Preprints
https://doi.org/10.5194/gmd-2021-368
https://doi.org/10.5194/gmd-2021-368
Submitted as: model description paper
06 Dec 2021
Submitted as: model description paper | 06 Dec 2021
Status: a revised version of this preprint was accepted for the journal GMD.

The CSTools (v4.0) Toolbox: from Climate Forecasts to Climate Forecast Information

Núria Pérez-Zanón1, Louis-Philippe Caron1,2, Silvia Terzago3, Bert Van Schaeybroeck4, Llorenç Lledó1, Nicolau Manubens1, Emmanuel Roulin4, M. Carmen Alvarez-Castro5, Lauriane Batté6, Carlos Delgado-Torres1, Marta Domínguez7, Jost von Hardenberg8,3, Eroteida Sánchez-García7, Verónica Torralba1, and Deborah Verfaillie9 Núria Pérez-Zanón et al.
  • 1Barcelona Supercomputing Center (BSC), Barcelona, Spain
  • 2Ouranos, 550 Sherbrooke St W, Montreal, Quebec H3A9, Canada
  • 3National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Turin, Italy
  • 4Royal Meteorological Institute of Belgium, Brussels, Belgium
  • 5Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Bologna, Italy
  • 6CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 7Delegación territorial (DT) Cantabria, Agencia Estatal de Meteorología (AEMET), Santander, Spain
  • 8Dept. of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, Italy
  • 9Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium

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 skilful climate information. This barrier is addressed through the development of an R package. 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 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 manuscript: 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 data to be used as input for the SCHEME hydrological model.

Núria Pérez-Zanón et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-368', Anonymous Referee #1, 04 Jan 2022
    • AC1: 'Reply on RC1', Núria Pérez-Zanón, 01 Apr 2022
  • RC2: 'Comment on gmd-2021-368', Anonymous Referee #2, 10 Jan 2022
    • AC2: 'Reply on RC2', Núria Pérez-Zanón, 01 Apr 2022

Núria Pérez-Zanón et al.

Model code and software

CSTools Núria Pérez-Zanón; Louis-Philippe Caron; Carmen Alvarez-Castro; Lauriane Batté; Carlos Delgado; Jost von Hardenberg; Llorenç LLedó; Nicolau Manubens; Lluís Palma; Eroteida Sanchez-Garcia; Bert van Schaeybroeck; Veronica Torralba; Deborah Verfaillie https://doi.org/10.5281/zenodo.5549474

Núria Pérez-Zanón et al.

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Short summary
CSTools (short for Climate Service Tools) is an R package that contains process-based methods for climate 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. In addition to describing the structure and methods in the package, we also present three use cases to illustrate the seasonal climate forecasts post-processing for specific purposes.