MEDSCOPE is developing and distributing a toolbox, CSTools, designed and built to improve the quality of climate forecasts in the Mediterranean region on seasonal to multi-annual scales, along with a number of examples of sectoral applications of relevance for the region. The improved climate prediction information will be achieved both via a better understanding of the Mediterranean climate variability and predictability and on the development of innovative methodologies to extract the information from climate predictions.
THE MEDSCOPE TOOLBOX, CSTools, also includes results of user engagement meetings, instructions for users, examples of sectoral applications, and much additional content.
CSTools is an R package and a preliminary version is publicly available on the CRAN repository. This tool, thought to assess the skill of Climate forecasts on seasonal-to-decadal timescales, exploits dynamical seasonal forecasts in order to provide information relevant to stakeholders at the seasonal timescale. The package contains process-based 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 (Table 1).
Table 1: Summary of the currently included functions.
Documentation
The package includes the description of each individual function through the typical R channels, including the pdf document and an overview of the package is also available in a poster format here. Information about Data Storage and Retrieval is also provided.
While other guidelines on how to use the software are provided in the shape of vignettes* showing how to analyze climate data:
* an instructive tutorial demonstrating practical uses of the software with the discussion of the interpretation of the results.
Installation
You can install the publicly released version of CSTools from CRAN in your R session:
> install.packages(“CSTools”)
and start using it by loading it:
> library(CSTools)
Managing big datasets and memory issues
Depending on the user needs, limitations can be found when trying to process big datasets. This may depend on the number of ensembles, the resolution and region that the user wants to process. CSTools has been developed for compatibility of startR package which covers the following aims:
retrieving data from NetCDF files to RAM memory in a flexible way,
divide automatically datasets in pieces to perform an analysis avoiding memory issues and
run the workflow in your local machine or submitting to an HPC cluster.
This is especially useful when a user doesn’t have access to a HPC and must work with small RAM memory size (Table 2).
Table 2: Scheme relating data, RAM and CSTools functions.
The functions in CSTools (with or without CST_ prefix) include a parameter called ‘ncores’ that allows to automatically parallelize the code in multiple cores when the parameter is set greater than one.
How to cite
Please, cite the package as:
Nuria Perez-Zanon, Louis-Philippe Caron, Carmen Alvarez-Castro, Lauriane Batte, Jost von Hardenberg, Llorenç LLedo, Nicolau Manubens, Eroteida Sanchez-Garcia, Bert van Schaeybroeck, Veronica Torralba and Deborah Verfaillie (2021). CSTools: Assessing Skill of Climate Forecasts on Seasonal-to-Decadal Timescales. R package version 4.0.0. https://CRAN.R-project.org/package=CSTools
Contact details
For further questions contact the coordinators of the package:
The package was recently showcased at the following events:
European Geosciences Union (EGU) General Assembly, On-line, 4-8 May 2020.
MedCOF-14. Fourtieth Mediterranean Climate Outlook Forum (online), May 2020.
MedCOF-13. Thirtieth Mediterranean Climate Outlook Forum (online)
European Meteorological Society (EMS) Annual Meeting, Copenhagen, Denmark, 9-13 September 2019.
14th International Meeting on Statistical Climatology (IMSC), Toulouse, France, 24-28 June 2019.
European Geosciences Union (EGU) General Assembly, Vienna, Austria, 8-12 April 2019.
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