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http://hdl.handle.net/20.500.11765/13919
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, Núria; Caron, Louis-Philippe; Terzago, Silvia; Schaeybroeck, Bert Van; Lledó, Llorenç; Manubens, Nicolau; Roulin, Emmanuel; Álvarez-Castro, Carmen; Batté, Lauriane; Bretonnière, Pierre-Antoine; Corti, Susanna; Delgado Torres, Carlos; Domínguez Alonso, Marta
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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: | https://doi.org/10.5194/gmd-15-6115-2022 |
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). |
URI: | http://hdl.handle.net/20.500.11765/13919 |
ISSN: | 1991-959X 1991-9603 |
Appears in Collections: | Artículos científicos 2019-2022 |
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![]() | GMD_Perez_2022.pdf | 13,61 MB | Adobe PDF | ![]() View/Open |
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