Advancing land surface model development with satellite-based Earth observations

Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo

doi:https://doi.org/10.5194/hess-21-2483-2017

Articles | Volume 21, issue 5

https://doi.org/10.5194/hess-21-2483-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-21-2483-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 21, issue 5

Research article

|

11 May 2017

Research article |

| 11 May 2017

Advancing land surface model development with satellite-based Earth observations

Rene Orth, Emanuel Dutra, Isabel F. Trigo, and Gianpaolo Balsamo

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Final revised paper (published on 11 May 2017)
Supplement to the final revised paper
Preprint (discussion started on 14 Dec 2016)
Supplement to the preprint

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'review', Anonymous Referee #1, 20 Dec 2016
RC2: 'hess-2016-628', Anonymous Referee #2, 15 Feb 2017
RC3: 'Reviewer's comments to HESS-2016-628 article', Anonymous Referee #3, 28 Feb 2017
AC1: 'Response to Reviewers', Rene Orth, 28 Mar 2017

Peer-review completion

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

ED: Publish subject to minor revisions (further review by Editor) (07 Apr 2017) by Shraddhanand Shukla

AR by Rene Orth on behalf of the Authors (07 Apr 2017) Manuscript

ED: Publish subject to minor revisions (further review by Editor) (13 Apr 2017) by Shraddhanand Shukla

AR by Rene Orth on behalf of the Authors (13 Apr 2017) Manuscript

ED: Publish as is (13 Apr 2017) by Shraddhanand Shukla

AR by Rene Orth on behalf of the Authors (19 Apr 2017)

Short summary

State-of-the-art land surface models (LSMs) rely on poorly constrained parameters. To enhance LSM configuration, new satellite-based Earth observations are essential. This is because multiple observational datasets allow us to assess and validate the representation of coupled processes in LSMs. The resulting improved LSM configuration is beneficial for coupled weather forecasts, and hence valuable to society.