PCLM-D-25-00428

Disentangling key cloud microphysical drivers of precipitation using machine learning on geostationary satellite data

PLOS Climate

Dear Dr. Choi,

Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

Jingyu Wang

Academic Editor

PLOS Climate

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If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Additional Editor Comments (if provided):

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Climate’s publication criteria ? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->?>

Reviewer #1: Yes

Reviewer #2: No

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2. Has the statistical analysis been performed appropriately and rigorously?-->?>

Reviewer #1: Yes

Reviewer #2: I don't know

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3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??>

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: 1. The introduction discusses previous machine learning models but does not highlight the innovative aspect of using explainable AI (XAI). The discussion should clearly describe the SHAP method as an XAI technique and connect this to the growing trend of improving trust and understanding in complex machine learning models.

2. While it mentions limited observations, the main issue is more about the complexities of cloud-precipitation transitions and the need for selecting variables that are physically consistent. A clearer problem statement would strengthen the justification for the study.

3. It would be helpful to rephrase a sentence in the Discussion (Section 4) to specifically mention Explainable AI (XAI) as an important benefit of the SHAP method, which helps address the "black-box" nature of many machine learning models.

4. It would be useful to include one or two relevant external references that discuss hyperparameter optimization or advanced machine learning techniques in environmental modeling. This would help explain the choice of Random Forest and SHAP interpretation, connecting the methodology with the latest developments in data-driven science.

5. Focus on the machine learning methodology, including Random Forest, hyperparameter optimization, and generative models for data enhancement.The suggestion should highlight the importance of machine learning in handling large data sets in environmental and IoT settings, which directly relates to the use of complex machine learning models with extensive satellite data. It is suggested that the authors refer from the paper: “Automated Machine Learning to Streamline Data-Driven Industrial Application Development”. This reference discusses the role of Automated Machine Learning (AutoML), including hyperparameter optimization, model selection, and feature extraction. The paper uses selected input variables and hyperparameter tuning in Random Forest, enhancing understanding of this efficient machine learning approach with SHAP feature selection for data-heavy applications. It supports the conclusion on feature optimization for better efficiency and frames the precipitation model development as a key step toward effective, data-driven applications.

6. Is recommended to Include the reference from paper titled “AI-Driven Mission-Critical Software Optimization for Small Satellites: Integrating an Automated Testing Framework” in the Discussion. This reference is relevant when discussing the need for efficient and optimized models for real-time applications. These findings are expected to enhance the computational efficiency of real-time satellite-based precipitation estimation, which is crucial for developing high-resolution precipitation estimation algorithms using geostationary satellite data.

Reviewer #2: Review of Disentangling key cloud microphysical drivers of precipitation using machine learning on geostationary satellite data

By Choi et al.

This manuscript presents an interesting analysis that merges geostationary satellite observations with the IMERG precipitation dataset using machine learning to determine which observables from geostationary satellites are the strongest predictors of light and heavy precipitation. This is interesting research and presents novel findings that could be useful to the field. However, I think that the current manuscript lacks sufficient scoping of the topic, justification of the methods, and introduction of the relevant concepts. Please see my individual comments below.

General comments

1. I think that the random forest model for precipitation estimation may work. However, this method requires much more justification. Why use random forest instead of other methods? Additionally, there needs to be evidence that this random forest is performing well. Some form of validation of the model needs to be shown early in the manuscript. Otherwise the reader does not know if the results are physical or a spurious outcome of a poorly fitting model.

2. I think the manuscript title and some of the discussion therein is somewhat misleading. Microphysical cloud properties are rarely mentioned throughout the manuscript, radiance, optical thickness, cloud top temperature, IWP, etc. would be more accurately described as macrophysical cloud properties. I suggest the authors either clarify their definition of microphysics or alter the title and discussion.

3. The IMERG precipitation is widely used, however does suffer some limitations. I suggest the authors add some material discussing the performance of IMERG in their regions of interest to validate its accuracy. This could include a discussion of previous work that has compared IMERG to other observation systems, or a new comparison between IMERG and surface-based observations.

Specific comments

1. In the abstract reflectance are mentioned in the R_xx notation introduced later in the manuscript, but this notation is not defined. I suggest either adding its definition or broadening the language in the abstract such that it does not rely on the specific variable.

2. Line 49: to retrieve -> retrieval of

3. Line 50-51: the sentence “These data … is important” is an unjustified statement and is very difficult to interpret.

4. Line 88: I am not sure if “data collection” is the right section title as no new data was collected for this study. It could be revised to simply “data”.

5. Lines 93-94: The sentence “The reflectance … this study” is redundant with the opening sentence of the paragraph.

6. Line 104: cloud effective radius -> cloud droplet effective radius.

7. Line 178 and elsewhere: I am not familiar with the phrase “microphysically mature” clouds. This needs to be introduced somewhere to clarify what the authors mean here.

8. Line 181-183: I find this a bit confusing. A new concept is being introduced in the results section, but then not really mentioned later. I suggest the authors either move this material to the introduction then relate back to it in the discussion.

9. Line 196: itself -> themselves.

10. Line 204: I do not like the phrasing “large moisture” instead it would read better rephrased to say “moist environment”.

11. Line 252-253: It is somewhat unsurprising the land-ocean temperature contrast is an effective predictor of precipitation in the TWP. The East Asian monsoon is a well-documented weather phenomenon and should be discussed here.

12. Line 335: The word “are” can be deleted.

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Reviewer #1: No

Reviewer #2: Yes: Travis AerensonTravis Aerenson

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PCLM-D-25-00428R1

Disentangling key cloud properties for precipitation retrievals from geostationary satellite data using machine learning

PLOS Climate

Dear Dr. Choi,

Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

As highlighted by the two reviewers, the authors must:

1. Provide validation of the random forest must be included in this manuscript or a supplement to it.

2. Rectify all remaining grammatical errors and ensure the manuscript strictly adheres to APA formatting requirements.

Please submit your revised manuscript by Mar 13 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at climate@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pclm/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Jingyu Wang

Academic Editor

PLOS Climate

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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publication criteria ? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->?>

Reviewer #1: Yes

Reviewer #2: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?-->?>

Reviewer #1: Yes

Reviewer #2: I don't know

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4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??>

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: I recommend accepting the manuscript. The authors have carefully revised the text and figures to include the suggestions from the initial review. They have addressed all technical questions and concerns raised by the reviewers with clear explanations and additional analyses. This version is a well-polished, scientifically sound contribution that meets the journal’s standards.

1. The authors have provided a detailed response to each point from the previous review, showing their commitment to scientific accuracy by adding important definitions and fixing earlier language issues.

2. The revised manuscript clearly explains the difference between cloud microphysical and macrophysical properties, ensuring that the terminology is accurate and aligns with current atmospheric science standards.

3. The authors justify their choice of the Random Forest model and explain how it compares to other common modeling methods.

4. This study presents a new and relevant use of understandable techniques for satellite-based precipitation retrievals, successfully connecting statistical performance with physical interpretability in different climate conditions.

5. The discussion of the GPM IMERG dataset’s performance and limitations in the specific study areas is more thorough, providing a realistic context for the findings.

6. The overall quality of the presentation has improved through better grammar, improved organization of the discussion sections, and clearer figures that accurately represent the main predictors.

Corrections:

1. The authors must rectify all remaining grammatical errors and ensure the manuscript strictly adheres to APA formatting requirements

2. Furthermore, the authors are prepared to finalize the APA formatting of the bibliography—specifically for reference [56], some typo errors like “editors” —and to resolve any remaining minor typographical errors. Given the robustness of the findings and the clarity of the updated text, I believe the paper is ready for publication and requires no further major revisions.

Reviewer #2: 1. In response to my previous comment on the justification of using the random forest model the authors state “a preliminary test in preparation of this study showed that the RF model yields the highest performance among various ML models employed in previous studies (not shown)”. I am glad to hear that such analysis was performed as it should provide ample justification for using a random forest, however this work, and in general validation of the random forest must be included in this manuscript or a supplement to it. I generally do not find it acceptable to reference results that are not provided to the reader. As such, I cannot recommend this paper for publication until the primary methods included are further justified and supported.

Otherwise, the authors have sufficiently addressed my previous comments.

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what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy .-->

Reviewer #1: No

Reviewer #2: No

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Disentangling key cloud properties for precipitation retrievals from geostationary satellite data using machine learning

PCLM-D-25-00428R2

Dear Professor Choi,

We are pleased to inform you that your manuscript 'Disentangling key cloud properties for precipitation retrievals from geostationary satellite data using machine learning' has been provisionally accepted for publication in PLOS Climate.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Climate.

Best regards,

Jingyu Wang

Academic Editor

PLOS Climate

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Additional Editor Comments (if provided):

Reviewer Comments (if any, and for reference):