the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical Note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers: First steps toward assessing the feasibility of a global observation strategy
Daniel C. Anderson
Bryan N. Duncan
Julie M. Nicely
Junhua Liu
Sarah A. Strode
Melanie B. Follette-Cook
Abstract. Despite its importance in controlling the abundance of methane (CH4) and a myriad of other tropospheric species, the hydroxyl radical (OH) is poorly constrained due to its large spatial heterogeneity and the inability to measure tropospheric OH with satellites. Here, we present a methodology to infer tropospheric column OH (TCOH) in the tropics over the open oceans using a combination of a machine learning model, output from a simulation of the GEOS model, and satellite observations. Our overall goals are to assess the feasibility of our methodology, to identify potential limitations, and to suggest areas of improvement in the current observational network. The methodology reproduces the variability of TCOH from independent 3D model output and of observations from the Atmospheric Tomography mission (ATom). While the methodology also reproduces the magnitude of the 3D model validation set, the accuracy of the magnitude when applied to observations is uncertain because current observations are insufficient to fully evaluate the machine learning model. Despite large uncertainties in some of the satellite retrievals necessary to infer OH, particularly for NO2 and HCHO, current satellite observations are of sufficient quality to apply the machine learning methodology, resulting in an error comparable to that of in situ OH observations. Finally, the methodology is not limited to a specific suite of satellite retrievals. Comparison of TCOH determined from two sets of retrievals does show, however, that systematic biases in NO2, resulting both from retrieval algorithm and instrumental differences, lead to relative biases in the calculated TCOH. Further evaluation of NO2 retrievals in the remote atmosphere is needed to determine their accuracy. With slight modifications, a similar methodology could likely be expanded to the extra-tropics and over land, with the benefits of increasing our understanding of the atmospheric oxidation capacity and, for instance, informing understanding of recent CH4 trends.
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Daniel C. Anderson et al.
Status: closed
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RC1: 'Insufficient credit to other efforts', Anonymous Referee #1, 15 Jan 2023
With the advance of satellite instruments and machine learning techniques, atmospheric chemistry research is developing fast. This paper does an attempt to build a machine learning method for total (tropospheric) column OH, based on satellite observations. To this end, they train a GBRT model on results of a chemistry transport model and apply this model to satellite observations from mainly MOPITT, OMI, AIRS, but also location (i.e. solar intensity). The basic idea is that OH in the remote atmosphere in the tropics is driven mainly by the abundance of O3, H2O, NOx, CO, and hydrocarbons. In that respect, this provides an original contribution and shows some promises for the future.
The main problem I have with the paper is that is insufficiently credits and discusses other developments in the field. Reading the paper, I was wondering if the authors are aware of these developments at all? In a fast advancing field, reading, referencing, and discussing the work of others is of utmost importance. And the paper fails to do this. References to own work dominate. Below I outline how the paper should improve to become acceptable for publication.
- General methodology
In studying OH in the remote atmosphere, we have to rely on knowledge on atmospheric chemistry. In this paper, the authors use results of atmospheric chemistry simulations to train a machine learning algorithm. No criticism here. In the discussion, however, they “come up” with the idea to reduce the uncertainties between the 3D model results and satellite observations (without any references). Long-standing efforts have been made to “merge” satellite information and models in a process called data assimilation. First, there is the idea of chemical data assimilation, performed in e.g. the EU Copernicus services (e.g. https://atmosphere.copernicus.eu/sites/default/files/custom-uploads/3rd-joint-training/ACT2021_AInness.pdf)
Second, some authors worked their whole life on the subject of OH, satellites, and models, and do not receive even a citation in the manuscript (e.g. https://acp.copernicus.org/articles/20/931/2020/). I am not claiming that the work in this paper is useless. What I am saying is that the added value could be much more when proper credit and discussion is dedicated to related studies.
In the revised manuscript, the authors should catch up with existing work and should discuss that in the introduction and discussion. This should replace the current self-centered manuscript with restricted references to work of other groups.
- NO2 satellite data
More or less along the same lines. NO2 abundance in the remote tropics appears to be very important in determining TCOH. In section 5, the authors attempt to use alternative satellite products. In their evaluation of the results, they systematically refer to differences with their product as ‘biases’. Although they evaluated to some extend their TCOH product against Atom data, with relative OK result, this does not imply that their product is OK in May 2018 (the analyzed month) and that all the other results are biased. On top of that, they fail to refer to an extensive (EU-funded) program QA4eCV in which the NO2 products (e.g. of OMI) have been evaluated (e.g. https://amt.copernicus.org/articles/11/6651/2018/). This effort is so central to the discussion, that it really shameful that relevant literature is not cited.
I found the paper a pleasant read, presenting an interesting view for future exploration. In that respect, publication is possible, but the paper should discuss and give credit to internationally well-established efforts, which would require a major overhaul of the introduction and discussion.
Some other comments on the paper are in the annotated pdf file.
- AC1: 'Reply on RC1', Daniel Anderson, 02 Feb 2023
-
RC2: 'Comment on acp-2022-763', Anonymous Referee #2, 06 Feb 2023
This study introduces a new framework to infer tropospheric column OH, TCOH, over the tropical remote oceans based on a random forest regression and gradient boosted regression trees (GBRT) techniques using outputs from MERRA2 and several satellite observation data sets, including trace gaces and H2O. Satellite-based TCOH estimates were compared to the OH field of the original MERRA2 data, and an independent validation was then conducted against the ATom aircraft measurements. This methodology appears to be unique and innovative. The discussion section also contains interesting implications. Nevertheless, I have some concerns as described below. If the authors address them, this paper can be published. Note that another reviewer already posted many constructive comments to improve the manuscript, especially with regard to the relevance to other studies and the value of other satellite data. I have similar concerns and agree with most of the comments, so I do not duplicate the concerns in my comments.
1. Because of the localized non-linear chemistry, the use of aggregated satellite information at the monthly scale and 1x1 degree resolution would not correctly capture the OH distributions needed to predict detailed chemical mechanisms and then OH mean states and variability. Daily L2 data would clearly be a better input for better use of satellite information. Combining the L2 retrieval uncertainty information (and averaging kernels) provided for each pixel may also allow for improved use of satellite data products in ML. Because the current framework does not yet fully take the advantage of satellite products, the implications for future satellite requirements obtained may be limited or biased. In particular, the demonstrated large biases indicate the need for further refinement. Consistent with my concerns, Nicely et al. (2020) clearly stated that “Much future work is needed, though; observations must be incorporated to introduce a ground truth element to this analysis in a manner that either adjusts for or avoids disconnects between coarse versus local/instantaneous spatiotemporal scales and appropriately accounts for measurement uncertainty; an analysis of model output with much higher temporal frequency is needed to identify exactly where model differences in chemical mechanisms lie”. I see no reason to continue to use the aggregated L3/L4 data in this study. The increased computational costs should be managebale with a computationally efficient ML approach.
2. With regard to the uncertainty discussion and the predicted positive bias against the Atom measurements, it is essential to understand the relative importance of each satellite measurement used in GBRT, in order to provide an optimal framework for inferring TCOH and suggesting future satellite measurements meaningfully. This can be simply done by applying each satellite data set separately in GBRT, validated against Atom measurements, similar to observing system impact analysis widely used in data assimilation. Furthermore, whether removing the OMI NO2 from the calculation reduces the positive bias against Atom is a relevant question here (see also my comments below). Meanwhile, the random forest feature importance would provide additional information on the relative values. The current uncertainty section includes a related discussion and provides suggestions on the relative contribution of each measure, but is more indirect and limited. The suggested additional effort should also help to better understand the comparisons of OMI/AIRS/MOPITT and TROPOMI results. While the purpose of this paper is to present a methodology, understanding the role of each measurement cannot be ignored to ensure that the proposed methodology that combines multiple satellite data works properly and synergistically.
3. Although the variability of OH is relatively well reproduced, the large positive bias against AToM measurements remains a serious concern. Knowing the realistic OH magnitude can even be more important than variability for some important applications, such as chemical lifetime estimation. The highly biased estimates can have limited impacts on future applications. Several potential error sources are discussed in the manuscript, but they are not very convincing. The first point, “spanned 300 – 400 km in latitude” might not be the main reason, as the authors also discussed. The second point, “if a large fraction of the tropospheric column of one input was outside the range of the ATom profile, this would likely cause large errors in calculated TCOH.” can be verified by comparing the entire tropospheric column with that based on ATom sampling, using the OH field in MERRA2. This requires assuming that MERRA-2 provides realistic vertical profiles, while multi-model simulation data can provide that uncertainty information. As for the third point, “Recalculating the TCOH from ATom with NO2 from a box model constrained with NO observations”, its approach and validity are unclear in the manuscript. While the purpose of this paper is to present the methodology, the reasons for the large positive bias need to be further explored to clarify why the proposed framework still does not reproduce the observed OH values that are essential for chemical lifetime estimation.
4. The large discrepancy between the MERRA2 and satellite HCHO remains a concern. This could lead to significant degradation in OH predictions. This can be demonstrated based on the ML framework with and without HCHO data.
5. Future discussion is needed on satellite data products. In particular, satellite column measurements should have different vertical information due to different vertical sensitivities and profiles among measurements and variables. Meanwhile, OH variability can be largely independent between the lower and upper troposphere. This would complicate the prediction and interpretation of TCOH.
Citation: https://doi.org/10.5194/acp-2022-763-RC2 - AC2: 'Reply on RC2', Daniel Anderson, 07 Mar 2023
-
RC3: 'Comment on acp-2022-763', Anonymous Referee #3, 16 Feb 2023
The authors present a gradient-boosted regression tree (GBRT) machine-learning (ML) model for tropospheric OH columns trained on synthetic satellite observations from the NASA Global Modeling Initiative (GMI) chemical transport model driven by the MERRA-2 meteorological reanalysis. They evaluate the ML model with available in situ observations from the ATom field campaign and then apply the model to actual satellite observation inputs for the recent past. The paper is well-written and clear, and a rare example of a manuscript I have reviewed that I think needs no modifications to be suitable for publication. That being said, I do think the other two reviewers have provided some helpful and constructive comments that would add to the discussion in useful ways. However, the scientific guts of the paper are sound and interesting, and I recommend publication.
Citation: https://doi.org/10.5194/acp-2022-763-RC3 -
AC3: 'Reply on RC3', Daniel Anderson, 07 Mar 2023
We thank the reviewer for their time and response.
Citation: https://doi.org/10.5194/acp-2022-763-AC3
-
AC3: 'Reply on RC3', Daniel Anderson, 07 Mar 2023
Status: closed
-
RC1: 'Insufficient credit to other efforts', Anonymous Referee #1, 15 Jan 2023
With the advance of satellite instruments and machine learning techniques, atmospheric chemistry research is developing fast. This paper does an attempt to build a machine learning method for total (tropospheric) column OH, based on satellite observations. To this end, they train a GBRT model on results of a chemistry transport model and apply this model to satellite observations from mainly MOPITT, OMI, AIRS, but also location (i.e. solar intensity). The basic idea is that OH in the remote atmosphere in the tropics is driven mainly by the abundance of O3, H2O, NOx, CO, and hydrocarbons. In that respect, this provides an original contribution and shows some promises for the future.
The main problem I have with the paper is that is insufficiently credits and discusses other developments in the field. Reading the paper, I was wondering if the authors are aware of these developments at all? In a fast advancing field, reading, referencing, and discussing the work of others is of utmost importance. And the paper fails to do this. References to own work dominate. Below I outline how the paper should improve to become acceptable for publication.
- General methodology
In studying OH in the remote atmosphere, we have to rely on knowledge on atmospheric chemistry. In this paper, the authors use results of atmospheric chemistry simulations to train a machine learning algorithm. No criticism here. In the discussion, however, they “come up” with the idea to reduce the uncertainties between the 3D model results and satellite observations (without any references). Long-standing efforts have been made to “merge” satellite information and models in a process called data assimilation. First, there is the idea of chemical data assimilation, performed in e.g. the EU Copernicus services (e.g. https://atmosphere.copernicus.eu/sites/default/files/custom-uploads/3rd-joint-training/ACT2021_AInness.pdf)
Second, some authors worked their whole life on the subject of OH, satellites, and models, and do not receive even a citation in the manuscript (e.g. https://acp.copernicus.org/articles/20/931/2020/). I am not claiming that the work in this paper is useless. What I am saying is that the added value could be much more when proper credit and discussion is dedicated to related studies.
In the revised manuscript, the authors should catch up with existing work and should discuss that in the introduction and discussion. This should replace the current self-centered manuscript with restricted references to work of other groups.
- NO2 satellite data
More or less along the same lines. NO2 abundance in the remote tropics appears to be very important in determining TCOH. In section 5, the authors attempt to use alternative satellite products. In their evaluation of the results, they systematically refer to differences with their product as ‘biases’. Although they evaluated to some extend their TCOH product against Atom data, with relative OK result, this does not imply that their product is OK in May 2018 (the analyzed month) and that all the other results are biased. On top of that, they fail to refer to an extensive (EU-funded) program QA4eCV in which the NO2 products (e.g. of OMI) have been evaluated (e.g. https://amt.copernicus.org/articles/11/6651/2018/). This effort is so central to the discussion, that it really shameful that relevant literature is not cited.
I found the paper a pleasant read, presenting an interesting view for future exploration. In that respect, publication is possible, but the paper should discuss and give credit to internationally well-established efforts, which would require a major overhaul of the introduction and discussion.
Some other comments on the paper are in the annotated pdf file.
- AC1: 'Reply on RC1', Daniel Anderson, 02 Feb 2023
-
RC2: 'Comment on acp-2022-763', Anonymous Referee #2, 06 Feb 2023
This study introduces a new framework to infer tropospheric column OH, TCOH, over the tropical remote oceans based on a random forest regression and gradient boosted regression trees (GBRT) techniques using outputs from MERRA2 and several satellite observation data sets, including trace gaces and H2O. Satellite-based TCOH estimates were compared to the OH field of the original MERRA2 data, and an independent validation was then conducted against the ATom aircraft measurements. This methodology appears to be unique and innovative. The discussion section also contains interesting implications. Nevertheless, I have some concerns as described below. If the authors address them, this paper can be published. Note that another reviewer already posted many constructive comments to improve the manuscript, especially with regard to the relevance to other studies and the value of other satellite data. I have similar concerns and agree with most of the comments, so I do not duplicate the concerns in my comments.
1. Because of the localized non-linear chemistry, the use of aggregated satellite information at the monthly scale and 1x1 degree resolution would not correctly capture the OH distributions needed to predict detailed chemical mechanisms and then OH mean states and variability. Daily L2 data would clearly be a better input for better use of satellite information. Combining the L2 retrieval uncertainty information (and averaging kernels) provided for each pixel may also allow for improved use of satellite data products in ML. Because the current framework does not yet fully take the advantage of satellite products, the implications for future satellite requirements obtained may be limited or biased. In particular, the demonstrated large biases indicate the need for further refinement. Consistent with my concerns, Nicely et al. (2020) clearly stated that “Much future work is needed, though; observations must be incorporated to introduce a ground truth element to this analysis in a manner that either adjusts for or avoids disconnects between coarse versus local/instantaneous spatiotemporal scales and appropriately accounts for measurement uncertainty; an analysis of model output with much higher temporal frequency is needed to identify exactly where model differences in chemical mechanisms lie”. I see no reason to continue to use the aggregated L3/L4 data in this study. The increased computational costs should be managebale with a computationally efficient ML approach.
2. With regard to the uncertainty discussion and the predicted positive bias against the Atom measurements, it is essential to understand the relative importance of each satellite measurement used in GBRT, in order to provide an optimal framework for inferring TCOH and suggesting future satellite measurements meaningfully. This can be simply done by applying each satellite data set separately in GBRT, validated against Atom measurements, similar to observing system impact analysis widely used in data assimilation. Furthermore, whether removing the OMI NO2 from the calculation reduces the positive bias against Atom is a relevant question here (see also my comments below). Meanwhile, the random forest feature importance would provide additional information on the relative values. The current uncertainty section includes a related discussion and provides suggestions on the relative contribution of each measure, but is more indirect and limited. The suggested additional effort should also help to better understand the comparisons of OMI/AIRS/MOPITT and TROPOMI results. While the purpose of this paper is to present a methodology, understanding the role of each measurement cannot be ignored to ensure that the proposed methodology that combines multiple satellite data works properly and synergistically.
3. Although the variability of OH is relatively well reproduced, the large positive bias against AToM measurements remains a serious concern. Knowing the realistic OH magnitude can even be more important than variability for some important applications, such as chemical lifetime estimation. The highly biased estimates can have limited impacts on future applications. Several potential error sources are discussed in the manuscript, but they are not very convincing. The first point, “spanned 300 – 400 km in latitude” might not be the main reason, as the authors also discussed. The second point, “if a large fraction of the tropospheric column of one input was outside the range of the ATom profile, this would likely cause large errors in calculated TCOH.” can be verified by comparing the entire tropospheric column with that based on ATom sampling, using the OH field in MERRA2. This requires assuming that MERRA-2 provides realistic vertical profiles, while multi-model simulation data can provide that uncertainty information. As for the third point, “Recalculating the TCOH from ATom with NO2 from a box model constrained with NO observations”, its approach and validity are unclear in the manuscript. While the purpose of this paper is to present the methodology, the reasons for the large positive bias need to be further explored to clarify why the proposed framework still does not reproduce the observed OH values that are essential for chemical lifetime estimation.
4. The large discrepancy between the MERRA2 and satellite HCHO remains a concern. This could lead to significant degradation in OH predictions. This can be demonstrated based on the ML framework with and without HCHO data.
5. Future discussion is needed on satellite data products. In particular, satellite column measurements should have different vertical information due to different vertical sensitivities and profiles among measurements and variables. Meanwhile, OH variability can be largely independent between the lower and upper troposphere. This would complicate the prediction and interpretation of TCOH.
Citation: https://doi.org/10.5194/acp-2022-763-RC2 - AC2: 'Reply on RC2', Daniel Anderson, 07 Mar 2023
-
RC3: 'Comment on acp-2022-763', Anonymous Referee #3, 16 Feb 2023
The authors present a gradient-boosted regression tree (GBRT) machine-learning (ML) model for tropospheric OH columns trained on synthetic satellite observations from the NASA Global Modeling Initiative (GMI) chemical transport model driven by the MERRA-2 meteorological reanalysis. They evaluate the ML model with available in situ observations from the ATom field campaign and then apply the model to actual satellite observation inputs for the recent past. The paper is well-written and clear, and a rare example of a manuscript I have reviewed that I think needs no modifications to be suitable for publication. That being said, I do think the other two reviewers have provided some helpful and constructive comments that would add to the discussion in useful ways. However, the scientific guts of the paper are sound and interesting, and I recommend publication.
Citation: https://doi.org/10.5194/acp-2022-763-RC3 -
AC3: 'Reply on RC3', Daniel Anderson, 07 Mar 2023
We thank the reviewer for their time and response.
Citation: https://doi.org/10.5194/acp-2022-763-AC3
-
AC3: 'Reply on RC3', Daniel Anderson, 07 Mar 2023
Daniel C. Anderson et al.
Daniel C. Anderson et al.
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