure 4: It is not generally true, but only under certain conditions (see my general comments).