An R package to visualize and communicate uncertainty in seasonal climate prediction. (January 2018)

• Record Type: Journal Article
• Authors: Frías, M.D.; Iturbide, M.; Manzanas, R.; Bedia, J.; Fernández, J.; Herrera, S.; Cofiño, A.S.; Gutiérrez, J.M.
• Is Part Of: Environmental modelling & software. Volume 99(2018)
• Page Start: 101
• Page End: 110
• Publication Date: 2018-01
• Subjects: visualizeR -- Probabilistic visualization -- Seasonal forecast -- R; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.09.008
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Interest in seasonal forecasting is growing fast in many environmental and socio-economic sectors due to the huge potential of these predictions to assist in decision making processes. The practical application of seasonal forecasts, however, is still hampered to some extent by the lack of tools for an effective communication of uncertainty to non-expert end users.visualizeR is aimed to fill this gap, implementing a set of advanced visualization tools for the communication of probabilistic forecasts together with different aspects of forecast quality, by means of perceptual multivariate graphical displays (geographical maps, time series and other graphs). These are illustrated in this work using the example of the strong El Niño 2015/16 event forecast. The package is part of the climate4R bundle providing transparent access to the ECOMS-UDG climate data service. This allows a flexible application ofvisualizeR to a wide variety of specific seasonal forecasting problems and datasets. Highlights: R tool for visualizing and communicating uncertainty in seasonal climate prediction. Open source package for reproducibility of results. Visualizations based on end users' feedback from EUPORIAS EU FP7 project. Graphical displays of incremental complexity to fit users of different expertise.