the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Jun 2022
Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS-regional air quality ensemble
Abstract. The Sentinel-5P TROPOMI instrument, launched in October 2017, provides unique observations of atmospheric trace gases at a high resolution of about 5 km with near-daily global coverage, resolving individual sources like thermal power plants, industrial complexes, fires, medium-scale towns, roads and shipping routes. Even though Sentinel-5P (S5P) is a global mission, these datasets are especially well suited to test high-resolution regional-scale air quality (AQ) models and provide valuable input for emission inversion systems.
In Europe, the Copernicus Atmosphere Monitoring Service (CAMS) has implemented an operational regional AQ forecasting capability based on an ensemble of 7 up to 11 European models, available at a resolution of 0.1° × 0.1°. In this paper, we present comparisons between TROPOMI observations of nitrogen dioxide (NO2) and the CAMS AQ forecasts and analyses of NO2. We discuss the different ways of making these comparisons, and present quantitative results in the form of maps for individual days, summer and winter months as well as a time series for European sub-regions and cities between May 2018 to March 2021. The CAMS regional products generally capture the fine-scale daily and averaged features observed by TROPOMI in much detail. In summer, the quantitative comparison shows a close agreement between TROPOMI and the CAMS ensemble NO2 tropospheric columns, but in winter we find a significant discrepancy in the column amounts over much of Europe. The possible causes for these differences are discussed, focusing on the possible impact of retrieval and modelling errors. Apart from comparisons with the CAMS ensemble, we also present results for comparisons with the individual CAMS models for selected months.
Furthermore, we demonstrate the importance of the free tropospheric contribution to the estimation of the tropospheric column, and thus include profile information from the CAMS configuration of the ECMWF’s global integrated model above 3 km altitude in the comparisons. We also show that replacing the global 1° × 1° a priori information in the retrieval by the regional 0.1° × 0.1° resolution profiles of CAMS leads to significant changes in the TROPOMI retrieved tropospheric column, with typical increases at the emission hotspots up to 30 % and smaller increases or decreases elsewhere. As a spin-off, we present a new TROPOMI NO2 level-2 data product for Europe, based on the replacement of the original TM5-MP generated global a priori profile by the regional CAMS ensemble profile. This European NO2 product is compared with ground-based remote sensing measurements of 6 Pandora instruments of the Pandonia global network and 8 MAX-DOAS instruments. As compared to the standard S5P tropospheric NO2 column data, the overall bias of the new product is smaller owing to a reduction of the multiplicative bias, while compared to the CAMS tropospheric NO2 columns, dispersion and correlation parameters with respect to the standard data are superior.
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CEC1: 'Comment on egusphere-2022-365', Juan Antonio Añel, 15 Aug 2022
Dear authors,
Unfortunately, after reading the "Code and data availability" section of your manuscript, it has come to our attention that it does not comply with the policy of our journal. You state, "The bulk of the code used in this paper has been written in Python and is available upon request from the authors"; we do not accept embargoes of code or assets of the paper such as "upon request from the authors". Therefore, you must deposit the code you mention in one of the acceptable permanent repositories according to our policy (https://www.geoscientific-model-development.net/policies/code_and_data_policy.html). Moreover, you must reply as soon as possible to this comment with this information so that it is available for the Discussions stage. Also, include in a potential reviewed version of your manuscript the modified 'Code and Data Availability' section and the DOI for the code (and another DOI for the dataset if necessary).
Note that when publishing the code, you must include a license so that the code can be used by others. If you do not do it, the code continues to be your property and can not be used by a third party, despite any statement on being free to use. Therefore, when uploading the code to the repository, you could want to choose a free software/open-source (FLOSS) license. We recommend the GPLv3. You only need to include the file 'https://www.gnu.org/licenses/gpl-3.0.txt' as LICENSE.txt with your code. Also, you can choose other options that Zenodo provides: GPLv2, Apache License, MIT License, etc.
Juan A. Añel
Geosci. Model Dev. Exec. EditorCitation: https://doi.org/10.5194/egusphere-2022-365-CEC1 -
AC1: 'Reply on CEC1', John Douros, 23 Aug 2022
Dear Editor,
the relevant code has now been deposited on Zenodo with DOI 10.5281/zenodo.7016483
This information will also appear in the "Code and data availability" section in subsequent revised versions of the manuscript.Best regards,
John Douros, on behalf of the co-authorsCitation: https://doi.org/10.5194/egusphere-2022-365-AC1
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AC1: 'Reply on CEC1', John Douros, 23 Aug 2022
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RC1: 'Comment on egusphere-2022-365', Anonymous Referee #1, 26 Aug 2022
The authors compare the NO2 simulated by CAMS and observed by TROPOMI. The comparison shows better agreement in summer than that in winter. The finding about the vertical profile is very informative. The methodology and conclusions are sound. However, the authors seem to favor super long sentences, which makes it difficult for readers sometimes. I recommend rephrasing the long sentences thoroughly to make them more reader-friendly.
General comments:
- Section 3. The authors discussed a lot of details about the ensemble database. It is not very clear to me what has been used for comparison in this study, what will be upgraded in the near future, and what has been done by previous studies since all information was mixed. I recommend reorganizing this section.
- section 5.5. It will be useful to compare the differences between ensemble vs tropomi and ensemble vs individual models.
- I recommend adding a table listing all products used for comparison in the manuscript and adding a brief description of those products.
Specific comments:
- line 30. The grammar seems incorrect for the 2nd Please check.
- line 40. Line 50. Those sentences are too long to read.
- Line 70. I don’t see the reason to separate items 2 & 3 as two angles. Additionally, it is useful to point out that the vertical profiles are replaced in item 2. Otherwise, it is confusing for the readers why TM5 is mentioned here.
- Line 145. What is “compo”?
- Line 149. Is it operational now?
- Line 204. I suppose the R in S5P-R represents regional? I suggest putting the name after the description directly. It is easier for the reader to link the name with the product.
- Line 275. I suggest commenting on the potential reason why TROPOMI cannot detect ship lanes here.
- Line 281. What is 1st day forecasts?
- Line 328. What is “process modelling”?
- Figure 10. What is “spread”? Do you simply mean NO2 column densities here?
- Line 513. Do the authors claim a new methodology for satellite-model intercomparison here? What is the improvement compared to Eskes et al. (2003)?
Citation: https://doi.org/10.5194/egusphere-2022-365-RC1 -
AC2: 'Reply on RC1', John Douros, 24 Sep 2022
The authors thank the reviewer for reviewing the manuscript and for the useful suggestions for improving it. Here are some replies on the comments provided.
General comments.
1. The text of the revised manuscript has been improved to describe better what the CAMS ensemble product is, how it has evolved in time and which of the CAMS products were used in this study.
2. Elements of the comparison between ENSEMBLE and TROPOMI as well as between ENSEMBLE and the individual models are presented if figure 9 and table 3, as well as through figures 7 & 8 which include a representation of the model spread. By spread, we refer to the range of values provided by all individual models, i.e. the distance between the minimum and the maximum values. We consider however that a comparison between the CAMS ENSEMBLE and the individual models as such is beyond the scope of this work. Elements of such a comparison can be found (in interactive form) in:https://regional.atmosphere.copernicus.eu/evaluation.php?interactive=cdf
or in the form of quarterly reports in the "validation of CAMS regional services" reports in https://atmosphere.copernicus.eu/publications
3. Quantities (and their nomenclature) used in the comparisons in the paper are presented in figure 1 and their description can be found in section 4.1. The text of the revised manuscript has been improved to clarify how each product is used.
Specific comments.
All comments are appreciated and the text of the manuscript has been adapted to take them into account, including clarification of terms and sentence clarity.
As regards specific comment 7, the text in the manuscript actually mentions that "ship tracks are generally more prominent in the CAMS fields", not that they are completely absent in the TROPOMI fields, where they appear to be not as easily discernible. It is now documented that TROPOMI can in fact not only detect ship lanes but also individual ship tracks (Aristeidis K Georgoulias et al 2020 Environ. Res. Lett. 15 124037,http://dx.doi.org /10.1088/1748-9326/abc445) under certain favourable conditions i.e. stable, calm wind conditions with limited dispersion of ship plumes. It would however be beyond the scope of this work to investigate whether the prevailing conditions during the days shown in figures 4 and 5 were favourable in this respect. Potential reasons for the apparent difference between modelled and TROPOMI fields in this particular respect include the inherent noise in the TROPOMI fields and unrealistically low dispersion characteristics of the modelled plumes.
As regards specific comment 11, the reviewer is correct that the wording is somewhat misleading. Our work does not claim a new methodology as such, but a scheme (outlined in Figure 1) to guide interested parties as regards the recommended approaches towards satellite-model comparisons and to point to the correct steps in order to apply them. The text in the conclusions is adapted accordingly in the revised version of the manuscript.
Citation: https://doi.org/10.5194/egusphere-2022-365-AC2
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RC2: 'Comment on egusphere-2022-365', Anonymous Referee #2, 09 Sep 2022
John Douros and co-workers report on comparisons between TROPOMI NO2 column observations and results from the 7 air quality models which are currently operational within CAMS, providing forecasts and analyses over Europe at 0.1x0.1 degree resolution. The comparison shows a reasonable agreement during summer, but a substantial (factor of about two) model overestimation in winter. The use of high-resolution a priori profiles from the CAMS model ensemble (instead of the global 1x1 profiles) in the tropospheric NO2 column retrieval from TROPOMI results in higher retrieved columns over emission hotspots by about 30%. The authors further performed validation of the new satellite TROPOMI NO2 dataset using remote sensing column measurements and found that despite the overall overall bias reduction compared to the operational TROPOMI product, the new dataset is not able to close the large gap between observed and modelled NO2 columns in wintertime.
The manuscript does not include significant advances in modelling and has quite limited novelty. It uses pre-existing models and their outputs which are routinely available. However, the study proposes an alternative TROPOMI NO2 dataset over Europe based on high resolution model profiles which could be useful for the community. The method used for this derivation has been already applied in previous studies. I find the comparison of the
data with the output of the 7 models interesting, in spite of the fact that the reasons for the large mismatches are not investigated in the manuscript. The scientific approach and the methods are not new but they are valid and widely used in the literature. The results are discussed in a balanced way,
although in most instances the discussion is only qualitative. The writing is not always very precise. The language should therefore be improved in the revised version. Some references are incomplete or not defined, and additional references are needed. I could recommend publication after the following points are adequately addressed.
Comments:l.8: "7 up to 11 models". Not precise and misleading since the manuscript presents only results from 7 models.
l.13: remove "quantitative"
l.13: provide information (e.g. bias, correlation) about how close this agreement is
l.14: 'significant discrepancy', provide numbers
l.25-28: here again provide figures of the bias reduction and correlation obtained from this validation
l.34: 'values above the surface which are otherwise very scarce', replace by 'measurements at the surface which are very scarce'
l.36: read 'at kilometer scale'
l.39-44: This information does not seem relevant for this paper.
l.50: What are the CAMS systems? I would replace by 'CAMS makes'
l.51: Inness et al. 2019b is not defined
l.53: 'consistent' appears twice in the same line, replace by "to daily (re)analyses of concentrations and emissions which are consistent with..."
l.57: changes are not sharp for pollutants other than NO2, see https://doi.org/10.1029/2020GL091265, https://doi.org/10.3390/atmos12080946, DOI: 10.1126/sciadv.abg7670. I suggest to drop 'sharp' from the sentence and add some more references.
l.58-60: poor wording, Replace 'dedicated studies have been launched to study' by 'dedicated studies have been performed to investigate'
l.62: near daily basis
l.72: TROPOMI appears twice, replace 'measurement series' by 'measurement period', mention that TROPOMI NO2 is derived using the global TM5-MP profiles
l.73: mention clearly the horizontal resolution of the CAMS and the TM5 models
l.71-75: improve the clarity
l.81-82: remove 'very small', replace 'very large' by 'high'
l.84: provide references for your statement
l.85: remove 'the paper by' here and throughout the manuscript
l.85-93: check your references, for example Eskes et al., 2021a is missing
l.97-98: 'to force the stratosphere to be consistent with TROPOMI', weird statement
l.108-114: 'do not have a large impact', 'rather stable', 'considerable change', provide quantification
l. 121: MAXDOAS or MAX-DOAS, not both
l.120: mention that the Verhoelst et al. comparisons do not account for averaging kernels
l.124: reference missing
l.135: could you mention the impact of the new version v2.2 described in https://doi.org/10.5194/amt-15-2037-2022 ?
l.146-48: link not accessible (and too long)
l. 153: correct typo
l.164: 'have', not 'has'
Figure 1: Acronyms are not explained in the caption.
l.179: not necessary
Section 4.1: I find this section describes well-known methods in a confusing way.
l.225-26: avoid repetition of the word 'gridded' in the same line
Sections 5.1, 5.2, and 5.3 could be merged, all of them consist in briefly presenting the figures 5-8
l.325: I could not find Huijnen et al. 2010b in the list
Table 3, add additional columns with the ratio S5P and S5Pcams and CAMS-RG-A. Or add another table. This would help your discussion.
l.455: did you use 8 or 9 MAX-DOAS stations for validation? In the abstract you mention 8
Section 6.1, the discussion is again only qualitative. For example, 'CAMS is higher close to he suface': higher by how much?
Fig.15 inset statistics are too difficult to read
Fig.16: Is S5Pcams and S5P-RG the same thing?
l.500: 'this is not done here', improve the wording
l.564: '10% column enhancement', is this on average?Citation: https://doi.org/10.5194/egusphere-2022-365-RC2 -
AC3: 'Reply on RC2', John Douros, 24 Sep 2022
The authors thank the reviewer for reviewing the manuscript, for the insightful comments and for the useful suggestions for improving it. Here are some replies on the comments provided.
Our manuscript does not introduce a new methodology on the technical level, but instead proposes a scheme (outlined in Figure 1 of the manuscript) to guide interested parties as regards recommended approaches for comparing modelled and satellite/observed atmospheric gas columns (TROPOMI NO2 in our case), as well as point to the indicated methodology in order to do so. The wording used at certain places in the manuscript may have been ambiguous on this and has now been phrased differently to make it clearer. What we consider important in this work lies in the fact that the comparison is performed using a sizeable collection of European operational regional air quality models, which provides insights into the state-of-the-art atmospheric composition modelling, especially above the surface. The more novel part is indeed the introduction of the alternative TROPOMI NO2 dataset over Europe based on the CAMS ENSEMBLE analysis, which is arguably the best available near real time modelling regional atmospheric composition product available for the European continent.The revised manuscript contains improvements, including clarification of terms and wording to address most of the specific comments of the reviewer.Some answers to specific comments:l.57: changes are not sharp for pollutants other than NO2, see https://doi.org/10.1029/2020GL091265, https://doi.org/10.3390/atmos12080946, DOI: 10.1126/sciadv.abg7670. I suggest to drop 'sharp' from the sentence and add some more references.
The remark about the sharp decreases was based on the extensive review by Gkatzelis et al (2021) which covers a wide range of pollutants and relies on various kinds of observations. It is true however that these changes are not always visible in satellite retrievals.
l.84: provide references for your statement
The ability of the TROPOMI instrument to identify power plants, highways and ships is documented in various works (below), to be added in the bibliography.
Daniel L. Goldberg, Zifeng Lu, David G. Streets, Benjamin de Foy, Debora Griffin, Chris A. McLinden, Lok N. Lamsal, Nickolay A. Krotkov, and Henk Eskes
Environmental Science & Technology 2019 53 (21), 12594-12601, DOI: 10.1021/acs.est.9b04488Miyazaki, K., Bowman, K., Sekiya, T., Jiang, Z., Chen, X., Eskes, H., Ru, M., Zhang, Y., and Shindell, D.: Air quality response in China linked to the 2019 novel coronavirus (COVID-19) lockdown, Geophys. Res. Lett., 47, e2020GL089252, https://doi.org/10.1029/2020GL089252, 2020.
F. Liu, A. Page, S. A. Strode, Y. Yoshida, S. Choi, B. Zheng, L. N. Lamsal, C. Li, N. A. Krotkov, H. Eskes, R. van der A, P. Veefkind, P. F. Levelt, O. P. Hauser, J. Joiner, Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19. Sci. Adv.6, eabc2992 (2020)Aristeidis K Georgoulias et al 2020 Environ. Res. Lett. 15 124037, http://dx.doi.org/10.1088/1748-9326/abc445
l.73: mention clearly the horizontal resolution of the CAMS and the TM5 modelsThis information is contained in table 2.
l.108-114: 'do not have a large impact', 'rather stable', 'considerable change', provide quantification
More details on the quantitative differences between the TROPOMI products produced with the successive versions of the level-2 processor can be found in the next paragraphs of the manuscript (lines 118-135) but also in (mentioned as van Geffen et al, 2021b in the manuscript):
van Geffen, J., Eskes, H., Compernolle, S., Pinardi, G., Verhoelst, T., Lambert, J.-C., Sneep, M., ter Linden, M., Ludewig, A., Boersma, K. F., and Veefkind, J. P.: Sentinel-5P TROPOMI NO2 retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data, Atmos. Meas. Tech., 15, 2037–2060, https://doi.org/10.5194/amt-15-2037-2022, 2022.
as well as in:
http://www.tropomi.eu/data-products/nitrogen-dioxide/
l.135: could you mention the impact of the new version v2.2 described in https://doi.org/10.5194/amt-15-2037-2022 ?
Indeed, van Geffen et al (2022) argue that "on average the NO2-v2.2 data have tropospheric VCDs that are between 10 % and 40 % larger than the v1.x data". This is explicitly mentioned in the revised version of the manuscript.
Section 4.1: I find this section describes well-known methods in a confusing way.
The main purpose of this section is to introduce the acronyms/naming conventions used in the manuscript. For that reason, it was necessary to resort to elements of the methodology introduced by Eskes et al. (2003). The section is restructured in the revised manuscript.
Table 3, add additional columns with the ratio S5P and S5Pcams and CAMS-RG-A. Or add another table. This would help your discussion.
Thanks for the suggestion, an additional column with the S5P to CAMS-RG-A ratio is included in table 3.
Section 6.1, the discussion is again only qualitative. For example, 'CAMS is higher close to he suface': higher by how much?
The reviewer is correct that there are occasions in the text were qualitative remarks should be accompanied by include quantitative information. Wherever possible, these are addressed in the revised manuscript. Discussion of figure 14 (mean averaging kernels and NO2 profiles by the various models) however, is qualitative as it is basically used as a means to explain the preceding figures/results, some of which may seem counterintuitive at first glance, e.g. how complementing the free tropospheric part with data from the global model can lead to lower columns (S5P-RG being generally lower to S5P-R).
l.564: '10% column enhancement', is this on average?
This is not accurate, the text should have read "a column enhancement of at least 10%". This is with the exception of Helsinki which exhibits quite low S5P-RG/S5P ratios.
Citation: https://doi.org/10.5194/egusphere-2022-365-AC3
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AC3: 'Reply on RC2', John Douros, 24 Sep 2022
Interactive discussion
Status: closed
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CEC1: 'Comment on egusphere-2022-365', Juan Antonio Añel, 15 Aug 2022
Dear authors,
Unfortunately, after reading the "Code and data availability" section of your manuscript, it has come to our attention that it does not comply with the policy of our journal. You state, "The bulk of the code used in this paper has been written in Python and is available upon request from the authors"; we do not accept embargoes of code or assets of the paper such as "upon request from the authors". Therefore, you must deposit the code you mention in one of the acceptable permanent repositories according to our policy (https://www.geoscientific-model-development.net/policies/code_and_data_policy.html). Moreover, you must reply as soon as possible to this comment with this information so that it is available for the Discussions stage. Also, include in a potential reviewed version of your manuscript the modified 'Code and Data Availability' section and the DOI for the code (and another DOI for the dataset if necessary).
Note that when publishing the code, you must include a license so that the code can be used by others. If you do not do it, the code continues to be your property and can not be used by a third party, despite any statement on being free to use. Therefore, when uploading the code to the repository, you could want to choose a free software/open-source (FLOSS) license. We recommend the GPLv3. You only need to include the file 'https://www.gnu.org/licenses/gpl-3.0.txt' as LICENSE.txt with your code. Also, you can choose other options that Zenodo provides: GPLv2, Apache License, MIT License, etc.
Juan A. Añel
Geosci. Model Dev. Exec. EditorCitation: https://doi.org/10.5194/egusphere-2022-365-CEC1 -
AC1: 'Reply on CEC1', John Douros, 23 Aug 2022
Dear Editor,
the relevant code has now been deposited on Zenodo with DOI 10.5281/zenodo.7016483
This information will also appear in the "Code and data availability" section in subsequent revised versions of the manuscript.Best regards,
John Douros, on behalf of the co-authorsCitation: https://doi.org/10.5194/egusphere-2022-365-AC1
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AC1: 'Reply on CEC1', John Douros, 23 Aug 2022
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RC1: 'Comment on egusphere-2022-365', Anonymous Referee #1, 26 Aug 2022
The authors compare the NO2 simulated by CAMS and observed by TROPOMI. The comparison shows better agreement in summer than that in winter. The finding about the vertical profile is very informative. The methodology and conclusions are sound. However, the authors seem to favor super long sentences, which makes it difficult for readers sometimes. I recommend rephrasing the long sentences thoroughly to make them more reader-friendly.
General comments:
- Section 3. The authors discussed a lot of details about the ensemble database. It is not very clear to me what has been used for comparison in this study, what will be upgraded in the near future, and what has been done by previous studies since all information was mixed. I recommend reorganizing this section.
- section 5.5. It will be useful to compare the differences between ensemble vs tropomi and ensemble vs individual models.
- I recommend adding a table listing all products used for comparison in the manuscript and adding a brief description of those products.
Specific comments:
- line 30. The grammar seems incorrect for the 2nd Please check.
- line 40. Line 50. Those sentences are too long to read.
- Line 70. I don’t see the reason to separate items 2 & 3 as two angles. Additionally, it is useful to point out that the vertical profiles are replaced in item 2. Otherwise, it is confusing for the readers why TM5 is mentioned here.
- Line 145. What is “compo”?
- Line 149. Is it operational now?
- Line 204. I suppose the R in S5P-R represents regional? I suggest putting the name after the description directly. It is easier for the reader to link the name with the product.
- Line 275. I suggest commenting on the potential reason why TROPOMI cannot detect ship lanes here.
- Line 281. What is 1st day forecasts?
- Line 328. What is “process modelling”?
- Figure 10. What is “spread”? Do you simply mean NO2 column densities here?
- Line 513. Do the authors claim a new methodology for satellite-model intercomparison here? What is the improvement compared to Eskes et al. (2003)?
Citation: https://doi.org/10.5194/egusphere-2022-365-RC1 -
AC2: 'Reply on RC1', John Douros, 24 Sep 2022
The authors thank the reviewer for reviewing the manuscript and for the useful suggestions for improving it. Here are some replies on the comments provided.
General comments.
1. The text of the revised manuscript has been improved to describe better what the CAMS ensemble product is, how it has evolved in time and which of the CAMS products were used in this study.
2. Elements of the comparison between ENSEMBLE and TROPOMI as well as between ENSEMBLE and the individual models are presented if figure 9 and table 3, as well as through figures 7 & 8 which include a representation of the model spread. By spread, we refer to the range of values provided by all individual models, i.e. the distance between the minimum and the maximum values. We consider however that a comparison between the CAMS ENSEMBLE and the individual models as such is beyond the scope of this work. Elements of such a comparison can be found (in interactive form) in:https://regional.atmosphere.copernicus.eu/evaluation.php?interactive=cdf
or in the form of quarterly reports in the "validation of CAMS regional services" reports in https://atmosphere.copernicus.eu/publications
3. Quantities (and their nomenclature) used in the comparisons in the paper are presented in figure 1 and their description can be found in section 4.1. The text of the revised manuscript has been improved to clarify how each product is used.
Specific comments.
All comments are appreciated and the text of the manuscript has been adapted to take them into account, including clarification of terms and sentence clarity.
As regards specific comment 7, the text in the manuscript actually mentions that "ship tracks are generally more prominent in the CAMS fields", not that they are completely absent in the TROPOMI fields, where they appear to be not as easily discernible. It is now documented that TROPOMI can in fact not only detect ship lanes but also individual ship tracks (Aristeidis K Georgoulias et al 2020 Environ. Res. Lett. 15 124037,http://dx.doi.org /10.1088/1748-9326/abc445) under certain favourable conditions i.e. stable, calm wind conditions with limited dispersion of ship plumes. It would however be beyond the scope of this work to investigate whether the prevailing conditions during the days shown in figures 4 and 5 were favourable in this respect. Potential reasons for the apparent difference between modelled and TROPOMI fields in this particular respect include the inherent noise in the TROPOMI fields and unrealistically low dispersion characteristics of the modelled plumes.
As regards specific comment 11, the reviewer is correct that the wording is somewhat misleading. Our work does not claim a new methodology as such, but a scheme (outlined in Figure 1) to guide interested parties as regards the recommended approaches towards satellite-model comparisons and to point to the correct steps in order to apply them. The text in the conclusions is adapted accordingly in the revised version of the manuscript.
Citation: https://doi.org/10.5194/egusphere-2022-365-AC2
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RC2: 'Comment on egusphere-2022-365', Anonymous Referee #2, 09 Sep 2022
John Douros and co-workers report on comparisons between TROPOMI NO2 column observations and results from the 7 air quality models which are currently operational within CAMS, providing forecasts and analyses over Europe at 0.1x0.1 degree resolution. The comparison shows a reasonable agreement during summer, but a substantial (factor of about two) model overestimation in winter. The use of high-resolution a priori profiles from the CAMS model ensemble (instead of the global 1x1 profiles) in the tropospheric NO2 column retrieval from TROPOMI results in higher retrieved columns over emission hotspots by about 30%. The authors further performed validation of the new satellite TROPOMI NO2 dataset using remote sensing column measurements and found that despite the overall overall bias reduction compared to the operational TROPOMI product, the new dataset is not able to close the large gap between observed and modelled NO2 columns in wintertime.
The manuscript does not include significant advances in modelling and has quite limited novelty. It uses pre-existing models and their outputs which are routinely available. However, the study proposes an alternative TROPOMI NO2 dataset over Europe based on high resolution model profiles which could be useful for the community. The method used for this derivation has been already applied in previous studies. I find the comparison of the
data with the output of the 7 models interesting, in spite of the fact that the reasons for the large mismatches are not investigated in the manuscript. The scientific approach and the methods are not new but they are valid and widely used in the literature. The results are discussed in a balanced way,
although in most instances the discussion is only qualitative. The writing is not always very precise. The language should therefore be improved in the revised version. Some references are incomplete or not defined, and additional references are needed. I could recommend publication after the following points are adequately addressed.
Comments:l.8: "7 up to 11 models". Not precise and misleading since the manuscript presents only results from 7 models.
l.13: remove "quantitative"
l.13: provide information (e.g. bias, correlation) about how close this agreement is
l.14: 'significant discrepancy', provide numbers
l.25-28: here again provide figures of the bias reduction and correlation obtained from this validation
l.34: 'values above the surface which are otherwise very scarce', replace by 'measurements at the surface which are very scarce'
l.36: read 'at kilometer scale'
l.39-44: This information does not seem relevant for this paper.
l.50: What are the CAMS systems? I would replace by 'CAMS makes'
l.51: Inness et al. 2019b is not defined
l.53: 'consistent' appears twice in the same line, replace by "to daily (re)analyses of concentrations and emissions which are consistent with..."
l.57: changes are not sharp for pollutants other than NO2, see https://doi.org/10.1029/2020GL091265, https://doi.org/10.3390/atmos12080946, DOI: 10.1126/sciadv.abg7670. I suggest to drop 'sharp' from the sentence and add some more references.
l.58-60: poor wording, Replace 'dedicated studies have been launched to study' by 'dedicated studies have been performed to investigate'
l.62: near daily basis
l.72: TROPOMI appears twice, replace 'measurement series' by 'measurement period', mention that TROPOMI NO2 is derived using the global TM5-MP profiles
l.73: mention clearly the horizontal resolution of the CAMS and the TM5 models
l.71-75: improve the clarity
l.81-82: remove 'very small', replace 'very large' by 'high'
l.84: provide references for your statement
l.85: remove 'the paper by' here and throughout the manuscript
l.85-93: check your references, for example Eskes et al., 2021a is missing
l.97-98: 'to force the stratosphere to be consistent with TROPOMI', weird statement
l.108-114: 'do not have a large impact', 'rather stable', 'considerable change', provide quantification
l. 121: MAXDOAS or MAX-DOAS, not both
l.120: mention that the Verhoelst et al. comparisons do not account for averaging kernels
l.124: reference missing
l.135: could you mention the impact of the new version v2.2 described in https://doi.org/10.5194/amt-15-2037-2022 ?
l.146-48: link not accessible (and too long)
l. 153: correct typo
l.164: 'have', not 'has'
Figure 1: Acronyms are not explained in the caption.
l.179: not necessary
Section 4.1: I find this section describes well-known methods in a confusing way.
l.225-26: avoid repetition of the word 'gridded' in the same line
Sections 5.1, 5.2, and 5.3 could be merged, all of them consist in briefly presenting the figures 5-8
l.325: I could not find Huijnen et al. 2010b in the list
Table 3, add additional columns with the ratio S5P and S5Pcams and CAMS-RG-A. Or add another table. This would help your discussion.
l.455: did you use 8 or 9 MAX-DOAS stations for validation? In the abstract you mention 8
Section 6.1, the discussion is again only qualitative. For example, 'CAMS is higher close to he suface': higher by how much?
Fig.15 inset statistics are too difficult to read
Fig.16: Is S5Pcams and S5P-RG the same thing?
l.500: 'this is not done here', improve the wording
l.564: '10% column enhancement', is this on average?Citation: https://doi.org/10.5194/egusphere-2022-365-RC2 -
AC3: 'Reply on RC2', John Douros, 24 Sep 2022
The authors thank the reviewer for reviewing the manuscript, for the insightful comments and for the useful suggestions for improving it. Here are some replies on the comments provided.
Our manuscript does not introduce a new methodology on the technical level, but instead proposes a scheme (outlined in Figure 1 of the manuscript) to guide interested parties as regards recommended approaches for comparing modelled and satellite/observed atmospheric gas columns (TROPOMI NO2 in our case), as well as point to the indicated methodology in order to do so. The wording used at certain places in the manuscript may have been ambiguous on this and has now been phrased differently to make it clearer. What we consider important in this work lies in the fact that the comparison is performed using a sizeable collection of European operational regional air quality models, which provides insights into the state-of-the-art atmospheric composition modelling, especially above the surface. The more novel part is indeed the introduction of the alternative TROPOMI NO2 dataset over Europe based on the CAMS ENSEMBLE analysis, which is arguably the best available near real time modelling regional atmospheric composition product available for the European continent.The revised manuscript contains improvements, including clarification of terms and wording to address most of the specific comments of the reviewer.Some answers to specific comments:l.57: changes are not sharp for pollutants other than NO2, see https://doi.org/10.1029/2020GL091265, https://doi.org/10.3390/atmos12080946, DOI: 10.1126/sciadv.abg7670. I suggest to drop 'sharp' from the sentence and add some more references.
The remark about the sharp decreases was based on the extensive review by Gkatzelis et al (2021) which covers a wide range of pollutants and relies on various kinds of observations. It is true however that these changes are not always visible in satellite retrievals.
l.84: provide references for your statement
The ability of the TROPOMI instrument to identify power plants, highways and ships is documented in various works (below), to be added in the bibliography.
Daniel L. Goldberg, Zifeng Lu, David G. Streets, Benjamin de Foy, Debora Griffin, Chris A. McLinden, Lok N. Lamsal, Nickolay A. Krotkov, and Henk Eskes
Environmental Science & Technology 2019 53 (21), 12594-12601, DOI: 10.1021/acs.est.9b04488Miyazaki, K., Bowman, K., Sekiya, T., Jiang, Z., Chen, X., Eskes, H., Ru, M., Zhang, Y., and Shindell, D.: Air quality response in China linked to the 2019 novel coronavirus (COVID-19) lockdown, Geophys. Res. Lett., 47, e2020GL089252, https://doi.org/10.1029/2020GL089252, 2020.
F. Liu, A. Page, S. A. Strode, Y. Yoshida, S. Choi, B. Zheng, L. N. Lamsal, C. Li, N. A. Krotkov, H. Eskes, R. van der A, P. Veefkind, P. F. Levelt, O. P. Hauser, J. Joiner, Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19. Sci. Adv.6, eabc2992 (2020)Aristeidis K Georgoulias et al 2020 Environ. Res. Lett. 15 124037, http://dx.doi.org/10.1088/1748-9326/abc445
l.73: mention clearly the horizontal resolution of the CAMS and the TM5 modelsThis information is contained in table 2.
l.108-114: 'do not have a large impact', 'rather stable', 'considerable change', provide quantification
More details on the quantitative differences between the TROPOMI products produced with the successive versions of the level-2 processor can be found in the next paragraphs of the manuscript (lines 118-135) but also in (mentioned as van Geffen et al, 2021b in the manuscript):
van Geffen, J., Eskes, H., Compernolle, S., Pinardi, G., Verhoelst, T., Lambert, J.-C., Sneep, M., ter Linden, M., Ludewig, A., Boersma, K. F., and Veefkind, J. P.: Sentinel-5P TROPOMI NO2 retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data, Atmos. Meas. Tech., 15, 2037–2060, https://doi.org/10.5194/amt-15-2037-2022, 2022.
as well as in:
http://www.tropomi.eu/data-products/nitrogen-dioxide/
l.135: could you mention the impact of the new version v2.2 described in https://doi.org/10.5194/amt-15-2037-2022 ?
Indeed, van Geffen et al (2022) argue that "on average the NO2-v2.2 data have tropospheric VCDs that are between 10 % and 40 % larger than the v1.x data". This is explicitly mentioned in the revised version of the manuscript.
Section 4.1: I find this section describes well-known methods in a confusing way.
The main purpose of this section is to introduce the acronyms/naming conventions used in the manuscript. For that reason, it was necessary to resort to elements of the methodology introduced by Eskes et al. (2003). The section is restructured in the revised manuscript.
Table 3, add additional columns with the ratio S5P and S5Pcams and CAMS-RG-A. Or add another table. This would help your discussion.
Thanks for the suggestion, an additional column with the S5P to CAMS-RG-A ratio is included in table 3.
Section 6.1, the discussion is again only qualitative. For example, 'CAMS is higher close to he suface': higher by how much?
The reviewer is correct that there are occasions in the text were qualitative remarks should be accompanied by include quantitative information. Wherever possible, these are addressed in the revised manuscript. Discussion of figure 14 (mean averaging kernels and NO2 profiles by the various models) however, is qualitative as it is basically used as a means to explain the preceding figures/results, some of which may seem counterintuitive at first glance, e.g. how complementing the free tropospheric part with data from the global model can lead to lower columns (S5P-RG being generally lower to S5P-R).
l.564: '10% column enhancement', is this on average?
This is not accurate, the text should have read "a column enhancement of at least 10%". This is with the exception of Helsinki which exhibits quite low S5P-RG/S5P ratios.
Citation: https://doi.org/10.5194/egusphere-2022-365-AC3
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AC3: 'Reply on RC2', John Douros, 24 Sep 2022
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7 citations as recorded by crossref.
- Horizontal distribution of tropospheric NO2 and aerosols derived by dual-scan multi-wavelength multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements in Uccle, Belgium E. Dimitropoulou et al. 10.5194/amt-15-4503-2022
- Sentinel-5P TROPOMI NO<sub>2</sub> retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data J. van Geffen et al. 10.5194/amt-15-2037-2022
- Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula H. Petetin et al. 10.5194/acp-23-3905-2023
- Evaluating NOx emissions and their effect on O3 production in Texas using TROPOMI NO2 and HCHO D. Goldberg et al. 10.5194/acp-22-10875-2022
- Estimating surface-level nitrogen dioxide concentrations from Sentinel-5P/TROPOMI observations in Finland H. Virta et al. 10.1016/j.atmosenv.2023.119989
- Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium C. Poraicu et al. 10.5194/gmd-16-479-2023
- Estimation of biomass burning emission of NO2 and CO from 2019–2020 Australia fires based on satellite observations N. Wan et al. 10.5194/acp-23-711-2023
Henk Eskes
Jos van Geffen
K. Folkert Boersma
Steven Compernolle
Gaia Pinardi
Anne-Marlene Blechschmidt
Vincent-Henri Peuch
Augustin Colette
Pepijn Veefkind
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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