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dc.contributor.authorManzanas, Rodrigo
dc.contributor.authorTorralba, Verónica
dc.contributor.authorLledó, Llorenç
dc.contributor.authorBretonnière, Pierre-Antoine
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2022-09-28T10:14:49Z
dc.date.available2022-09-28T10:14:49Z
dc.date.issued2022-09
dc.identifier.citationManzanas, R. [et al.]. On the reliability of global seasonal forecasts: sensitivity to ensemble size, hindcast length and region definition. "Geophysical Research Letters", Setembre 2022, vol. 49, núm. 17, e2021GL094662.
dc.identifier.issn0094-8276
dc.identifier.issn1944-8007
dc.identifier.urihttp://hdl.handle.net/2117/373553
dc.description.abstractOne of the key quality aspects in a probabilistic prediction is its reliability. However, this property is difficult to estimate in the case of seasonal forecasts due to the limited size of most of the hindcasts that are available nowadays. To shed light on this issue, this work presents a detailed analysis of how the ensemble size, the hindcast length and the number of points pooled together within a particular region affect the resulting reliability estimates. To do so, we build on 42 land reference regions recently defined for the IPCC-AR6 and assess the reliability of global seasonal forecasts of temperature and precipitation from the European Center for Medium Weather Forecasts SEAS5 prediction system, which is compared against its predecessor, System4. Our results indicate that whereas longer hindcasts and larger ensembles lead to increased reliability estimates, the number of points that are pooled together within a homogeneous climate region is much less relevant.
dc.description.sponsorshipThis research has been partially supported by the AfriCultuReS (“Enhancing Food Security in African Agricultural Systems with the Support of Remote Sensing”) and FOCUS-Africa projects, which received funding from the European Union's Horizon 2020 Research and Innovation Framework Programme under grant agreements No. 77465 and 869575, respectively.
dc.language.isoeng
dc.publisherWiley
dc.relation.urihttps://agupubs.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1029%2F2021GL094662&file=2021GL094662-sup-0001-Supporting+Information+SI-S01.pdf
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectÀrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia
dc.subject.lcshWeather forecasting
dc.subject.lcshClimate variations
dc.subject.otherSeasonal climate forecasts
dc.subject.otherProbabilistic prediction
dc.subject.otherReliability
dc.subject.otherEuropean Center for Medium Weather Forecasts
dc.titleOn the reliability of global seasonal forecasts: sensitivity to ensemble size, hindcast length and region definition
dc.typeArticle
dc.subject.lemacSimulació per ordinador
dc.identifier.doi10.1029/2021GL094662
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021GL094662
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/774652/EU/Enhancing Food Security in AFRIcan AgriCULTUral Systems with the Support of REmote Sensing/AfriCultuReS
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/869575/EU/Full-value chain Optimised Climate User-centric Services for Southern Africa: FOCUS-Africa/FOCUS-Africa
local.citation.othere2021GL094662
local.citation.publicationNameGeophysical Research Letters
local.citation.volume49
local.citation.number17
dc.relation.datasetData Availability Statement SEAS5 data was retrieved from the MARS archive (https://confluence.ecmwf.int/display/COPSRV/MARS+archive) following the ECMWF data policy. CRU TS v4.04 was downloaded from https://catalogue.ceda.ac.uk/uuid/89e1e34ec3554dc98594a5732622bce9. Differently, System4 and EWEMBI were obtained from the User Data Gateway (UDG), a THREDDS-based service from the Santander Climate Data Service that provides access to a wide catalogue of popular climate datasets: http://meteo.unican.es/udg-tap/home. See Cofiño et al. (2018) for further information. Finally, reliability categories can be computed (and plotted) with the function reliabilityCategories included in the visualizeR package (Frías et al., 2018), which forms part of climate4R (Iturbide et al., 2019), a bundle of R packages developed by the Santander Meteorology Group for transparent climate data access, post-processing and visualization.


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