Multisite, multivariate weather generation based on generalised linear models. (December 2020)

• Record Type: Journal Article
• Authors: Chandler, Richard E.
• Is Part Of: Environmental modelling & software. Volume 134(2020)
• Publication Date: 2020-12
• Subjects: Climate impacts -- Downscaling -- Weather generator; 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.2020.104867
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper describes a methodology for constructing and simulating from models of daily weather time series at multiple locations, incorporating potential nonstationarities and suitable for use in those studies of climate impacts and adaptation where a detailed representation of local weather is required. The approach is based on generalised linear models (GLMs) and aims to allow for realistic representations of local weather structures including spatial, temporal and inter-variable dependencies. The theory is implemented in a software tool, Rglimclim, that runs in the R programming environment; and is illustrated using a case study involving generation of daily precipitation and temperature at 26 locations in northern Iberia. Highlights: Publicly available software with the ability to represent many different weather variables. Climate change signals can be captured in daily weather simulations. The methodology is designed to accommodate situations with missing data values. Simulations can be generated at locations where no data are available. Effective use of the methodology requires a high level of statistical literacy.