Ability of an Australian reanalysis dataset to characterise sub-daily precipitation

Acharya, Suwash Chandra; Nathan, Rory; Wang, Quan J.; Su, Chun-Hsu; Eizenberg, Nathan

doi:https://doi.org/10.5194/hess-24-2951-2020

Articles | Volume 24, issue 6

https://doi.org/10.5194/hess-24-2951-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-24-2951-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 6

Research article

|

04 Jun 2020

Research article |

| 04 Jun 2020

Ability of an Australian reanalysis dataset to characterise sub-daily precipitation

Suwash Chandra Acharya, Rory Nathan, Quan J. Wang, Chun-Hsu Su, and Nathan Eizenberg

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Final revised paper (published on 04 Jun 2020)
Preprint (discussion started on 03 Sep 2019)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review of hess-2019-432', Anonymous Referee #1, 08 Jan 2020
- AC1: 'Authors' response', Suwash Acharya, 11 Mar 2020
RC2: 'Review of Acharya et al. HESS-2019-432', Anonymous Referee #2, 10 Feb 2020
- AC1: 'Authors' response', Suwash Acharya, 11 Mar 2020

Peer-review completion

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

ED: Publish subject to minor revisions (further review by editor) (04 Apr 2020) by Micha Werner

AR by Suwash Acharya on behalf of the Authors (14 Apr 2020) Author's response Manuscript

ED: Publish subject to technical corrections (02 May 2020) by Micha Werner

AR by Suwash Acharya on behalf of the Authors (07 May 2020) Author's response Manuscript

Short summary

BARRA is a high-resolution reanalysis dataset over the Oceania region. This study evaluates the performance of sub-daily BARRA precipitation at point and spatial scales over Australia. We find that the dataset reproduces some of the sub-daily characteristics of precipitation well, although it exhibits some spatial displacement errors, and it performs better in temperate than in tropical regions. The product is well suited to complement other estimates derived from remote sensing and rain gauges.