Performance of site-specific parameterizations of longwave radiation

Formetta, Giuseppe; Bancheri, Marialaura; David, Olaf; Rigon, Riccardo

doi:https://doi.org/10.5194/hess-20-4641-2016

Articles | Volume 20, issue 11

https://doi.org/10.5194/hess-20-4641-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-20-4641-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 20, issue 11

Research article

|

18 Nov 2016

Research article |

| 18 Nov 2016

Performance of site-specific parameterizations of longwave radiation

Giuseppe Formetta, Marialaura Bancheri, Olaf David, and Riccardo Rigon

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Final revised paper (published on 18 Nov 2016)
Supplement to the final revised paper
Preprint (discussion started on 31 May 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review comments', Anonymous Referee #1, 20 Jun 2016
- AC1: 'Missing supplementary material', G. Formetta, 21 Jun 2016
- AC2: 'Reply to the reviewer n.1', G. Formetta, 12 Aug 2016
RC2: 'Comment on "Site specific parameterizations of longwave radiation" by G. Formetta et al.', Anonymous Referee #2, 30 Jun 2016
- AC3: 'Reply to the reviewer n.2', G. Formetta, 12 Aug 2016
AC4: 'Complimentary material', G. Formetta, 12 Aug 2016
AC5: 'Complimentary material', G. Formetta, 12 Aug 2016

Peer-review completion

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

ED: Reconsider after major revisions (15 Aug 2016) by Bettina Schaefli

AR by Giuseppe Formetta on behalf of the Authors (21 Sep 2016) Author's response Manuscript

ED: Referee Nomination & Report Request started (22 Sep 2016) by Bettina Schaefli

RR by Anonymous Referee #2 (10 Oct 2016)

Suggestions for revision or reasons for rejection

The organisation of the paper has improved and the methods section is more comprehensive now. However, otherwise many of the issues in the first manuscript version still persist in the revised version, for example:
- Unlike the authors answer (2nd reviewer, A3), Angstrom (1918) still does not appear in the reference list
- The mix of citation styles in both, the text and the reference list
- The reference list is not sorted alphabetically
- Unlike the authors answer (2nd reviewer, A27), Figure 2 still does not show the locations of the tests sites with the correct index. The numbers should be between 1 and 24, and all numbers should be visible

I see four major issues with the manuscript in its current form, which I already mentioned in the first review and I really find important:
- Q31/A31: I really think the figures should be changed in order to allow the reader to (i) see the variability within the classes and (ii) compare the results of all three approaches (original parameters, fitted parameters, and parameters
from regression analysis). These two things are essential for the message of the manuscript and currently not possible.
- Q33/A33: A discussion section would really be helpful
- Q13/A13 (l59): The added value of this study is not clear to me, in particular point 1 ("i) developed a method to systematically compute the site-specific model parameters for location where measurements are available") is clearly wrong. This point should be removed as lots of studies systematically calibrate empirical models, e.g. Carmona et al. (2014) as the authors admit themselves, and also Juszak and Pellicciotti (2013).
- Q5/Q6: The manuscript still does not contain information on which model is best. Furthermore, important results are moved to the supplementary material, which was not available for review. They should be checked carefully. The presence of the table mentioned ("Presenting in a table the value of the regression parameters for each model was not our focus but we added it in a supplementary material.") should be indicated in the text.

Other points to clarify:
l92: What is an hour angle?
l97: What do you mean by "and citation therein"?
Table 2: If you take the literature formulation as in Flerchinger et al., 2009 (mentioned in the answer A19), this should be cited in the table caption
l145: the sentence "which could be compensated by the optimization of the parameters a and b" is not based on an argument
Figure 4: The caption is wrong as now Model 8 is within the range for all but one case.
Figures 4,5,6,8,9: It would be better to rename the latitude and longitude classes, e.g. change 25;30 to 25-30°N
l309: the latitude class name is not intuitive, do you mean between 25°N and 30°N?
Figure 6: The caption does not mention which parameters were taken (original, fitted, or from regression analysis)
Figures 8+9: The captions are equal, the figures are not, mention the difference in the captions

Other issues I would like to mention are:
- Units should not be in italics.
- The English of the changed paragraphs needs proofreading.
- Citation of tables and figures are not consistent: either "table 1" or "Table 1" and never "figure1"

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RR by Anonymous Referee #1 (11 Oct 2016)

ED: Reconsider after major revisions (13 Oct 2016) by Bettina Schaefli

AR by Giuseppe Formetta on behalf of the Authors (21 Oct 2016) Author's response Manuscript

ED: Publish subject to minor revisions (further review by Editor) (28 Oct 2016) by Bettina Schaefli

AR by Giuseppe Formetta on behalf of the Authors (31 Oct 2016) Author's response Manuscript

ED: Publish subject to technical corrections (01 Nov 2016) by Bettina Schaefli

AR by Giuseppe Formetta on behalf of the Authors (08 Nov 2016) Manuscript

Short summary

Ten algorithms for estimating DL and one for UL are integrated in a new model (LWRB) and connected to hydrological model JGrass-NewAge. The algorithms are tested against energy flux measurements available for 24 sites in North America to assess their reliability. We evaluated the performances of simplified models (SMs) of DL, as presented in literature formulations, and determined by automatic calibration the site-specific parameter sets for SMs of DL to improve model predictions.