the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Aug 2022
Estimation of CH4 emission based on advanced 4D-LETKF assimilation system
Abstract. Methane (CH4) is the second major greenhouse gas after carbon dioxide (CO2) which is substantially increased during last decades in the atmosphere, raising serious sustainability and climate change issues. Here, we develop a data assimilation system for in situ and column averaged concentrations using Local ensemble transform Kalman filter (LETKF) to estimate surface emissions of CH4. The data assimilation performance is tested and optimized based on idealized settings using Observation System Simulation Experiments (OSSEs) where a known surface emission distribution (the truth) is retrieved from synthetic observations. We tested three covariance inflation methods to avoid covariance underestimation in the emission estimates, namely; fixed multiplicative (FM), relaxation to prior spread (RTPS) and adaptive multiplicative. First, we assimilate the synthetic observations at every grid point at the surface level. In such a case of dense observational network, the normalized Root Mean Square Error (RMSE) in the analyses over global land regions are smaller by 10–15 % in case of RTPS covariance inflation method compared to FM. We have shown that integrated estimated flux seasonal cycles over 15 regions using RTPS inflation are in reasonable agreement between true and estimated flux with 0.04 global absolute normalized annual mean bias. We have then assimilated the column averaged CH4 concentration by sampling the model simulations at GOSAT observation locations and time for another OSSE experiment. Similar to the case of dense observational network, RTPS covariance inflation method performs better than FM for GOSAT synthetic observation in terms of normalized RMSE (2–3 %) and integrated flux estimation comparison with the true flux. The annual mean averaged normalized RMSE (normalized absolute mean bias) in LETKF CH4 flux estimation in case of RTPS and FM covariance inflation is found to be 0.59 (0.18) and 0.61 (0.23) respectively. The chi-square test performed for GOSAT synthetic observations assimilation suggests high underestimation of background error covariance in both RTPS and FM covariance inflation methods, however, the underestimation is much high (>100 % always) for FM compared to RTPS covariance inflation method.
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CEC1: 'Comment on egusphere-2022-719', Juan Antonio Añel, 24 Aug 2022
Dear authors,Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlYou have archived the LETKF code on GitHub. However, GitHub is not a suitable repository. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo. Also, the GitHub repository for LETKF does not include a license. If you do not include a license, despite what you state, the code can not be used by others, as it continues to be your property. Therefore, when uploading the model's code to the new 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.About the MIROC model. We understand the reasons that preclude its publication. However, ideally, we should have evidence of proper internal storage by their developers. Therefore, please, provide additional detail on where and how the code is stored, and if possible, ask the developers to share a DOI for the version you use in this work.Therefore, please, publish your code in one of the appropriate repositories, and reply to this comment with the relevant information (links and DOIs) as soon as possible, as it should be available for the Discussions stage. Also, you must include the modified 'Code and Data Availability' section in a potential reviewed version of your manuscript.Please, be aware that failing to comply promptly with this request could result in rejecting your manuscript for publication.Regards,Juan A. AñelGeosci. Model Dev. Exec. EditorCitation: https://doi.org/
10.5194/egusphere-2022-719-CEC1 -
AC1: 'Reply on CEC1', Jagat Bisht, 14 Sep 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-719/egusphere-2022-719-AC1-supplement.pdf
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CEC2: 'Reply on AC1', Juan Antonio Añel, 14 Sep 2022
Dear authors,
Unfortunately, your reply does not solve the problems with your manuscript. First, the repositories in Zenodo are not accessible; requesting permission to access them is necessary. This is against our policy. Indeed, I would ask you to read it carefully. The repositories must be public, and removal of them must be impossible. Also, your reply states that you have uploaded "data assimilation code". This is not enough, you must publish the complete model code.Finally, I want to point out that I made my comment on your paper three weeks ago, asking for a prompt reply. This is much more time than I expect to solve an issue so simple as uploading code to a repository. Indeed, your manuscript should have never been accepted for Discussions or sent out to review by the Topical Editor with these shortcomings; however, we are trying to solve it now.I must make clear again that if you do not store the code of the models in a public repository in a prompt and quick manner and reply to this comment with the information about the repositories, we will proceed to reject your manuscript.Best regards,
Juan A. Añel
Geosci. Model Dev. Executive Editor.
Citation: https://doi.org/10.5194/egusphere-2022-719-CEC2 -
AC2: 'Reply on CEC2', Jagat Bisht, 28 Sep 2022
Dear Honourable Chief Editor,First for the delay in my reply: I was on vacation immediately after submission of the manuscript. It is my first visit to my home in India since the Covid-19 outbreak. I have tried to work on the code submission to Zenodo as the first thing after returning. We appreciate your immediate attention to our submission. Apologies for slower responses on our side.
Secondly, I have only developed the LETKF code for this work as a part of my post-doctoral research. This is a stand-alone code and any ACTM can be plugged into this code to perform CH4 data assimilation. We are also keeping the CH4 model simulation module in MIROC in our Zenodo account (https://zenodo.org/record/7079139; this contains only the CH4 simulation module not whole MIROC package), so that all components of CH4 simulations can be publicly available. The MIROC version 4 is coupled with our CH4 simulation module to perform simulations. The whole MIROC package couldn't be publicly or with restrictions made available due to copyright policy of the MIROC community as we mentioned in our manuscript.
Thus we are able to obey the GMD code and data policy:
Core Principle #2: “…. In particular, authors must make every effort to publish any code whose development is described in the manuscript.”
Following this we are happy to publicly archive the LETKF code (https://zenodo.org/record/7079167). Please note that the restricted access is now removed. In addition, all the scripts for running the assimilation code, input files and output file are also made available (https://zenodo.org/record/7098323) on Zenodo. We also provided a LETKF data assimilation user guide (LETKF_DA_GUIDE) in our zenodo account which describes how to prepare and run LETKF data assimilation experiments mentioned in our manuscript.We also discussed with our colleagues who are using MIROC and associated research and development codes, and checked their recent publications. We were encouraged to submit our manuscript because such a code sharing policy in the most recent papers has been accepted. An example from the year 2022 is given.https://gmd.copernicus.org/articles/15/5627/2022/gmd-15-5627-2022.htmlCitation: https://doi.org/10.5194/egusphere-2022-719-AC2 -
CEC3: 'Reply on AC2', Juan Antonio Añel, 28 Sep 2022
Dear authors,
I have checked the Zenodo repositories, and I have not found the LETKF_DA_GUIDE that you mention. Could you have missed it? If not, please, could you be more explicit about where we can find it?
Also, some of your data files are in binary format (.bin). Can you clarify what kind of software is necessary to read them?
Many thanks,
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2022-719-CEC3 -
EC1: 'Reply on CEC3', Shu-Chih Yang, 28 Sep 2022
Dear Authors,
Is "https://zenodo.org/record/7079167" the LETKF_DA_GUIDE? Please specify what kind of software you used to compile the LETKF codes and MIROC model. It is not clear enough to provide the Makefile only.
Thanks,
Shu-Chih
Citation: https://doi.org/10.5194/egusphere-2022-719-EC1 -
AC4: 'Reply on EC1', Jagat Bisht, 30 Sep 2022
Dear Honourable Executive Editor
We uploaded a pdf document "LETKF_system_setup_User_Guide_v2.pdf " in our zenodo account (https://doi.org/10.5281/zenodo.7127658), apology for the incorrect name ("LETKF_DA_GUIDE") in the previous version. In this document we updated the compiler information used to compile the LETKF and MIROC4-ACTM source codes which is basically "Intel MPI with Intel Fortran compilers (mpiifort; version 2018.1.163)”.
Best regards,
Jagat Bisht
Citation: https://doi.org/10.5194/egusphere-2022-719-AC4 -
EC2: 'Reply on AC4', Shu-Chih Yang, 06 Oct 2022
Dear authors,
The LETKF system set-up user guide looks clear to me. However, since MIROC4-ACTM Zenodo account contains only the CH4 simulation module, it is not clear to me which version of the MIROC model you are using.
Considering the license issue of the MIROC model, it is accepted that access to Zenodo for MIROC is restricted. However, please grant me access to the MIROC model you used in this study so we can make sure that all the results presented in this manuscript are reproducible.
Thank you.
Shu-Chih
Citation: https://doi.org/10.5194/egusphere-2022-719-EC2
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EC2: 'Reply on AC4', Shu-Chih Yang, 06 Oct 2022
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AC4: 'Reply on EC1', Jagat Bisht, 30 Sep 2022
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AC3: 'Reply on CEC3', Jagat Bisht, 30 Sep 2022
Dear Honourable Chief Editor,
Thank you very much for your prompt attention to our reply.
Apology for the incorrect nomenclature "LETKF_DA_GUIDE". We introduced a pdf document “LETKF_system_setup_User_Guide_v2.pdf” in our zenodo account (https://doi.org/10.5281/zenodo.7127658) which describes about LETKF system set-up compilation and run procedure. We added point number 3 "Observation data for assimilation" in “LETKF_system_setup_User_Guide_v2.pdf” which describes the procedure to read the *.bin files. In addition, we uploaded a Fortran script separately (“read_bin_files.f90”) to read *.bin files.
Best regards,
Jagat Bisht
Citation: https://doi.org/10.5194/egusphere-2022-719-AC3
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EC1: 'Reply on CEC3', Shu-Chih Yang, 28 Sep 2022
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CEC3: 'Reply on AC2', Juan Antonio Añel, 28 Sep 2022
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AC2: 'Reply on CEC2', Jagat Bisht, 28 Sep 2022
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CEC2: 'Reply on AC1', Juan Antonio Añel, 14 Sep 2022
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AC1: 'Reply on CEC1', Jagat Bisht, 14 Sep 2022
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RC1: 'Comment on egusphere-2022-719', Anonymous Referee #1, 08 Sep 2022
General comments
Bisht et al. present in their manuscript a new data assimilation system based on the Local Ensemble Transform Kalman Filter (LETKF) method with atmospheric transport described by the atmospheric transport model, MIROC4. The system is applied to the estimation of surface fluxes of methane, using both a network of surface observations and GOSAT satellite retrievals. This study describes the method and tests it using Observing System Simulation Experiments (OSSEs) consisting of performing inversions with synthetic observations and for which the true fluxes are known. A number of sensitivity tests are presented to test the system.
On the whole the methodology is scientifically sound and based on previously published models and algorithms. However, in parts the manuscript is difficult to follow and the text unclear or ambiguous. In particular, I suggest improving the description of the methodology especially regarding the preparation and selection of the pseudo observations (see specific comments). In addition, the results and discussion section could be improved to make it easier to follow.
Based on this, I think the manuscript could become acceptable after minor revisions.
Specific comments
L10: “which is substantially” should be “which has substantially”
L23: suggest removing “absolute” before normalized, since if normalization is done the value is always relative
L32: “much high” should be “much higher”
L35: “that have anthropogenic” should be “that has anthropogenic” (i.e., singular form)
L36, “the global CH4 budget”
L37: suggest stating that the given range is for the total of all sources and not to put it in parentheses since it is quite important information
L38: suggest changing “remaining CH4 emissions” to “main anthropogenic CH4 emissions” since you list only anthropogenic ones and not all (e.g., the minor source from incomplete combustion of bio and fossil fuels is not mentioned)
L43: I think the reaction with Cl radicals actually mostly occurs in the troposphere where Cl is more abundant, see e.g.:
Wuebbles, D., Hayhoe, K. and Kotamarthi, R. (1999), Atmospheric Methane in the Global Environment. In: Atmospheric Methane: Sources, Sinks, and Role in Global Change. (Eds. M. Khalil), Springer-Verlag, New York, NY.
Allan, W., Struthers, H., and Lowe, D. C. (2007), Methane carbon isotope effects caused by atomic chlorine in the marine boundary layer: Global model results compared with Southern Hemisphere measurements, J. Geophys. Res., 112(D4), doi:10.1029/2006JD007369.
L63: The resolution of the control vector in EnKF methods is strongly limited by the ensemble size, if the number of ensemble members is much smaller than the rank of the error covariance matrix, then this method can give spurious results, see e.g.:
Houtekamer, P. L., & Zhang, F. (2016). Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation. Mon. Wea. Rev., 144(12), 4489–4532. http://doi.org/10.1175/MWR-D-15-0440.1.
This limitation is not present in variational methods.
L70: replace “in the” with “for”, i.e., “for carbon cycle research”
L73: Remove “The” before “assimilation” and change “window” to “windows” since you are not referring to one specific assimilation window, but to them generally.
L74: change “hour” to “hours”
L75: The time resolution of the control vector is not the only consideration in the assimilation time window, but the time frame over which the system behaves linearly, and in what time frame the observations respond to the control variables (in this case, determined by atmospheric transport)
L80: change “estimate” to “estimates”
Eq. 1: This equation should be re-written to express x^b and xmean^b (column vectors) as matrices with the same dimensions as X^b (or alternatively for any ith member of the ensemble using the ith column of X^b)
L99: “and is derived” (missing “is”) and change “with” to “using”
Figure 1: Please change “broken line” to “dotted line” as “broken” could also be confused with the dashed line used.
L140: Please spell-out RTPS
L144: Please specify that Eq. 8 is referring to RTPS and not RTPP.
L169: change “accelerates” to “accelerate”
L170: change “observation” to “observations”
L191: change to “applied to the”
L198: change to “initial perturbations are applied”
Section 3.3: I don’t see where the locations of the surface network sites are given. It would be helpful to include a figure of these.
L205: change to “Errors in the estimated fluxes could arise…” I think the authors should also specify that this is in the context of the OSSE. In real-data inversions there are additional sources of potential error, e.g., modelled transport, inappropriate prior or observation uncertainties.
L205: Please clarify if “inflation used” the authors refer to the inflation of the covariance matrix (as described in section 2.1), and if so, is this not coupled to insufficient ensemble size since the inflation is to account for an under dispersive ensemble?
L210-212: I’m not sure what the authors mean by the following:
“We have estimated the CH4 flux for each grid by choosing the observation that influence the grid point using optimal cutoff radius (horizontal covariance localization) of 2200 km and vertical covariance localization of 0.3 in the natural logarithmic pressure (ln P) coordinate.” Could the authors please explain in more detail how observations were selected for assimilation?
In addition could the authors please explain:
“The optimized value of horizontal and vertical localizations…”
The localizations of which variables?
L229-234: I suggest removing the discussion of the assimilation window here and adding the new information to where this is discussed in section 2 (note, the assimilation window is discussed in section 2 (not 2.1).
L242: XCH4 is not weighted by the prior and averaging kernel, but rather it is a weighting of the prior and the modelled mixing ratios, where the weighting is given by the averaging kernel.
L247-251: Similar to my comment above, I think the selection of observations needs further explanation.
L258: Please change “It could be noticed that the…” to e.g. “Noteworthy is that the…”
L259: change to “15% larger error”
L315: change “discussed” to “discuss” and add “of” before “GOSAT”
Section 4.2, L315-329: I find these paragraphs quite confusing. If I understand well, these paragraphs should introduce the sensitivity tests carried-out in this section? If so, please start with the description of these tests, and simply state if the same set-up was used (or not) as Experiment 1. Why were the experiments for assimilation window and ensemble size performed on the satellite observation dataset and not on the “dense surface observation” dataset? Would the results, e.g., for assimilation window, change for surface observations compared to satellite ones?
L359: By “the larger coverage of CH4 observations” in the 8 day assimilation window do the authors mean the greater sensitivity or “footprint” of the observations through the longer computation of atmospheric transport? This should be made clearer.
L375: I think by “relatively diluted flux signal” the authors mean the weaker constraint on surface fluxes provided by satellite observations or the weaker connection of the satellite signal to surface fluxes. I think the term “diluted” is a bit vague.
Section 4.2.3: I think the chi-square test needs a bit more explanation. For instance, which normal variables are being summed in this test? It would be helpful to write the equation. Also, if I understand correctly what is being tested here, would a value greater or lower than one possibly be also due to an under estimation of the observation error covariance?
L387: “Our results suggest that, background error covariance matrix is highly underestimated in both RTPS and FM covariance inflation methods (Fig. 7b) and indicates much confidence in the model” – please explain why this gives the authors “much confidence”
L395-407: This section is difficult to follow. To start with, by “the flux estimation is extremely sensitive…” do the authors mean that the analysis fluxes are sensitive to the prior uncertainty, and by “provide larger prior uncertainty” do the authors mean to generate the prior error covariance matrix, or do they mean the perturbation to generate the prior fluxes? And in L402, by “the flux estimated error” do the authors mean the error between the analysis and true fluxes, and that this error would be larger when the inflation parameter is calculated grid-wise compared to region-wise?
L406: The authors mention that machine learning could be used to determine the spread of the initial ensemble. I think this needs to be explained, i.e., how could machine learning help?
Citation: https://doi.org/10.5194/egusphere-2022-719-RC1 -
AC5: 'Reply on RC1', Jagat Bisht, 19 Nov 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-719/egusphere-2022-719-AC5-supplement.pdf
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AC5: 'Reply on RC1', Jagat Bisht, 19 Nov 2022
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RC2: 'Comment on egusphere-2022-719', Anonymous Referee #2, 14 Sep 2022
General Comments
Bisht et al present a data assimilation system for local ensemble transform Kalman filter, and evaluate that through OSSEs, particularly testing three covariance inflation methods (fixed multiplicative, relaxation to prior spread, and adaptive multiplicative) and two observing networks (surface dense network and GOSAT satellite network). This manuscript describes several interesting findings. I have three concerns.
- This OSSE does not account model transport error, which would result in over-optimized solutions.
- The number of ensemble members is not sufficiently greater than the dimension of the state vectors, which might bias the inversion performance interpretation.
- Several sections require clarifications, as in the following “Specific Comments”.
Specific Comments
L43: “Cl in the stratosphere”. Suggest including Cl in the troposphere.
L22: Typo, “the ensemble forecast of CH4 concentrations”
L79: “Advanced”. Could you please specify what is the advanced aspect of this study, comparing to the previous studies using the same model? Is it the setup of the multi-window optimizing framework, or these inflation methods, or others?
L188: “by 30%”. Unclear if this is uniform bias. According to the later text, the perturbation is not uniform. Could you please specify the way to combine this “30%” with the following regional/grid level perturbation?
L196: “Experiment1”. The word is misleading. Confused the readers if these experiments are corresponding to the experiments in section 4.1 and 4.2 (in fact, they are not).
L196: “regional basis over land” and “every grid over ocean”. Please explain why emissions over land and over ocean are perturbed differently.
L207: “Only surface layer CH4 concentrations are used”. Both over land and ocean? Please explain if the “dense observation network” include all surface grids or a collection of surface networks. If it is the first case, the word “dense observation network” is confusing.
L208: “added a constant measurement uncertainty of 5ppb”. Please explain the way to add this 5 ppb (uniformly increase/decrease 5 ppb?). Also, typo, space between “5” and “ppb”.
L236: “3.4 Experiment2”. In experiment 1, “dense observation formulation”, the author added measurement uncertainty of 5 ppb. Please explain why experiment 2 has no observation error, given the fact that satellite observations have larger uncertainties than measurements of surface sites.
L406: “Machine learning tools could be used to”. Machine learning comes from nowhere. Please explain why it would help.
Citation: https://doi.org/10.5194/egusphere-2022-719-RC2 -
AC6: 'Reply on RC2', Jagat Bisht, 19 Nov 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-719/egusphere-2022-719-AC6-supplement.pdf
Interactive discussion
Status: closed
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CEC1: 'Comment on egusphere-2022-719', Juan Antonio Añel, 24 Aug 2022
Dear authors,Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlYou have archived the LETKF code on GitHub. However, GitHub is not a suitable repository. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo. Also, the GitHub repository for LETKF does not include a license. If you do not include a license, despite what you state, the code can not be used by others, as it continues to be your property. Therefore, when uploading the model's code to the new 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.About the MIROC model. We understand the reasons that preclude its publication. However, ideally, we should have evidence of proper internal storage by their developers. Therefore, please, provide additional detail on where and how the code is stored, and if possible, ask the developers to share a DOI for the version you use in this work.Therefore, please, publish your code in one of the appropriate repositories, and reply to this comment with the relevant information (links and DOIs) as soon as possible, as it should be available for the Discussions stage. Also, you must include the modified 'Code and Data Availability' section in a potential reviewed version of your manuscript.Please, be aware that failing to comply promptly with this request could result in rejecting your manuscript for publication.Regards,Juan A. AñelGeosci. Model Dev. Exec. EditorCitation: https://doi.org/
10.5194/egusphere-2022-719-CEC1 -
AC1: 'Reply on CEC1', Jagat Bisht, 14 Sep 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-719/egusphere-2022-719-AC1-supplement.pdf
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CEC2: 'Reply on AC1', Juan Antonio Añel, 14 Sep 2022
Dear authors,
Unfortunately, your reply does not solve the problems with your manuscript. First, the repositories in Zenodo are not accessible; requesting permission to access them is necessary. This is against our policy. Indeed, I would ask you to read it carefully. The repositories must be public, and removal of them must be impossible. Also, your reply states that you have uploaded "data assimilation code". This is not enough, you must publish the complete model code.Finally, I want to point out that I made my comment on your paper three weeks ago, asking for a prompt reply. This is much more time than I expect to solve an issue so simple as uploading code to a repository. Indeed, your manuscript should have never been accepted for Discussions or sent out to review by the Topical Editor with these shortcomings; however, we are trying to solve it now.I must make clear again that if you do not store the code of the models in a public repository in a prompt and quick manner and reply to this comment with the information about the repositories, we will proceed to reject your manuscript.Best regards,
Juan A. Añel
Geosci. Model Dev. Executive Editor.
Citation: https://doi.org/10.5194/egusphere-2022-719-CEC2 -
AC2: 'Reply on CEC2', Jagat Bisht, 28 Sep 2022
Dear Honourable Chief Editor,First for the delay in my reply: I was on vacation immediately after submission of the manuscript. It is my first visit to my home in India since the Covid-19 outbreak. I have tried to work on the code submission to Zenodo as the first thing after returning. We appreciate your immediate attention to our submission. Apologies for slower responses on our side.
Secondly, I have only developed the LETKF code for this work as a part of my post-doctoral research. This is a stand-alone code and any ACTM can be plugged into this code to perform CH4 data assimilation. We are also keeping the CH4 model simulation module in MIROC in our Zenodo account (https://zenodo.org/record/7079139; this contains only the CH4 simulation module not whole MIROC package), so that all components of CH4 simulations can be publicly available. The MIROC version 4 is coupled with our CH4 simulation module to perform simulations. The whole MIROC package couldn't be publicly or with restrictions made available due to copyright policy of the MIROC community as we mentioned in our manuscript.
Thus we are able to obey the GMD code and data policy:
Core Principle #2: “…. In particular, authors must make every effort to publish any code whose development is described in the manuscript.”
Following this we are happy to publicly archive the LETKF code (https://zenodo.org/record/7079167). Please note that the restricted access is now removed. In addition, all the scripts for running the assimilation code, input files and output file are also made available (https://zenodo.org/record/7098323) on Zenodo. We also provided a LETKF data assimilation user guide (LETKF_DA_GUIDE) in our zenodo account which describes how to prepare and run LETKF data assimilation experiments mentioned in our manuscript.We also discussed with our colleagues who are using MIROC and associated research and development codes, and checked their recent publications. We were encouraged to submit our manuscript because such a code sharing policy in the most recent papers has been accepted. An example from the year 2022 is given.https://gmd.copernicus.org/articles/15/5627/2022/gmd-15-5627-2022.htmlCitation: https://doi.org/10.5194/egusphere-2022-719-AC2 -
CEC3: 'Reply on AC2', Juan Antonio Añel, 28 Sep 2022
Dear authors,
I have checked the Zenodo repositories, and I have not found the LETKF_DA_GUIDE that you mention. Could you have missed it? If not, please, could you be more explicit about where we can find it?
Also, some of your data files are in binary format (.bin). Can you clarify what kind of software is necessary to read them?
Many thanks,
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2022-719-CEC3 -
EC1: 'Reply on CEC3', Shu-Chih Yang, 28 Sep 2022
Dear Authors,
Is "https://zenodo.org/record/7079167" the LETKF_DA_GUIDE? Please specify what kind of software you used to compile the LETKF codes and MIROC model. It is not clear enough to provide the Makefile only.
Thanks,
Shu-Chih
Citation: https://doi.org/10.5194/egusphere-2022-719-EC1 -
AC4: 'Reply on EC1', Jagat Bisht, 30 Sep 2022
Dear Honourable Executive Editor
We uploaded a pdf document "LETKF_system_setup_User_Guide_v2.pdf " in our zenodo account (https://doi.org/10.5281/zenodo.7127658), apology for the incorrect name ("LETKF_DA_GUIDE") in the previous version. In this document we updated the compiler information used to compile the LETKF and MIROC4-ACTM source codes which is basically "Intel MPI with Intel Fortran compilers (mpiifort; version 2018.1.163)”.
Best regards,
Jagat Bisht
Citation: https://doi.org/10.5194/egusphere-2022-719-AC4 -
EC2: 'Reply on AC4', Shu-Chih Yang, 06 Oct 2022
Dear authors,
The LETKF system set-up user guide looks clear to me. However, since MIROC4-ACTM Zenodo account contains only the CH4 simulation module, it is not clear to me which version of the MIROC model you are using.
Considering the license issue of the MIROC model, it is accepted that access to Zenodo for MIROC is restricted. However, please grant me access to the MIROC model you used in this study so we can make sure that all the results presented in this manuscript are reproducible.
Thank you.
Shu-Chih
Citation: https://doi.org/10.5194/egusphere-2022-719-EC2
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EC2: 'Reply on AC4', Shu-Chih Yang, 06 Oct 2022
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AC4: 'Reply on EC1', Jagat Bisht, 30 Sep 2022
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AC3: 'Reply on CEC3', Jagat Bisht, 30 Sep 2022
Dear Honourable Chief Editor,
Thank you very much for your prompt attention to our reply.
Apology for the incorrect nomenclature "LETKF_DA_GUIDE". We introduced a pdf document “LETKF_system_setup_User_Guide_v2.pdf” in our zenodo account (https://doi.org/10.5281/zenodo.7127658) which describes about LETKF system set-up compilation and run procedure. We added point number 3 "Observation data for assimilation" in “LETKF_system_setup_User_Guide_v2.pdf” which describes the procedure to read the *.bin files. In addition, we uploaded a Fortran script separately (“read_bin_files.f90”) to read *.bin files.
Best regards,
Jagat Bisht
Citation: https://doi.org/10.5194/egusphere-2022-719-AC3
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EC1: 'Reply on CEC3', Shu-Chih Yang, 28 Sep 2022
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CEC3: 'Reply on AC2', Juan Antonio Añel, 28 Sep 2022
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AC2: 'Reply on CEC2', Jagat Bisht, 28 Sep 2022
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CEC2: 'Reply on AC1', Juan Antonio Añel, 14 Sep 2022
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AC1: 'Reply on CEC1', Jagat Bisht, 14 Sep 2022
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RC1: 'Comment on egusphere-2022-719', Anonymous Referee #1, 08 Sep 2022
General comments
Bisht et al. present in their manuscript a new data assimilation system based on the Local Ensemble Transform Kalman Filter (LETKF) method with atmospheric transport described by the atmospheric transport model, MIROC4. The system is applied to the estimation of surface fluxes of methane, using both a network of surface observations and GOSAT satellite retrievals. This study describes the method and tests it using Observing System Simulation Experiments (OSSEs) consisting of performing inversions with synthetic observations and for which the true fluxes are known. A number of sensitivity tests are presented to test the system.
On the whole the methodology is scientifically sound and based on previously published models and algorithms. However, in parts the manuscript is difficult to follow and the text unclear or ambiguous. In particular, I suggest improving the description of the methodology especially regarding the preparation and selection of the pseudo observations (see specific comments). In addition, the results and discussion section could be improved to make it easier to follow.
Based on this, I think the manuscript could become acceptable after minor revisions.
Specific comments
L10: “which is substantially” should be “which has substantially”
L23: suggest removing “absolute” before normalized, since if normalization is done the value is always relative
L32: “much high” should be “much higher”
L35: “that have anthropogenic” should be “that has anthropogenic” (i.e., singular form)
L36, “the global CH4 budget”
L37: suggest stating that the given range is for the total of all sources and not to put it in parentheses since it is quite important information
L38: suggest changing “remaining CH4 emissions” to “main anthropogenic CH4 emissions” since you list only anthropogenic ones and not all (e.g., the minor source from incomplete combustion of bio and fossil fuels is not mentioned)
L43: I think the reaction with Cl radicals actually mostly occurs in the troposphere where Cl is more abundant, see e.g.:
Wuebbles, D., Hayhoe, K. and Kotamarthi, R. (1999), Atmospheric Methane in the Global Environment. In: Atmospheric Methane: Sources, Sinks, and Role in Global Change. (Eds. M. Khalil), Springer-Verlag, New York, NY.
Allan, W., Struthers, H., and Lowe, D. C. (2007), Methane carbon isotope effects caused by atomic chlorine in the marine boundary layer: Global model results compared with Southern Hemisphere measurements, J. Geophys. Res., 112(D4), doi:10.1029/2006JD007369.
L63: The resolution of the control vector in EnKF methods is strongly limited by the ensemble size, if the number of ensemble members is much smaller than the rank of the error covariance matrix, then this method can give spurious results, see e.g.:
Houtekamer, P. L., & Zhang, F. (2016). Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation. Mon. Wea. Rev., 144(12), 4489–4532. http://doi.org/10.1175/MWR-D-15-0440.1.
This limitation is not present in variational methods.
L70: replace “in the” with “for”, i.e., “for carbon cycle research”
L73: Remove “The” before “assimilation” and change “window” to “windows” since you are not referring to one specific assimilation window, but to them generally.
L74: change “hour” to “hours”
L75: The time resolution of the control vector is not the only consideration in the assimilation time window, but the time frame over which the system behaves linearly, and in what time frame the observations respond to the control variables (in this case, determined by atmospheric transport)
L80: change “estimate” to “estimates”
Eq. 1: This equation should be re-written to express x^b and xmean^b (column vectors) as matrices with the same dimensions as X^b (or alternatively for any ith member of the ensemble using the ith column of X^b)
L99: “and is derived” (missing “is”) and change “with” to “using”
Figure 1: Please change “broken line” to “dotted line” as “broken” could also be confused with the dashed line used.
L140: Please spell-out RTPS
L144: Please specify that Eq. 8 is referring to RTPS and not RTPP.
L169: change “accelerates” to “accelerate”
L170: change “observation” to “observations”
L191: change to “applied to the”
L198: change to “initial perturbations are applied”
Section 3.3: I don’t see where the locations of the surface network sites are given. It would be helpful to include a figure of these.
L205: change to “Errors in the estimated fluxes could arise…” I think the authors should also specify that this is in the context of the OSSE. In real-data inversions there are additional sources of potential error, e.g., modelled transport, inappropriate prior or observation uncertainties.
L205: Please clarify if “inflation used” the authors refer to the inflation of the covariance matrix (as described in section 2.1), and if so, is this not coupled to insufficient ensemble size since the inflation is to account for an under dispersive ensemble?
L210-212: I’m not sure what the authors mean by the following:
“We have estimated the CH4 flux for each grid by choosing the observation that influence the grid point using optimal cutoff radius (horizontal covariance localization) of 2200 km and vertical covariance localization of 0.3 in the natural logarithmic pressure (ln P) coordinate.” Could the authors please explain in more detail how observations were selected for assimilation?
In addition could the authors please explain:
“The optimized value of horizontal and vertical localizations…”
The localizations of which variables?
L229-234: I suggest removing the discussion of the assimilation window here and adding the new information to where this is discussed in section 2 (note, the assimilation window is discussed in section 2 (not 2.1).
L242: XCH4 is not weighted by the prior and averaging kernel, but rather it is a weighting of the prior and the modelled mixing ratios, where the weighting is given by the averaging kernel.
L247-251: Similar to my comment above, I think the selection of observations needs further explanation.
L258: Please change “It could be noticed that the…” to e.g. “Noteworthy is that the…”
L259: change to “15% larger error”
L315: change “discussed” to “discuss” and add “of” before “GOSAT”
Section 4.2, L315-329: I find these paragraphs quite confusing. If I understand well, these paragraphs should introduce the sensitivity tests carried-out in this section? If so, please start with the description of these tests, and simply state if the same set-up was used (or not) as Experiment 1. Why were the experiments for assimilation window and ensemble size performed on the satellite observation dataset and not on the “dense surface observation” dataset? Would the results, e.g., for assimilation window, change for surface observations compared to satellite ones?
L359: By “the larger coverage of CH4 observations” in the 8 day assimilation window do the authors mean the greater sensitivity or “footprint” of the observations through the longer computation of atmospheric transport? This should be made clearer.
L375: I think by “relatively diluted flux signal” the authors mean the weaker constraint on surface fluxes provided by satellite observations or the weaker connection of the satellite signal to surface fluxes. I think the term “diluted” is a bit vague.
Section 4.2.3: I think the chi-square test needs a bit more explanation. For instance, which normal variables are being summed in this test? It would be helpful to write the equation. Also, if I understand correctly what is being tested here, would a value greater or lower than one possibly be also due to an under estimation of the observation error covariance?
L387: “Our results suggest that, background error covariance matrix is highly underestimated in both RTPS and FM covariance inflation methods (Fig. 7b) and indicates much confidence in the model” – please explain why this gives the authors “much confidence”
L395-407: This section is difficult to follow. To start with, by “the flux estimation is extremely sensitive…” do the authors mean that the analysis fluxes are sensitive to the prior uncertainty, and by “provide larger prior uncertainty” do the authors mean to generate the prior error covariance matrix, or do they mean the perturbation to generate the prior fluxes? And in L402, by “the flux estimated error” do the authors mean the error between the analysis and true fluxes, and that this error would be larger when the inflation parameter is calculated grid-wise compared to region-wise?
L406: The authors mention that machine learning could be used to determine the spread of the initial ensemble. I think this needs to be explained, i.e., how could machine learning help?
Citation: https://doi.org/10.5194/egusphere-2022-719-RC1 -
AC5: 'Reply on RC1', Jagat Bisht, 19 Nov 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-719/egusphere-2022-719-AC5-supplement.pdf
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AC5: 'Reply on RC1', Jagat Bisht, 19 Nov 2022
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RC2: 'Comment on egusphere-2022-719', Anonymous Referee #2, 14 Sep 2022
General Comments
Bisht et al present a data assimilation system for local ensemble transform Kalman filter, and evaluate that through OSSEs, particularly testing three covariance inflation methods (fixed multiplicative, relaxation to prior spread, and adaptive multiplicative) and two observing networks (surface dense network and GOSAT satellite network). This manuscript describes several interesting findings. I have three concerns.
- This OSSE does not account model transport error, which would result in over-optimized solutions.
- The number of ensemble members is not sufficiently greater than the dimension of the state vectors, which might bias the inversion performance interpretation.
- Several sections require clarifications, as in the following “Specific Comments”.
Specific Comments
L43: “Cl in the stratosphere”. Suggest including Cl in the troposphere.
L22: Typo, “the ensemble forecast of CH4 concentrations”
L79: “Advanced”. Could you please specify what is the advanced aspect of this study, comparing to the previous studies using the same model? Is it the setup of the multi-window optimizing framework, or these inflation methods, or others?
L188: “by 30%”. Unclear if this is uniform bias. According to the later text, the perturbation is not uniform. Could you please specify the way to combine this “30%” with the following regional/grid level perturbation?
L196: “Experiment1”. The word is misleading. Confused the readers if these experiments are corresponding to the experiments in section 4.1 and 4.2 (in fact, they are not).
L196: “regional basis over land” and “every grid over ocean”. Please explain why emissions over land and over ocean are perturbed differently.
L207: “Only surface layer CH4 concentrations are used”. Both over land and ocean? Please explain if the “dense observation network” include all surface grids or a collection of surface networks. If it is the first case, the word “dense observation network” is confusing.
L208: “added a constant measurement uncertainty of 5ppb”. Please explain the way to add this 5 ppb (uniformly increase/decrease 5 ppb?). Also, typo, space between “5” and “ppb”.
L236: “3.4 Experiment2”. In experiment 1, “dense observation formulation”, the author added measurement uncertainty of 5 ppb. Please explain why experiment 2 has no observation error, given the fact that satellite observations have larger uncertainties than measurements of surface sites.
L406: “Machine learning tools could be used to”. Machine learning comes from nowhere. Please explain why it would help.
Citation: https://doi.org/10.5194/egusphere-2022-719-RC2 -
AC6: 'Reply on RC2', Jagat Bisht, 19 Nov 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-719/egusphere-2022-719-AC6-supplement.pdf
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Jagat S. H. Bisht
Prabir K. Patra
Masayuki Takigawa
Takashi Sekiya
Yugo Kanaya
Naoko Saitoh
Kazuyuki Miyazaki
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