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Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information
Title: Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information
Authors: Pérez Zanón, NúriaCaron, Louis-PhilippeTerzago, SilviaSchaeybroeck, Bert VanLledó, LlorençManubens, NicolauRoulin, EmmanuelÁlvarez-Castro, CarmenBatté, LaurianeBretonnière, Pierre-AntoineCorti, SusannaDelgado Torres, CarlosDomínguez Alonso, Marta ORCID Autor AEMETFabiano, FedericoGiuntoli, IgnazioHardenberg, Jost vonSánchez García, Eroteida ORCID RESEARCHERID Autor AEMETTorralba, VerónicaVerfaillie, Deborah
Keywords: Climate forecast data; Climate Services Toolbox; Climate information; Climate data
Issue Date: 2022
Publisher: European Geosciences Union; Copernicus Publications
Citation: Geoscientific Model Development. 2022, 15(15), 6115–6142
Publisher version:
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.
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).
ISSN: 1991-959X
Appears in Collections:Artículos científicos 2019-2022

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