IMAGE: a multivariate multi-site stochastic weather generator for European weather and climate

• Published in: Stochastic environmental research and risk assessment Vol. 32; no. 3; pp. 771 - 784
• Main Authors: Sparks, Nathan J., Hardwick, Stephen R., Schmid, Matthias, Toumi, Ralf
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2018; Springer Nature B.V
• Subjects: Aquatic Pollution; Autoregressive models; Chemistry and Earth Sciences; Climate models; Climatic data; Computational Intelligence; Computer Science; Earth and Environmental Science; Earth Sciences; Environment; Math. Appl. in Environmental Science; Original Paper; Orthogonal functions; Physics; Precipitation; Probability Theory and Stochastic Processes; Resampling; Risk management; Statistics for Engineering; Stochasticity; Waste Water Technology; Water Management; Water Pollution Control; Weather; Climate extremes index; Weather generator; Multivariate; EOF; Extreme events; Multi-site
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-017-1433-9

Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging. A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and parameters are correlated through resampling of principle component time series of empirical orthogonal function modes. A case study using European climate data demonstrates the model’s ability to reproduce extreme events of temperature and precipitation. The ability to capture the spatial and temporal extent of extremes using a modified Climate Extremes Index is demonstrated. Importantly, the model generates events covering not observed temporal and spatial scales giving new insights for risk management purposes.