Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

Jha, Sanjeev K.; Shrestha, Durga L.; Stadnyk, Tricia A.; Coulibaly, Paulin

doi:https://doi.org/10.5194/hess-22-1957-2018

Articles | Volume 22, issue 3

https://doi.org/10.5194/hess-22-1957-2018

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

https://doi.org/10.5194/hess-22-1957-2018

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

Articles | Volume 22, issue 3

Research article

|

23 Mar 2018

Research article |

| 23 Mar 2018

Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

Sanjeev K. Jha, Durga L. Shrestha, Tricia A. Stadnyk, and Paulin Coulibaly

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Final revised paper (published on 23 Mar 2018)
Supplement to the final revised paper
Preprint (discussion started on 07 Aug 2017)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Referee Comments', Fabio Oriani, 14 Sep 2017
- AC1: 'Reply to comments of Dr. Oriani and accordingly modified manuscript and figures', Sanjeev Jha, 09 Jan 2018
RC2: 'Evaluation of ensemble precipitation forecasts generated through postprocessing in a Canadian catchment', Geoff Pegram, 18 Oct 2017
- AC2: 'Reply to comments of Prof. G. Pegram', Sanjeev Jha, 09 Jan 2018
- AC1: 'Reply to comments of Dr. Oriani and accordingly modified manuscript and figures', Sanjeev Jha, 09 Jan 2018

Peer-review completion

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

ED: Publish subject to revisions (further review by editor and referees) (23 Jan 2018) by Luis Samaniego

AR by Sanjeev Kumar Jha on behalf of the Authors (31 Jan 2018) Author's response Manuscript

ED: Publish as is (09 Feb 2018) by Luis Samaniego

Short summary

The output from numerical weather prediction (NWP) models is known to have errors. River forecast centers in Canada mostly use precipitation forecasts directly obtained from American and Canadian NWP models. In this study, we evaluate the forecast performance of ensembles generated by a Bayesian post-processing approach in cold climates. We demonstrate that the post-processing approach generates bias-free forecasts and provides a better picture of uncertainty in the case of an extreme event.