Dealing with non-stationarity in sub-daily stochastic rainfall models

Benoit, Lionel; Vrac, Mathieu; Mariethoz, Gregoire

doi:https://doi.org/10.5194/hess-22-5919-2018

Articles | Volume 22, issue 11

https://doi.org/10.5194/hess-22-5919-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-22-5919-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 11

Research article

|

19 Nov 2018

Research article |

| 19 Nov 2018

Dealing with non-stationarity in sub-daily stochastic rainfall models

Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz

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Final revised paper (published on 19 Nov 2018)
Preprint (discussion started on 28 May 2018)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review', Nadav Peleg, 12 Jun 2018
- AC1: 'Response to the comments of Reviewer #1', Lionel Benoit, 26 Jul 2018
RC2: 'Referee Comment', Anonymous Referee #2, 02 Jul 2018
- AC2: 'Reply to the comments of Reviewer #2', Lionel Benoit, 26 Jul 2018

Peer-review completion

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

ED: Reconsider after major revisions (further review by editor and referees) (07 Aug 2018) by Carlo De Michele

AR by Lionel Benoit on behalf of the Authors (14 Sep 2018) Manuscript

ED: Referee Nomination & Report Request started (02 Oct 2018) by Carlo De Michele

RR by Nadav Peleg (09 Oct 2018)

RR by Anonymous Referee #2 (17 Oct 2018)

ED: Publish as is (08 Nov 2018) by Carlo De Michele

AR by Lionel Benoit on behalf of the Authors (09 Nov 2018) Manuscript

Short summary

We propose a method for unsupervised classification of the space–time–intensity structure of weather radar images. The resulting classes are interpreted as rain types, i.e. pools of rain fields with homogeneous statistical properties. Rain types can in turn be used to define stationary periods for further stochastic rainfall modelling. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm.