Bias Correction Methods Applied to Satellite Rainfall Products over the Western Part of Saudi Arabia

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Manuscript Title: “Assessment of Satellite Precipitation Products and Bias Correction Methods Over the Western Part of Saudi Arabia" (Manuscript ID: atmosphere- 3656117)

 

Overview:

Thank you for the opportunity to review the manuscript titled “Assessment of Satellite Precipitation Products and Bias Correction Methods Over the Western Part of Saudi Arabia” (Manuscript ID: atmosphere-3656117). This work addresses a timely and important topic, evaluating the accuracy of five satellite-based precipitation (SBP) products—GPM, GPCP, CHIRPS, PERSIANN-CDR, and PERSIANN—using ground observations from 28 rain gauge stations. The study also investigates the effectiveness of different bias correction methods, with Artificial Neural Network (ANN) emerging as the most effective.

The research is relevant for hydrological studies in arid regions; however, several critical aspects need clarification and improvement. These include the interpolation methodology, the basis for correlation analysis, and the limited temporal scale (monthly only) used in performance evaluation. The manuscript would benefit from a more transparent and rigorous methodological approach and a broader temporal validation (e.g., seasonal, annual).

Furthermore, a 25% similarity index from the iThenticate report is concerning and should be addressed through proper citation or rewriting to avoid potential ethical issues.

Recommendation: Reject (Major Revision Required)
At this stage, I recommend rejecting the manuscript. However, the authors are encouraged to revise and resubmit after addressing the concerns and incorporating the 10 detailed comments provided in the annotated PDF. This will significantly improve the manuscript’s scientific quality and clarity.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

1. Inconsistent figure numbering and descriptions (e.g., references to Figure 2 on page 9 and Figure 5 on page 11 conflict with the text’s context). Please confirm the figures and tables are numbered sequentially and referenced accurately. Verify that all figures (e.g., spatial rainfall distributions, scatterplots) are correctly labeled and described in the text.
2. While the use of 35 hidden nodes via trial-and-error is noted, provide additional details on validation techniques (e.g., cross-validation) and measures taken to prevent overfitting (e.g., dropout layers, early stopping).
3. pleaseclarify whether bias correction was applied station-wise or regionally. Include a flowchart or pseudocode for ANN implementation to improve reproducibility.
4. The Abstract reports a correlation coefficient (CC) of 0.56 for GPM, while the Conclusion states CC = 0.27. Similar inconsistencies exist for RMSE values. Please confirm.
5. Reconcile these values across sections. Specify whether metrics in the Conclusion are post-correction values or refer to a different temporal scale (e.g., annual vs. monthly).
6. Inconsistent use of "CDR" vs. "PERSIANN-CDR" (e.g., Table 1 lists "CDR" as a separate product, but text refers to "PERSIANN-CDR"). Standardize terminology (e.g., always use "PERSIANN-CDR") to avoid confusion.
7. Discuss how Makkah’s topography (e.g., elevation gradients, proximity to the Red Sea) specifically impacts SBP performance. Reference DEM data (Figure 1) to explain spatial rainfall variability.
8. Elaborate on why ANN outperforms traditional methods (e.g., nonlinear relationships captured, adaptability to sparse data). Compare with studies that used ANN for similar arid regions.
9. Expand on factors causing SBP overestimation (e.g., ground-truth scarcity, cloud-top temperature misestimation in IR-based products).
10. please highlight how corrected GPM data can directly aid water resource management, flood forecasting, or agricultural planning in Saudi Arabia.
11. I suggest integrating multi-source data (e.g., radar, reanalysis) or testing hybrid models (e.g., ANN coupled with physical models).
12. Maybe for the abstract:specify the study period.
13. Table 1:Clarify temporal resolution for GPCP (listed as "monthly" but referenced as daily in text).
14. For the references:please update citations where possible (e.g., ensure post-2020 studies are included for ML applications).

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Over the manuscript I have added some comments and suggestions. More analysis will be done related with the results obtained. Also in the introduction and methodological definition a deeper explanation of the region of study and about the limitations of the satellite products should be done. Moreover a review of the format and proper adaptation of the template is required.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Suggestions for improvement the manuscript have been incorporated in the revision file.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have addressed all comments scientifically and satisfactorily. The manuscript is suitable for acceptance.

Reviewer 2 Report

Comments and Suggestions for Authors

No further comments. Recommend to accept.

Reviewer 3 Report

Comments and Suggestions for Authors

Thanks for the changes done.