the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Jun 2023
The NOAA Aerosol Reanalysis version 1.0 (NARA v1.0): Description of the Modeling System and its Evaluation
Abstract. In this manuscript, we describe the first ever global aerosol reanalysis at the National Oceanic and Atmospheric Administration (NOAA), the NOAA Aerosol ReAnalysis version 1.0 (NARA v1.0) that was produced for the year 2016. In NARA v1.0, the forecast model is an early version of the operational Global Ensemble Forecast System-Aerosols (GEFS-Aerosols) model. The three-dimensional ensemble-variational (3D-EnVar) data assimilation (DA) system configuration is built using elements of the Joint Effort for Data assimilation Integration (JEDI) framework being developed at the Joint Center for Satellite Data Assimilation (JCSDA). The Neural Network Retrievals (NNR) of Aerosol Optical Depth (AOD) at 550 nm from the MODerate resolution Imaging Spectroradiometer (MODIS) instruments are assimilated to provide reanalysis of aerosol mass mixing ratios. We evaluate NARA v1.0 against a wide variety of Aerosol Robotic NETwork (AERONET) observations, against National Aeronautics and Space Administration’s (NASA) Modern-Era Retrospective analysis for Research and Applications 2 (MERRA-2; Gelaro et al., 2017; Randles et al., 2017; Buchard et al., 2017) and European Centre for Medium-Range Weather Forecasts’ (ECMWF) Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA; Inness et al., 2019), and against measurements of surface concentrations of particulate matter 2.5 (PM2.5) and aerosol species. Overall, the 3D-EnVar DA system significantly improves AOD simulations compared to observations, but the assimilation has limited impact on chemical composition and size distributions of aerosols. This reveals limitations of assimilating AOD retrievals at a single wavelength. We also identify deficiencies in the model’s representations of aerosol chemistry and their optical properties elucidated from evaluation of NARA v1.0 against AERONET observations. A comparison of seasonal profiles of aerosol species from NARA v1.0 with the other two reanalyses exposes significant differences in climatologies. These differences reflect uncertainties in simulating aerosols in general. In our opinion, such uncertainties may translate to inaccuracies in weather and climate modeling when impacts of aerosols on atmospheric radiation and/or cloud processes are considered.
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Notice on discussion status
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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Preprint
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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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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-356', Anonymous Referee #1, 12 Jul 2023
Review of
The NOAA Aerosol Reanalysis version 1.0 (NARA v1.0): Description of the Modeling System and its Evaluation
By Wei et al.
Overview:
The paper presents a global aerosol reanalysis data set for the year 2016 (NARA v1.0) produced by assimilating MODIS AOD in the GEFS aerosol model using the JEDI data assimilation framework. The reanalysis data and a control run (free) without AOD assimilation is evaluated with AOD and Angstroem exponent (AE) observations from Aeronet, surface PM observations derived from openAQ and speciated observations from the IMPROVE network. Further, it is intercompared against the MERRA-2 and CAMS aerosol reanalysis data sets. The authors find improved AOD compared to AERONET observations but no or only smaller improvements for AE and PM. Overall NARA v1.0 seems closer to the MERRA-2 re-analysis than to CAMS.
General remarks
The paper gives a reasonable description of the modelling system and the scientific accuracy of the NARA data set. However, there are further updates required before the paper can be recommended for publication.
- It is an omission of the paper that NARA, CAMS and MERRA-2 are only compared against each other for AOD and regional vertical profiles. Instead, the evaluation against independent observations should be carried for CAMS and MERRA-2 too. The accuracy measure of the three data should be compared in the paper. It is of great interest to the reader to get know which of these data sets is closer to observations.
- The evaluation results should be represented in a more quantitative manner and always for NARA and the FREE run to document the impact of the AOD assimilation. For example, the pdf plots do not have a colour scale legend and only some of them contain information about bias and correlation.
- The approach for the PM evaluation needs to be described in more detail, in particular the seemingly missing application of any quality control of the openAQ data should be justified. Further, the method to account for vertical stratification and spatial representativeness is not always clear.
- The abstract should be revised to contain more factual information from the paper. General remarks that are as such not direct conclusion from the paper should be avoided.
- The data assimilation procedure seems to include an optimisation of the emissions (see also Fig 1) Please provide more detail on this important aspect from a technical and a scientific perspective.
Specific comments:
L 11: once published that is not a manuscript
L 11: “first ever” may be misleading because there are other aerosol re-analysis
L 24: There is no clear evidence in the paper that single-wavelength assimilation is the main factor of the limited impact.
L 28: What is meant here by “climatologies”
L 28: “In our opinion, such uncertainties may translate to inaccuracies in weather and climate modeling when impacts of aerosols on atmospheric radiation and/or cloud processes are considered.” The paper does not deal in any way with the impact of aerosol and radiation and cloud processes and it is therefore not a conclusion of this paper. Please avoid statements that are not substantiated by the paper.
L 71: Please check of SOA’s and Nitrates are included in CAMSRA (I believe not)
L 114: openAQ data are know for the lack of QC. Please expand on that and clarify the data sources that were compiled in the openAQ data set for 2016. A good solution would be to provide a global map to show the spatial distribution of the PM2.5 observations.
L 123: Why are gases mentioned here? Did you use the data , for example SO2 ?
L 134: Comment on the importance of representing Nitrates and Secondary Organic Aerosol especially for the accuracy of PM.
L 170-174: Please expand on these aspects (SPPT and emission updates) and provide more information about their usefulness for the realism of the NARA data set.
L 174: Please show and discuss the modified emissions.
L 205: Please provide more evidence for this statement “We hope ….”
L 232: Section 5.2 should also include a verification of CAMS-RA and MERRA-2 consistent with the verification of NARA and a comparison of the results
L 304: Please, clarify how you account for the stratification. This would require making assumptions about the vertical aerosol profiles within the model grid box. Just using the diagnosed air density, will not achieve that.
L 307: Please provide information about the size distribution of the different aerosol component and motivate the PM formulae.
L 358: Please add a comparison of the evaluation results from MERRA-2, CAMS and NARA here.
L 362-365: “These observations … “ That is a very general statement and not really justified by the findings of the paper as you do not test the impact of NARA aerosol on radiation and clouds. Please include multiple references if you want to make a point here. For example, you mention yourself that Bozzo et al. 2017 (there is also an ACP paper) successfully used CAMSRA to represent aerosol in the ECMWF model.
Figures:
Fig 1:. Please here or in the text, provide more details in particular on the emission updates, the observation operators and the length of the forecast and assimilation window.
Fig 2: Please add colour legend and basic statistics (bias, R) . Please show AOD in linear scale and not in log scale. The pdf of AOD does not justify the use of the log scale.
Fig 3: Please, clarify if you show a spatial or temporal Correlation coefficient R.
Fig 12: Please add a colour legend, consider showing the plot for different regions and not just a global plot.
Citation: https://doi.org/10.5194/egusphere-2023-356-RC1 - AC4: 'Reply on RC1', Shih Wei Wei, 31 Aug 2023
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RC2: 'Comment on egusphere-2023-356', Anonymous Referee #2, 24 Jul 2023
Review of “The NOAA Aerosol Reanalysis version 1.0 (NARA v1.0): Description of the Modeling System and its Evaluation
By Wei et al.
Overview: This manuscript describes an aerosol product generated for the year 2016, referred to as NARA v1.0, using the GEFS aerosol model and NNR AOD product assimilation using a 3Den-Var data assimilation. The results were compared to NASA’s MERRA-2 and ECMWF’s CAMSRA reanalysis products as well as against the AERONET AOD and surface PM2.5 data. The 2016 NARA v1.0 output was found to be more consistent with AERONET than the free model run. The results are also more consistent with MERRA2 than CAMSRA reanalysis. This isn’t really surprising as the NARA v1.0 setup has more similarities to MERRA-2, using GOCART aerosol and NNR AOD product that is also assimilated for MERRA-2.
General Remarks: The main issue I have with this manuscript is referring to the output as a reanalysis when the results are only shown for 1 year. A reanalysis is typically over a long period of time (10years+) as is the case for the other aerosol reanalysis products that are available. I would call this an evaluation paper of the performance of the 3Den-Var with the GEFS model. I think it’s fair to say that you will apply this setup in the future for generation of a reanalysis product, but don’t think you can call it that here. Given that, I think more needs to be done to define what is different here than in the Huang et al. 2023 paper that defined the data assimilation setup and evaluates the analysis results, although for only a month time period versus a year. I also think there are more details needed in the manuscript, including the data assimilation setup. More description of observations and reanalysis data used in this paper would also be helpful. For the observations, was there any QC done prior to using the data? Was there any temporal averaging done in order to make point data comparable to model output? I do want to highlight that reanalysis products are very important and once you are able to generate that, I think that will be a valuable contribution.
Specific Comments:
- First sentence in the abstract. I see what you are saying here, but I think this could be a bit misleading. It could come off as you have the first aerosol reanalysis product ever, which is not the case. Please reword this as “the first version of the aerosol reanalysis for NOAA”, or something like that in order to clarify.
- Page 4, first line: “In this study, we used and designed a specific JEDI-based 3D-EnVar DA configuration to produce the NOAA Aerosol ReAnalysis version 1.0 (NARA v1.0). “ Please elaborate on how this differs from the setup described in Huang et al. 2023. The difference is not clear and why is the chosen configuration better for a reanalysis?
- Have you also looked at performance of fine mode fraction or fine/coarse AOD?
- I think looking at timeseries that are not monthly-averaged would also be helpful, at AERONET sites for example, to see if your product is able to generate daily variability. Perhaps you can select reference sites that have a good amount of data and at locations that are representative of big dust/smoke/pollution/sea salt dominated regimes.
Citation: https://doi.org/10.5194/egusphere-2023-356-RC2 - AC3: 'Reply on RC2', Shih Wei Wei, 31 Aug 2023
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CEC1: 'Comment on egusphere-2023-356', Juan Antonio Añel, 30 Jul 2023
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.htmlYour "Code and data availability" section reads:
JEDI code is available at https://github.com/JCSDA/fv3-bundle
GEFS-Aerosols code is available at https://github.com/SamuelTrahanNOAA/ufs-weather-model
NARA v1.0 data is available at https://esrl.noaa.gov/gsd/thredds/catalog/retro/global_aerosol_reanalysis/catalog.html
MERRA-2 data is available at https://disc.gsfc.nasa.gov/385
CAMSRA data is available at https://atmosphere.copernicus.eu/
OpenAQ data is available at https://openaq.org
IMPROVE data is available at http://vista.cira.colostate.edu/improve/None of these repositories is accepted by our policy. What's more, GitHub is specifically mentioned as an unacceptable repository. GitHub is not a suitable repository for scientific publication. GitHub itself instructs authors to use other alternatives for long-term archival and publishing, such as Zenodo.
Therefore, please, publish your code and data in one of the appropriate repositories, and reply to this comment with the relevant information (link and DOI) as soon as possible, as it should be available for the Discussions stage. I should note that the license for the ufs-weather-model does not clarify what license applies to each part of the code in the GitHub repository. In this way, it is impossible to know what conditions apply to each part of the code. This problem should be addressed and solved.
About the data: MERRA2, NARA and CAMSRA data: It would be ideal if you could save the exact data that you have used in new files instead of simply pointing it out to generic download pages, where it can be hard to determine exactly the variable and data used in your work, precluding its replicability. Beyond the NOAA, NASA and COPERNICUS servers, the openaq.org and Colorado State University repositories are not acceptable, and you must store the data in one of the acceptable repositories listed in our policy.
In this way, if you do not fix these issues, we will have to reject your manuscript for publication in our journal. I should note that, actually, your manuscript should not have been accepted in Discussions, given this lack of compliance with our policy. Therefore, the current situation with your manuscript is irregular.Also, you remember including in a potentially reviewed version of your manuscript the modified 'Code and Data Availability' section, including the necessary DOIs.Juan A. AñelGeosci. Model Dev. Exec. EditorCitation: https://doi.org/10.5194/egusphere-2023-356-CEC1 -
AC1: 'Reply on CEC1', Shih Wei Wei, 01 Aug 2023
Dear Editor,
We are working on making data available in public archives with the DOIs.
However, the dataset is pretty large.
For instance, it is over 100GB per month for MERRA-2.
NARA v1 and CAMSRA will be in a similar size.
Could you please advise what is the best way to make it?
Thank you.Best regards,
Shih-WeiCitation: https://doi.org/10.5194/egusphere-2023-356-AC1 - AC2: 'Reply on CEC1', Shih Wei Wei, 31 Aug 2023
-
AC1: 'Reply on CEC1', Shih Wei Wei, 01 Aug 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-356', Anonymous Referee #1, 12 Jul 2023
Review of
The NOAA Aerosol Reanalysis version 1.0 (NARA v1.0): Description of the Modeling System and its Evaluation
By Wei et al.
Overview:
The paper presents a global aerosol reanalysis data set for the year 2016 (NARA v1.0) produced by assimilating MODIS AOD in the GEFS aerosol model using the JEDI data assimilation framework. The reanalysis data and a control run (free) without AOD assimilation is evaluated with AOD and Angstroem exponent (AE) observations from Aeronet, surface PM observations derived from openAQ and speciated observations from the IMPROVE network. Further, it is intercompared against the MERRA-2 and CAMS aerosol reanalysis data sets. The authors find improved AOD compared to AERONET observations but no or only smaller improvements for AE and PM. Overall NARA v1.0 seems closer to the MERRA-2 re-analysis than to CAMS.
General remarks
The paper gives a reasonable description of the modelling system and the scientific accuracy of the NARA data set. However, there are further updates required before the paper can be recommended for publication.
- It is an omission of the paper that NARA, CAMS and MERRA-2 are only compared against each other for AOD and regional vertical profiles. Instead, the evaluation against independent observations should be carried for CAMS and MERRA-2 too. The accuracy measure of the three data should be compared in the paper. It is of great interest to the reader to get know which of these data sets is closer to observations.
- The evaluation results should be represented in a more quantitative manner and always for NARA and the FREE run to document the impact of the AOD assimilation. For example, the pdf plots do not have a colour scale legend and only some of them contain information about bias and correlation.
- The approach for the PM evaluation needs to be described in more detail, in particular the seemingly missing application of any quality control of the openAQ data should be justified. Further, the method to account for vertical stratification and spatial representativeness is not always clear.
- The abstract should be revised to contain more factual information from the paper. General remarks that are as such not direct conclusion from the paper should be avoided.
- The data assimilation procedure seems to include an optimisation of the emissions (see also Fig 1) Please provide more detail on this important aspect from a technical and a scientific perspective.
Specific comments:
L 11: once published that is not a manuscript
L 11: “first ever” may be misleading because there are other aerosol re-analysis
L 24: There is no clear evidence in the paper that single-wavelength assimilation is the main factor of the limited impact.
L 28: What is meant here by “climatologies”
L 28: “In our opinion, such uncertainties may translate to inaccuracies in weather and climate modeling when impacts of aerosols on atmospheric radiation and/or cloud processes are considered.” The paper does not deal in any way with the impact of aerosol and radiation and cloud processes and it is therefore not a conclusion of this paper. Please avoid statements that are not substantiated by the paper.
L 71: Please check of SOA’s and Nitrates are included in CAMSRA (I believe not)
L 114: openAQ data are know for the lack of QC. Please expand on that and clarify the data sources that were compiled in the openAQ data set for 2016. A good solution would be to provide a global map to show the spatial distribution of the PM2.5 observations.
L 123: Why are gases mentioned here? Did you use the data , for example SO2 ?
L 134: Comment on the importance of representing Nitrates and Secondary Organic Aerosol especially for the accuracy of PM.
L 170-174: Please expand on these aspects (SPPT and emission updates) and provide more information about their usefulness for the realism of the NARA data set.
L 174: Please show and discuss the modified emissions.
L 205: Please provide more evidence for this statement “We hope ….”
L 232: Section 5.2 should also include a verification of CAMS-RA and MERRA-2 consistent with the verification of NARA and a comparison of the results
L 304: Please, clarify how you account for the stratification. This would require making assumptions about the vertical aerosol profiles within the model grid box. Just using the diagnosed air density, will not achieve that.
L 307: Please provide information about the size distribution of the different aerosol component and motivate the PM formulae.
L 358: Please add a comparison of the evaluation results from MERRA-2, CAMS and NARA here.
L 362-365: “These observations … “ That is a very general statement and not really justified by the findings of the paper as you do not test the impact of NARA aerosol on radiation and clouds. Please include multiple references if you want to make a point here. For example, you mention yourself that Bozzo et al. 2017 (there is also an ACP paper) successfully used CAMSRA to represent aerosol in the ECMWF model.
Figures:
Fig 1:. Please here or in the text, provide more details in particular on the emission updates, the observation operators and the length of the forecast and assimilation window.
Fig 2: Please add colour legend and basic statistics (bias, R) . Please show AOD in linear scale and not in log scale. The pdf of AOD does not justify the use of the log scale.
Fig 3: Please, clarify if you show a spatial or temporal Correlation coefficient R.
Fig 12: Please add a colour legend, consider showing the plot for different regions and not just a global plot.
Citation: https://doi.org/10.5194/egusphere-2023-356-RC1 - AC4: 'Reply on RC1', Shih Wei Wei, 31 Aug 2023
-
RC2: 'Comment on egusphere-2023-356', Anonymous Referee #2, 24 Jul 2023
Review of “The NOAA Aerosol Reanalysis version 1.0 (NARA v1.0): Description of the Modeling System and its Evaluation
By Wei et al.
Overview: This manuscript describes an aerosol product generated for the year 2016, referred to as NARA v1.0, using the GEFS aerosol model and NNR AOD product assimilation using a 3Den-Var data assimilation. The results were compared to NASA’s MERRA-2 and ECMWF’s CAMSRA reanalysis products as well as against the AERONET AOD and surface PM2.5 data. The 2016 NARA v1.0 output was found to be more consistent with AERONET than the free model run. The results are also more consistent with MERRA2 than CAMSRA reanalysis. This isn’t really surprising as the NARA v1.0 setup has more similarities to MERRA-2, using GOCART aerosol and NNR AOD product that is also assimilated for MERRA-2.
General Remarks: The main issue I have with this manuscript is referring to the output as a reanalysis when the results are only shown for 1 year. A reanalysis is typically over a long period of time (10years+) as is the case for the other aerosol reanalysis products that are available. I would call this an evaluation paper of the performance of the 3Den-Var with the GEFS model. I think it’s fair to say that you will apply this setup in the future for generation of a reanalysis product, but don’t think you can call it that here. Given that, I think more needs to be done to define what is different here than in the Huang et al. 2023 paper that defined the data assimilation setup and evaluates the analysis results, although for only a month time period versus a year. I also think there are more details needed in the manuscript, including the data assimilation setup. More description of observations and reanalysis data used in this paper would also be helpful. For the observations, was there any QC done prior to using the data? Was there any temporal averaging done in order to make point data comparable to model output? I do want to highlight that reanalysis products are very important and once you are able to generate that, I think that will be a valuable contribution.
Specific Comments:
- First sentence in the abstract. I see what you are saying here, but I think this could be a bit misleading. It could come off as you have the first aerosol reanalysis product ever, which is not the case. Please reword this as “the first version of the aerosol reanalysis for NOAA”, or something like that in order to clarify.
- Page 4, first line: “In this study, we used and designed a specific JEDI-based 3D-EnVar DA configuration to produce the NOAA Aerosol ReAnalysis version 1.0 (NARA v1.0). “ Please elaborate on how this differs from the setup described in Huang et al. 2023. The difference is not clear and why is the chosen configuration better for a reanalysis?
- Have you also looked at performance of fine mode fraction or fine/coarse AOD?
- I think looking at timeseries that are not monthly-averaged would also be helpful, at AERONET sites for example, to see if your product is able to generate daily variability. Perhaps you can select reference sites that have a good amount of data and at locations that are representative of big dust/smoke/pollution/sea salt dominated regimes.
Citation: https://doi.org/10.5194/egusphere-2023-356-RC2 - AC3: 'Reply on RC2', Shih Wei Wei, 31 Aug 2023
-
CEC1: 'Comment on egusphere-2023-356', Juan Antonio Añel, 30 Jul 2023
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.htmlYour "Code and data availability" section reads:
JEDI code is available at https://github.com/JCSDA/fv3-bundle
GEFS-Aerosols code is available at https://github.com/SamuelTrahanNOAA/ufs-weather-model
NARA v1.0 data is available at https://esrl.noaa.gov/gsd/thredds/catalog/retro/global_aerosol_reanalysis/catalog.html
MERRA-2 data is available at https://disc.gsfc.nasa.gov/385
CAMSRA data is available at https://atmosphere.copernicus.eu/
OpenAQ data is available at https://openaq.org
IMPROVE data is available at http://vista.cira.colostate.edu/improve/None of these repositories is accepted by our policy. What's more, GitHub is specifically mentioned as an unacceptable repository. GitHub is not a suitable repository for scientific publication. GitHub itself instructs authors to use other alternatives for long-term archival and publishing, such as Zenodo.
Therefore, please, publish your code and data in one of the appropriate repositories, and reply to this comment with the relevant information (link and DOI) as soon as possible, as it should be available for the Discussions stage. I should note that the license for the ufs-weather-model does not clarify what license applies to each part of the code in the GitHub repository. In this way, it is impossible to know what conditions apply to each part of the code. This problem should be addressed and solved.
About the data: MERRA2, NARA and CAMSRA data: It would be ideal if you could save the exact data that you have used in new files instead of simply pointing it out to generic download pages, where it can be hard to determine exactly the variable and data used in your work, precluding its replicability. Beyond the NOAA, NASA and COPERNICUS servers, the openaq.org and Colorado State University repositories are not acceptable, and you must store the data in one of the acceptable repositories listed in our policy.
In this way, if you do not fix these issues, we will have to reject your manuscript for publication in our journal. I should note that, actually, your manuscript should not have been accepted in Discussions, given this lack of compliance with our policy. Therefore, the current situation with your manuscript is irregular.Also, you remember including in a potentially reviewed version of your manuscript the modified 'Code and Data Availability' section, including the necessary DOIs.Juan A. AñelGeosci. Model Dev. Exec. EditorCitation: https://doi.org/10.5194/egusphere-2023-356-CEC1 -
AC1: 'Reply on CEC1', Shih Wei Wei, 01 Aug 2023
Dear Editor,
We are working on making data available in public archives with the DOIs.
However, the dataset is pretty large.
For instance, it is over 100GB per month for MERRA-2.
NARA v1 and CAMSRA will be in a similar size.
Could you please advise what is the best way to make it?
Thank you.Best regards,
Shih-WeiCitation: https://doi.org/10.5194/egusphere-2023-356-AC1 - AC2: 'Reply on CEC1', Shih Wei Wei, 31 Aug 2023
-
AC1: 'Reply on CEC1', Shih Wei Wei, 01 Aug 2023
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Mariusz Pagowski
Arlindo da Silva
Cheng-Hsuan Lu
Bo Huang
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2178 KB) - Metadata XML