Articles | Volume 20, issue 5
https://doi.org/10.5194/nhess-20-1369-2020
https://doi.org/10.5194/nhess-20-1369-2020
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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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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
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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.
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