Correcting for model changes in statistical postprocessing – an approach based on response theory

Demaeyer, Jonathan; Vannitsem, Stéphane

doi:https://doi.org/10.5194/npg-27-307-2020

Articles | Volume 27, issue 2

https://doi.org/10.5194/npg-27-307-2020

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

Special issue:

Advances in post-processing and blending of deterministic...

https://doi.org/10.5194/npg-27-307-2020

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

Articles | Volume 27, issue 2

Research article

|

27 May 2020

Research article |

| 27 May 2020

Correcting for model changes in statistical postprocessing – an approach based on response theory

Jonathan Demaeyer and Stéphane Vannitsem

Download

Final revised paper (published on 27 May 2020)
Supplement to the final revised paper
Preprint (discussion started on 21 Nov 2019)
Supplement to the preprint

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

RC1: 'Review', Michael Zamo, 20 Dec 2019
- AC1: 'Response to referee 1', Jonathan Demaeyer, 28 Feb 2020
RC2: 'Correcting for Model Changes in Statistical Post-Processing – An approach based on Response Theory', Anonymous Referee #2, 08 Jan 2020
- AC2: 'Response to referee 2', Jonathan Demaeyer, 28 Feb 2020
AC3: 'Changes in the manuscript', Jonathan Demaeyer, 28 Feb 2020

Peer-review completion

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

AR by Jonathan Demaeyer on behalf of the Authors (22 Mar 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (25 Mar 2020) by Maxime Taillardat

RR by Michael Zamo (11 Apr 2020)

ED: Publish subject to minor revisions (review by editor) (12 Apr 2020) by Maxime Taillardat

AR by Jonathan Demaeyer on behalf of the Authors (17 Apr 2020) Author's response Manuscript

ED: Publish as is (22 Apr 2020) by Maxime Taillardat

Short summary

Postprocessing schemes used to correct weather forecasts are no longer efficient when the model generating the forecasts changes. An approach based on response theory to take the change into account without having to recompute the parameters based on past forecasts is presented. It is tested on an analytical model and a simple model of atmospheric variability. We show that this approach is effective and discuss its potential application for an operational environment.