An assessment of the performance of global rainfall estimates without ground-based observations

Massari, Christian; Crow, Wade; Brocca, Luca

doi:https://doi.org/10.5194/hess-21-4347-2017

Articles | Volume 21, issue 9

https://doi.org/10.5194/hess-21-4347-2017

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

https://doi.org/10.5194/hess-21-4347-2017

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

Articles | Volume 21, issue 9

Research article

|

05 Sep 2017

Research article |

| 05 Sep 2017

An assessment of the performance of global rainfall estimates without ground-based observations

Christian Massari, Wade Crow, and Luca Brocca

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Final revised paper (published on 05 Sep 2017)
Supplement to the final revised paper
Preprint (discussion started on 10 Apr 2017)
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, 18 Apr 2017
- AC1: 'Responses to the comments of reviewer #1: An assessment of the accuracy of global rainfall estimates without ground-based observations', Christian Massari, 08 May 2017
RC2: 'Anonymous Reviewer Comments', Anonymous Referee #2, 25 Apr 2017
- AC2: 'Responses to the comments of reviewer #2: An assessment of the accuracy of global rainfall estimates without ground-based observations', Christian Massari, 17 May 2017
  - RC4: 'My comments have also been addressed.', Anonymous Referee #2, 19 May 2017
RC3: 'Comments have been addressed', Anonymous Referee #1, 17 May 2017

Peer-review completion

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

ED: Publish subject to revisions (further review by Editor and Referees) (11 Jun 2017) by Lixin Wang

AR by Christian Massari on behalf of the Authors (10 Jul 2017) Author's response Manuscript

ED: Referee Nomination & Report Request started (11 Jul 2017) by Lixin Wang

RR by Viviana Maggioni (12 Jul 2017)

RR by Anonymous Referee #2 (31 Jul 2017)

ED: Publish as is (01 Aug 2017) by Lixin Wang

AR by Christian Massari on behalf of the Authors (03 Aug 2017)

Short summary

The paper explores a method for the assessment of the performance of global rainfall estimates without relying on ground-based observations. Thanks to this method, different global correlation maps are obtained (for the first time without relying on a benchmark dataset) for some of the most used globally available rainfall products. This is central for hydroclimatic studies within data-scarce regions, where ground observations are scarce to evaluate the relative quality of a rainfall product