The Weather On-Demand Framework
Round 1
Reviewer 1 Report
Comments and Suggestions for Authorssee attached document
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 AuthorsThis manuscript introduces the Weather On Demand (WOD) weather forecasting framework, which is a software stack used for running operations and on-demand weather forecasting. The WOD framework can integrate weather forecasts and observational data from multiple sources, use the WRF Chem atmospheric model for data assimilation and forecasting, and provide forecast results and upstream data through APIs. The manuscript also demonstrates the effectiveness of WOD in different application scenarios and discusses its future development, cloud deployment, data assimilation priorities, and the application of machine learning in weather forecasting. Before being considered for acceptance, this manuscript should address and revise the following questions.
1. Although data assimilation is mentioned in the paper, the specific assimilation algorithms and technical details used are not sufficiently described. Suggest adding a detailed introduction to assimilation algorithms such as 3DVAR, ETKF, etc., as well as their specific application methods and advantages in the WOD framework.
2. The paper lacks a detailed description of the WRF model validation and calibration process. Suggest providing methods and results for model validation, including the validation tools used (such as Verify), validation metrics (such as bias, root mean square error, etc.), and performance comparison before and after model calibration.
3. When it comes to machine learning models such as ClimaX, there is a lack of specific evaluation of their performance. Suggest increasing the evaluation of the prediction accuracy, training time, and computational resource consumption of machine learning models, as well as comparing them with traditional NWP models.
4.The case analysis in the paper mainly focuses on the prediction of volcanic ash and SO2 diffusion, hydrological model input, etc. It is suggested to add more types of application cases, such as air quality prediction, wind energy resource assessment, etc., to demonstrate the wide applicability of the WOD framework.
5.The paper briefly mentions cloud deployment, but lacks specific technical details and analysis of its advantages. Suggest delving into the implementation methods, resource allocation strategies, security considerations, and comparison with other deployment methods (such as on premises deployment) of cloud deployment.
6.The quality of observation data is crucial for weather prediction results. Suggest increasing the discussion on the evaluation of observation data quality, including data quality control methods, data missing and anomaly handling strategies, and the impact of data quality on prediction results.
7.The prediction results in the paper are mainly presented through charts, but lack detailed textual analysis. Suggest adding a detailed interpretation of the prediction results, including prediction accuracy, sources of error, and possible directions for improvement.
8.Multiple data sources were used for weather prediction in the paper, but there is a lack of comparative analysis of the impact of different data sources on the prediction results. Suggest adding this section to understand the contribution and limitations of different data sources on prediction accuracy.
9.In the conclusion section, it is recommended to clearly indicate the main directions and challenges for future research, including algorithm optimization, improvement of data assimilation techniques, further application of machine learning models, and integration with other meteorological systems.
10. The conclusion section can provide more specific improvement suggestions and action plans, including a technology research and development roadmap, partner recruitment plan, and future version release plan, to enhance the practicality and guiding significance of the paper.
Comments on the Quality of English LanguageFine
Author Response
Please see the attachment
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsDear authors,
The work presented is actual and of great importance. I only have a few comments:
1. The meaning of the nomenclatures should be included when they are mentioned for the first time (example: WRF, APIs, Git).
2. It would be advisable to address the issue of the current use of the Weather On Demand (WOD) forecasting framework. For example, in the energy sector or others: are some leading companies currently using it? Additionally, how much is it used in research published in recognized journals?
Successes!!!!
Author Response
Please see the attachment
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsThe overall response is clear and the problem has been effectively resolved and responded to. I think the current version can be accepted for publication.
Comments on the Quality of English LanguageFine
