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Article
Peer-Review Record

Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique

Atmosphere 2023, 14(1), 112; https://doi.org/10.3390/atmos14010112
by Ehsan Forootan 1,*, Masood Dehvari 2, Saeed Farzaneh 2 and Sedigheh Karimi 2
Reviewer 1:
Reviewer 2: Anonymous
Atmosphere 2023, 14(1), 112; https://doi.org/10.3390/atmos14010112
Submission received: 12 December 2022 / Revised: 29 December 2022 / Accepted: 30 December 2022 / Published: 4 January 2023
(This article belongs to the Special Issue Advanced GNSS for Severe Weather Events and Climate Monitoring)

Round 1

Reviewer 1 Report

It is really interesting work to apply machine learning in improving wet estimation. Except minor editorial work, I can say the current work is one step ahead on estimation of wet refractivity.

Author Response

Thanks for your very positive feedback. This encourages us to continue our research in this direction.

Reviewer 2 Report

Please see the attached file for comments

Comments for author File: Comments.pdf

Author Response

A file is uploaded containing our comments and actions.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The authors have done a good job in revising and improving the manuscript, and can be accepted in it's present form.

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