Satellite-based high-resolution mapping of rainfall over southern Africa

Meyer, Hanna; Drönner, Johannes; Nauss, Thomas

doi:https://doi.org/10.5194/amt-10-2009-2017

Articles | Volume 10, issue 6

https://doi.org/10.5194/amt-10-2009-2017

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

https://doi.org/10.5194/amt-10-2009-2017

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

Articles | Volume 10, issue 6

Research article

|

06 Jun 2017

Research article |

| 06 Jun 2017

Satellite-based high-resolution mapping of rainfall over southern Africa

Hanna Meyer, Johannes Drönner, and Thomas Nauss

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Final revised paper (published on 06 Jun 2017)
Preprint (discussion started on 06 Feb 2017)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review of Satellite based high resolution mapping of rainfall over Southern Africa by Hanna Meyer, Johannes Drönner, and Thomas Nauss.', Anonymous Referee #1, 21 Feb 2017
- AC1: 'Response to RC1', Hanna Meyer, 21 Apr 2017
RC2: 'RC#2 - Review of Satellite based high resolution mapping of rainfall over Southern Africa By Hanna Meyer, Johannes Drönner, and Thomas Nauss - Anonymous Referee #2, 05 March 2017', Anonymous Referee #2, 05 Mar 2017
- AC2: 'Response to RC2', Hanna Meyer, 21 Apr 2017

Peer-review completion

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

AR by Hanna Meyer on behalf of the Authors (21 Apr 2017) Author's response Manuscript

ED: Referee Nomination & Report Request started (24 Apr 2017) by Gianfranco Vulpiani

RR by Anonymous Referee #2 (09 May 2017)

ED: Publish as is (09 May 2017) by Gianfranco Vulpiani

AR by Hanna Meyer on behalf of the Authors (09 May 2017)

Short summary

A spatially explicit mapping of rainfall is required for southern Africa but obtaining accurate estimates is still a challenging task. We estimated hourly rainfall based on optical satellite data and neural networks. The results indicated that the majority of rainfall events could be captured by the model, but with a clear tendency to overestimate rainfall. Despite being a comparably simple approach, the presented rainfall retrieval could outperform a complex global rainfall product.