Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)

Lim, Sujeong; Gim, Hyeon-Ju; Lee, Ebony; Lee, Seungyeon; Lee, Won Young; Lee, Yong Hee; Cassardo, Claudio; Park, Seon Ki

doi:https://doi.org/10.5194/gmd-15-8541-2022

Articles | Volume 15, issue 22

https://doi.org/10.5194/gmd-15-8541-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-15-8541-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 22

Development and technical paper

|

22 Nov 2022

Development and technical paper |

| 22 Nov 2022

Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)

Sujeong Lim, Hyeon-Ju Gim, Ebony Lee, Seungyeon Lee, Won Young Lee, Yong Hee Lee, Claudio Cassardo, and Seon Ki Park

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Final revised paper (published on 22 Nov 2022)
Preprint (discussion started on 19 Oct 2021)

Interactive discussion

Status: closed

CEC1:
'Comment on gmd-2021-333', Juan Antonio Añel, 20 Nov 2021

Dear authors,
After checking your manuscript, it has come to our attention that it does not comply with our Code and Data Policy.

https://www.geoscientific-model-development.net/policies/code_and_data_policy.html

You include several links in the Code Availability and Data Availability sections. However, they are not suitable repositories. The web pages you link do not assure long-term archival of the code necessary to reproduce your work. Therefore, we can not accept them. Both in the case of the Noah-LSM model and the Genetic Algorithm, you could want kindly ask the developers to storage the versions used in your work in one of the repositories that we can accept. Or you could offer to do it for them, given that they make it freely downloadable.

One additional problem is that both codes do not have a license listed. The Genetic Algorithm code contains kind of a copyright statement, but it does not address many issues. Despite what many developers think, if a software piece does not include a license, the code continues to be their property, and others cannot use it.

Therefore, you could ask the developers to include a license with their code, or at least for the GA to choose 'Other'. For licenses, we recommend the GPLv3. You only need to include the file 'https://www.gnu.org/licenses/gpl-3.0.txt' as LICENSE.txt with your code. Also, you can choose other options that Zenodo provides: GPLv2, Apache License, MIT License, etc.

Similarly, for the data used, we would like to request that you store in a permanent repository (including DOI) the final input data (not generic reanalysis data) and output files.

In this way, before the Discussions stage is closed, you must reply to this comment with the link to the repository for the code and the corresponding DOI. Also, you must include in a potential reviewed version of your manuscript the modified 'Code and Data Availability' section and the DOI of the code.

Please, reply as soon as possible to this comment with the link for it.
Juan A. Añel

Geosc. Mod. Dev. Executive Editor

Citation: https://doi.org/10.5194/gmd-2021-333-CEC1
- AC1: 'Reply on CEC1', Sujeong Lim, 12 Jan 2022
  
  Dear Chief Editor,
  
  We appreciate your comments. We have revised the Code and Data Availability section following your suggestion.
  As for the revised Code and Data Availability, please find the supplement file.
  
  The exact versions of both the Noah Land Surface Model (Noah LSM) and the genetic algorithm (GA) codes, including the DOI numbers, are available at the Zenodo (https://doi.org/10.5281/zenodo.5777821). This repository also includes the input and output files of the Noah LSM and the GA along with the scripts to plot the same figures as shown in the manuscript.
  
  Regarding the license issue, the GA mentioned “copyright” in the code, saying, “Copyright David L. Carroll; this code may not be reproduced for sale or for use in part of another code for sale without the express written permission of David L. Carroll.” Since we use the code for non-commercial purpose (i.e., not “for sale”), we assume that there should be no copyright issue regarding our paper. We hope the repository of code and data above with DOI and license (for files) can help to cover the license issue. A recent article published in GMD by our group also used the Zenode to secure the exact codes and data including DOI (see Park, S. and Park, S. K.: A micro-genetic algorithm (GA v1.7.1a) for combinatorial optimization of physics parameterizations in the Weather Research and Forecasting model (v4.0.3) for quantitative precipitation forecast in Korea, Geosci. Model Dev., 14, 6241–6255, https://doi.org/10.5194/gmd-14-6241-2021, 2021.).
  Thank you very much.
  
  Sincerely,
  Sujeong Lim
  
  Citation: https://doi.org/10.5194/gmd-2021-333-AC1
RC1:
'Comment on gmd-2021-333', Anonymous Referee #1, 02 Feb 2022

The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2021-333/gmd-2021-333-RC1-supplement.pdf

Citation: https://doi.org/10.5194/gmd-2021-333-RC1
- AC2: 'Reply on RC1', Sujeong Lim, 10 Mar 2022
  
  Please find atteched our detailed response.
  
  Citation: https://doi.org/10.5194/gmd-2021-333-AC2
RC2:
'Comment on gmd-2021-333', Anonymous Referee #2, 21 Feb 2022
Comments on the Manuscript ID gmd-2021-333, entitled "Optimization of Snow-Related

Parameters in Noah Land Surface Model (v3.4.1) Using Micro-Genetic

Algorithm (v1.7a)"

This study worked on determining the optimal parameter values in the snow-related processes – snow cover fraction, snow albedo, and snow depth – of the Noah LSM, using the micro-genetic algorithm and the in-situ surface observations and remotely-sensed satellite data. The study area was South Korea. This manuscript does not have sufficient elements on the model development, it is rather a study of applying a certain optimization algorithm to calibrate the model parameters. I have doubts about the novelty of this manuscript and its suitability for consideration for publication in Geoscientific Model Development. Below are some comments which I hope could help improve the manuscript.

Short Introduction and unclear novelty of this study. The introduction is rather short and the novelty of this study is not explicitly stated.

Insufficient details on the methods/procedures. Section 2.2 and Table 2 miss necessary details on the selected parameters and settings for the different experiments.

I advise the authors to add more figures to show the comparison, via scatter plot, time series plot to show the modelling results in different perspectives. Besides the RMSE value, what about the performance of the model in terms of other commonly used metrics such as R or R2 value?

Results need more description and particularly figures. I advise adding more figures on the modelling results, and particularly representing the spatial patterns of modeling results. The author studied South Korea, readers are interested in the spatial distribution of model performance.

Discussion is completely missing. The current manuscript has no discussion. I strongly advise the authors to compare their findings with existing literature. In addition, what are the limitation of the study? And any potential solutions for future studies? What are effects of some settings or input on the modelling results? Lots of aspects need to be discussed.
Citation: https://doi.org/10.5194/gmd-2021-333-RC2
- AC3: 'Reply on RC2', Sujeong Lim, 10 Mar 2022
  
  Please find atteched our detailed response.
  
  Citation: https://doi.org/10.5194/gmd-2021-333-AC3

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Sujeong Lim on behalf of the Authors (22 Apr 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (28 Apr 2022) by Yuefei Zeng

RR by Anonymous Referee #3 (18 May 2022)

RR by Anonymous Referee #1 (24 May 2022)

ED: Reconsider after major revisions (26 May 2022) by Yuefei Zeng

AR by Sujeong Lim on behalf of the Authors (22 Jul 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (23 Jul 2022) by Yuefei Zeng

ED: Publish subject to minor revisions (review by editor) (06 Sep 2022) by Yuefei Zeng

AR by Sujeong Lim on behalf of the Authors (26 Sep 2022) Author's response Author's tracked changes Manuscript

ED: Publish as is (27 Sep 2022) by Yuefei Zeng

AR by Sujeong Lim on behalf of the Authors (09 Oct 2022) Author's response Manuscript

Post-review adjustments

AA: Author's adjustment | EA: Editor approval

AA by Sujeong Lim on behalf of the Authors (16 Nov 2022) Author's adjustment Manuscript

EA: Adjustments approved (17 Nov 2022) by Yuefei Zeng

Short summary

The land surface model (LSM) contains various uncertain parameters, which are obtained by the empirical relations reflecting the specific local region and can be a source of uncertainty. To seek the optimal parameter values in the snow-related processes of the Noah LSM over South Korea, we have implemented an optimization algorithm, a micro-genetic algorithm using the observations. As a result, the optimized snow parameters improve snowfall prediction.