Predictive verification for the design of partially exchangeable multi-model ensembles
dc.contributor.author | Ben Bouallegue, Z | |
dc.contributor.author | Ferro, C | |
dc.contributor.author | Leutbecher, M | |
dc.contributor.author | Richardson, D | |
dc.date.accessioned | 2019-09-30T14:02:54Z | |
dc.date.issued | 2019-12-18 | |
dc.description.abstract | The performance of an ensemble forecast, as measured by scoring rules, depends on its number of members. Under the assumption of ensemble member exchangeability, ensemble-adjusted scores provide unbiased estimates of the ensemble-size effect. In this study, the concept of ensemble-adjusted scores is revisited and exploited in the general context of multi-model ensemble forecasting. In particular, an ensemblesize adjustment is proposed for the continuous ranked probability score in a multi-model ensemble setting. The method requires that the ensemble forecasts satisfy generalized multi-model exchangeability conditions. These conditions do not require the models themselves to be exchangeable. The adjusted scores are tested here on a dual-resolution ensemble, an ensemble which combines members drawn from the same numerical model but run at two different grid resolutions. It is shown that performance of different ensemble combinations can be robustly estimated based on a small subset of members from each model. At no additional cost, the ensemble-size effect is investigated not only considering the pooling of potential extra-members but also including the impact of optimal weighting strategies. With simple and efficient tools, the proposed methodology paves the way for predictive verification of multi-model ensemble forecasts; the derived statistics can provide guidance for the design of future operational ensemble configurations without having to run additional ensemble forecast experiments for all the potential configurations. | en_GB |
dc.identifier.citation | Vol. 72 (1), pp. 1-12 | en_GB |
dc.identifier.doi | 10.1080/16000870.2019.1697165 | |
dc.identifier.uri | http://hdl.handle.net/10871/38982 | |
dc.language.iso | en | en_GB |
dc.publisher | Taylor & Francis / Co-Action Publishing | en_GB |
dc.rights | © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. | |
dc.title | Predictive verification for the design of partially exchangeable multi-model ensembles | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-09-30T14:02:54Z | |
dc.identifier.issn | 0280-6495 | |
dc.description | This is the final version. Available on open access from Taylor & Francis via the DOI in this record | en_GB |
dc.identifier.journal | Tellus A: Dynamic Meteorology and Oceanography | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | en_GB |
dcterms.dateAccepted | 2019-09-30 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2019-09-30 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-09-30T08:59:46Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2020-02-12T15:51:52Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.