dc.contributor.author | Pérez Zanón, Núria |
dc.contributor.author | Caron, Louis-Philippe |
dc.contributor.author | Terzago, Silvia |
dc.contributor.author | Van Schaeybroeck, Bert |
dc.contributor.author | Lledó, Llorenç |
dc.contributor.author | Bretonnière, Pierre-Antoine |
dc.contributor.author | Delgado Torres, Carlos |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2022-08-05T12:57:36Z |
dc.date.available | 2022-08-05T12:57:36Z |
dc.date.issued | 2022 |
dc.identifier.citation | Pérez Zanón, N. [et al.]. Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information. "Geoscientific Model Development", 2022, vol. 15, núm. 15, p. 6115-6142. |
dc.identifier.issn | 1991-959X |
dc.identifier.issn | 1991-9603 |
dc.identifier.uri | http://hdl.handle.net/2117/371631 |
dc.description.abstract | Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skillful climate information. This barrier is addressed through the development of an R package. Climate Services Toolbox (CSTools) is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi-annual scales. The package contains process-based, state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the modular design of the toolbox in individual functions, the users can develop their own post-processing chain of functions, as shown in the use cases presented in this paper, including the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model, and the post-processing of temperature and precipitation data to be used as input in impact models. |
dc.description.sponsorship | This research has been supported by the Horizon 2020 (S2S4E; grant no. 776787), EUCP (grant no. 776613), ERA4CS (grant no. 690462), and the Ministerio de Ciencia e Innovación (grant no. FPI PRE2019-088646). |
dc.language.iso | eng |
dc.publisher | Copernicus Publications |
dc.rights | Attribution 4.0 International |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia |
dc.subject.lcsh | Forecasting--Mathematical models |
dc.subject.lcsh | Temperature--Seasonal variations |
dc.subject.lcsh | Climatic changes |
dc.subject.lcsh | Climatic changes--Forecasting. |
dc.subject.other | Climate forecast |
dc.subject.other | Climate Services Toolbox (CSTools) v4.0 |
dc.title | Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information |
dc.type | Article |
dc.subject.lemac | Simulació per ordinador |
dc.subject.lemac | Simulació, Mètodes de |
dc.identifier.doi | 10.5194/gmd-15-6115-2022 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://gmd.copernicus.org/articles/15/6115/2022/ |
dc.rights.access | Open Access |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/776787/EU/Sub-seasonal to Seasonal climate forecasting for Energy/S2S4E |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/776613/EU/European Climate Prediction system/EUCP |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/690462/EU/European Research Area for Climate Services/ERA4CS |
local.citation.publicationName | Geoscientific Model Development |
local.citation.volume | 15 |
local.citation.number | 15 |
local.citation.startingPage | 6115 |
local.citation.endingPage | 6142 |
dc.relation.dataset | Data availability The data sets used in this article are available from the CDS (https://cds.climate.copernicus.eu/ last access: 1 August 2022), which are the SEAS5 (Johnson et al., 2019; ECMWF long-range forecasting system SEAS5), the ERA5 (Hersbach et al., 2020) and the ERA5-Land data (Muñoz-Sabater et al., 2021), the CHIRPS (Funk et al., 2015; Climate Hazards group Infrared Precipitation with Stations; https://data.chc.ucsb.edu/products/CHIRPS-2.0/, last access: 24 July 2022), and in the WorldClim2 (Fick and Hijmans, 2017; http://www.worldclim.com/version2, last access: 1 August 2022). |
dc.relation.dataset | Code availability CSTools is released under the Apache License version 2.0. The latest release of CSTools 4.0.1 is publicly available from a CRAN repository https://CRAN.R-project.org/package=CSTools (last access: 18 July 2022). It is being developed at BSC-CNS for a GitLab repository https://earth.bsc.es/gitlab/external/cstools/ (last access: 22 July 2022) and shared via Zenodo (https://doi.org/10.5281/zenodo.5549474; Pérez-Zanón et al., 2021a). The code to reproduce the use cases and plots shown in this work is shared on the three sites, and we recommend finding it in the GitLab repository https://earth.bsc.es/gitlab/external/cstools/-/tree/master/inst/doc (last access: 24 July 2022). |
dc.description.authorship | "Article signat per 19 autors/es: Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert Van Schaeybroeck, Llorenç Lledó, Nicolau Manubens, Emmanuel Roulin, M. Carmen Alvarez-Castro, Lauriane Batté , Pierre-Antoine Bretonnière, Susana Corti, Carlos Delgado-Torres, Marta Domínguez, Federico Fabiano, Ignazio Giuntoli, Jost von Hardenberg, Eroteida Sánchez-García, Verónica Torralba, and Deborah Verfaillie" |