An R package for climate forecast verification. (May 2018)

• Record Type: Journal Article
• Authors: Manubens, Nicolau; Caron, Louis-Philippe; Hunter, Alasdair; Bellprat, Omar; Exarchou, Eleftheria; Fučkar, Neven S.; Garcia-Serrano, Javier; Massonnet, François; Ménégoz, Martin; Sicardi, Valentina; Batté, Lauriane; Prodhomme, Chloé; Torralba, Verónica; Cortesi, Nicola; Mula-Valls, Oriol; Serradell, Kim; Guemas, Virginie; Doblas-Reyes, Francisco J.
• Is Part Of: Environmental modelling & software. Volume 103(2018)
• Page Start: 29
• Page End: 42
• Publication Date: 2018-05
• Subjects: Forecast verification -- Climate forecast -- Skill score -- R package; 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.2018.01.018
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Forecast verification is necessary to determine the skill and quality of a forecasting system, and whether it shows improvement with pre- or post-processing. s2dverification v2.8.0 is an open-source R package for the quality assessment of climate forecasts using state-of-the-art verification scores. The package provides tools for each step of the forecast verification process: data retrieval, processing, calculation of verification measures and visualisation of the results. Examples are provided and explained for each of these stages using climate model output. Highlights: An R package that streamlines the entire verification process is presented. A range of verification scores can be applied directly to multi-model ensembles. Plotting functions allow the rapid visualization and interpretation of results. Sophisticated bias correction methods are included.