Using Chemical Transport Model and Climatology Data as Backgrounds for Aerosol Optical Depth Spatial–Temporal Optimal Interpolation
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe study is in general clear and of high scientific soundness. However, some revisions are needed in order to be published. Plee see my comments below:
Line 142 – 145 : Provide a map concerning the AERONET locations
Line 146 – 148: Please add more detail concerning the estimation of the climatology background. Please give also a feedback on how you plan to build a more relevant climatology background in the future (you can also mention that in your conclusion)
Line 167 – 170: You can provide some map(s) of the European domain with AOD simulations if possible
General comment 1: you should create a separate session with some information concerning the AERONET observational data and a separate session with a brief description concerning the GEOS – Chem simulation
General comment 2: If possible, please give some more comments on your results in session: 3. Results and discussion
General comment 3: There is no session of conclusions (there is one short paragraph at the end instead). Please add one with the main findings of your study and some general comments.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsManuscript ID; atmosphere-3622360
Title: Using Chemical Transport Model and Climatology Data as Backgrounds for Aerosol Optical Depth Spatial-Temporal Optimal Interpolation
This manuscript should be improved before it is considered for publication, even though the issue is of interest to the scientific community. The primary goal of the study should be reexamined by the authors in light of the content. Instead of using lengthy words, they should attempt to summarize and highlight the study's key findings. Moreover, writers should refrain from making rash judgments.
Evaluation; Minor Revision
- Keywords: It is crucial to revise the keywords, ensuring they are spelled correctly and avoid general terms, abbreviations, and plural terms and multiple concepts (avoid, for example, 'and', 'of'). This will help to maintain the precision and clarity of the manuscript.
e.g. spatial-temporal optimal interpolation; chemical transport model GEOS-Chem; radiometric network AERONET (too long )
- Introduction; Current AOD estimating approaches utilizing OI consider solely geographical variability, neglecting temporal evolution, hence failing to encompass the complete spatiotemporal dynamics crucial for precise aerosol monitoring and forecasting. Please add and review this information.
- The primary uncertainty stems from model overestimation relative to data, simplified assumptions for AERONET accuracy, and idealized premises (homogeneity, isotropy, exponential decay) in modeling background error structures. Kindly provide additional information.
- Why only 440, 675, and 870 nm in AOD? AERONET typically measures AOD at 440, 675, 870, and 1020 nm, among others.
- Overall, the method is unclear and does not provide clear results, so it is judged that it should be re-examined after rewriting. Including the above information, please reconfirm and revise the paper.
- All of the main text, many numeric data are given with too many significant figures; 2 significant figures suffice, and 3 suffice in case the first significant figure is "1".
- Conclusion: The conclusions could be further developed; there is a lot of interesting data in the article.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsSummary:
The data assimilation method optimal interpolation (OI) is presented to provide the best estimate of the required parameters by combining observations with appropriate prior information (background), which is relatively simple and computationally cheap. A weighted linear combination of observational data and background is defined to estimate a value of interest for the whole time-interval of interest and spatial-temporal OI (STOI) utilizes both spatial and temporal observational error covariance and background error covariance. Here STOI is applied to daily mean aerosol optical depth (AOD) observations obtained at the European AERONET (Aerosol Robotic Network) sites with the use of the GEOS-Chem chemical transport model simulations and the AOD climatology as backgrounds. Not only spatial, but also temporal gaps in observations are filled. Estimates obtained using modelled data and climatology are merged according to their mean square errors.
General comments:
It is found that mean square errors in the estimate when using modelled data are comparable with those when using climatology. It is concluded that this allows improving AOD estimates in areas where observations are limited in space and time.
The manuscript is clear, relevant for the field and presented in a well-structured manner. It is written in an appropriate way.
The cited references are not mostly recent publications (within the last 5 years) and relevant. Papers within the last 5 years should be added as references. It does not include an excessive number of self-citations.
The manuscript sounds scientifically and the experimental design is appropriate to test the hypothesis and fulfil the objectives.
The manuscript’s results are mainly reproducible based on the details given in the methods section.
The data are interpreted appropriately and consistently throughout the manuscript.
The figures/tables/images/schemes are appropriate. They properly show the data. They are easy to interpret and understand.
The mathematical symbols, abbreviations, and units are generally correctly defined and used.
There are ethics and data availability statements.
Additional comments:
The main question is to provide an approach that has the potential to improve the estimate of atmospheric species characteristics distribution when observations are sparse. It is original and well-defined. The results provide an advancement of the current knowledge.
The topic is relevant in the field. The study confirms a capability of the selected method to fill in spatial and temporal gaps in observations. The estimates of this method are sensitive to the choice of the background in areas with sparsely distributed observations.
Compared with other published material it adds a method to estimate atmospheric species characteristics distribution when observations are sparse.
The analyses are performed with the highest technical standards. The data are robust enough to draw conclusions. The raw data are available and correct. The authors should consider to add an own chapter Conclusions.
The results are interpreted appropriately. They are significant. The conclusions are justified and supported by the results as well as consistent with the evidence and arguments presented and they address the main question posed. The paper will attract a wide readership.
The references are appropriate.
The English language appropriate and understandable.
The work fits the journal scope.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe authors have adequately addressed the reviewer's comments. The paper is ready for publication.
Reviewer 3 Report
Comments and Suggestions for AuthorsThe authors followed the reviewer requirements
