Beyond univariate calibration: verifying spatial structure in ensembles of forecast fields

Jacobson, Josh; Kleiber, William; Scheuerer, Michael; Bellier, Joseph

doi:https://doi.org/10.5194/npg-27-411-2020

Articles | Volume 27, issue 3

https://doi.org/10.5194/npg-27-411-2020

© 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-27-411-2020

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

Articles | Volume 27, issue 3

Research article

|

31 Aug 2020

Research article |

| 31 Aug 2020

Beyond univariate calibration: verifying spatial structure in ensembles of forecast fields

Josh Jacobson, William Kleiber, Michael Scheuerer, and Joseph Bellier

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Final revised paper (published on 31 Aug 2020)
Preprint (discussion started on 20 Jan 2020)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review of your manuscript', Anonymous Referee #1, 03 Feb 2020
- AC1: 'Response to referees', Joshuah Jacobson, 15 Jul 2020
RC2: 'Review', Anonymous Referee #2, 17 Jun 2020
- AC1: 'Response to referees', Joshuah Jacobson, 15 Jul 2020
AC1: 'Response to referees', Joshuah Jacobson, 15 Jul 2020

Peer-review completion

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

AR by Josh Jacobson on behalf of the Authors (15 Jul 2020) Author's response Manuscript

ED: Publish as is (21 Jul 2020) by Maxime Taillardat

AR by Josh Jacobson on behalf of the Authors (24 Jul 2020)

Short summary

Most verification metrics for ensemble forecasts assess the representation of uncertainty at a particular location and time. We study a new diagnostic tool based on fractions of threshold exceedance (FTE) which evaluates an additional important attribute: the ability of ensemble forecast fields to reproduce the spatial structure of observed fields. The utility of this diagnostic tool is demonstrated through simulations and an application to ensemble precipitation forecasts.