Technical Note: Temporal disaggregation of spatial  rainfall fields with generative adversarial networks

Scher, Sebastian; Peßenteiner, Stefanie

doi:https://doi.org/10.5194/hess-25-3207-2021

Articles | Volume 25, issue 6

https://doi.org/10.5194/hess-25-3207-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-3207-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 6

Technical note

|

11 Jun 2021

Technical note |

| 11 Jun 2021

Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks

Sebastian Scher and Stefanie Peßenteiner

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Final revised paper (published on 11 Jun 2021)
Preprint (discussion started on 30 Sep 2020)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review', Jussi Leinonen, 14 Dec 2020
- SC1: 'Short reply', Sebastian Scher, 17 Dec 2020
- AC1: 'Reply on RC1', Sebastian Scher, 10 Feb 2021
RC2: 'Review', Anonymous Referee #2, 15 Jan 2021
- AC2: 'Reply on RC2', Sebastian Scher, 10 Feb 2021

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 Feb 2021) by Nadav Peleg

AR by Sebastian Scher on behalf of the Authors (25 Mar 2021) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (30 Mar 2021) by Nadav Peleg

RR by Daniele Nerini (08 Apr 2021)

ED: Publish as is (09 Apr 2021) by Nadav Peleg

AR by Sebastian Scher on behalf of the Authors (16 Apr 2021) Author's response

Short summary

In hydrology, it is often necessary to infer from a daily sum of precipitation a possible distribution over the day – for example how much it rained in each hour. In principle, for a given daily sum, there are endless possibilities. However, some are more likely than others. We show that a method from artificial intelligence called generative adversarial networks (GANs) can learn what a typical distribution over the day looks like.