Systematic error analysis of heavy-precipitation-event prediction using a 30-year hindcast dataset

Ponzano, Matteo; Joly, Bruno; Descamps, Laurent; Arbogast, Philippe

doi:https://doi.org/10.5194/nhess-20-1369-2020

Articles | Volume 20, issue 5

https://doi.org/10.5194/nhess-20-1369-2020

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

https://doi.org/10.5194/nhess-20-1369-2020

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

Articles | Volume 20, issue 5

Research article

|

20 May 2020

Research article |

| 20 May 2020

Systematic error analysis of heavy-precipitation-event prediction using a 30-year hindcast dataset

Matteo Ponzano, Bruno Joly, Laurent Descamps, and Philippe Arbogast

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Final revised paper (published on 20 May 2020)
Preprint (discussion started on 25 Sep 2019)

Interactive discussion

Status: closed

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

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RC1: 'Ref comment "Systematic errors analysis of heavy precipitating events prediction using a 30-year hndcast dataset"', Anonymous Referee #1, 28 Oct 2019
- AC1: 'Response to reviewer #1', Matteo Ponzano, 26 Jan 2020
RC2: 'Interesting but added value not clear', Anonymous Referee #2, 31 Oct 2019
- AC2: 'Response to reviewer #2', Matteo Ponzano, 26 Jan 2020

Peer-review completion

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

ED: Reconsider after major revisions (further review by editor and referees) (31 Jan 2020) by Joaquim G. Pinto

AR by Matteo Ponzano on behalf of the Authors (10 Feb 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (19 Feb 2020) by Joaquim G. Pinto

RR by Anonymous Referee #1 (20 Feb 2020)

RR by Anonymous Referee #2 (03 Mar 2020)

ED: Publish subject to minor revisions (review by editor) (05 Mar 2020) by Joaquim G. Pinto

AR by Matteo Ponzano on behalf of the Authors (13 Mar 2020) Author's response Manuscript

ED: Publish as is (17 Mar 2020) by Joaquim G. Pinto

AR by Matteo Ponzano on behalf of the Authors (19 Mar 2020)

Short summary

We assess a methodology to evaluate and improve intense precipitation forecasting in the southeastern French region. This methodology is based on the use of a 30-year dataset of past forecasts which are analysed using a spatial verification approach. We found that precipitation forecasting is qualitatively driven by the deep-convection parametrization. Locally the model is able to reproduce the distribution of spatially integrated rainfall patterns of the most intense precipitation.