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Article Dans Une Revue Geoscientific Model Development Année : 2022

Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information

Núria Pérez-Zanón
Louis-Philippe Caron
Silvia Terzago
Bert van Schaeybroeck
Llorenç Lledó
Nicolau Manubens
  • Fonction : Auteur
Emmanuel Roulin
  • Fonction : Auteur
M Carmen Alvarez-Castro
Pierre-Antoine Bretonnière
Susana Corti
Carlos Delgado-Torres
Marta Domínguez
Federico Fabiano
Ignazio Giuntoli
  • Fonction : Auteur
Jost von Hardenberg
Eroteida Sánchez-García
Verónica Torralba
  • Fonction : Auteur
Deborah Verfaillie

Résumé

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.
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hal-03752392 , version 1 (17-08-2022)

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Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert van Schaeybroeck, Llorenç Lledó, et al.. Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information. Geoscientific Model Development, 2022, 15 (15), pp.6115-6142. ⟨10.5194/gmd-15-6115-2022⟩. ⟨hal-03752392⟩
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