A conditional stochastic weather generator for seasonal to multi-decadal simulations

Andrew Verdin, Balaji Rajagopalan, William Kleiber, Guillermo Podestá, Federico Bert

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


We present the application of a parametric stochastic weather generator within a nonstationary context, enabling simulations of weather sequences conditioned on interannual and multi-decadal trends. The generalized linear model framework of the weather generator allows any number of covariates to be included, such as large-scale climate indices, local climate information, seasonal precipitation and temperature, among others. Here we focus on the Salado A basin of the Argentine Pampas as a case study, but the methodology is portable to any region. We include domain-averaged (e.g., areal) seasonal total precipitation and mean maximum and minimum temperatures as covariates for conditional simulation. Areal covariates are motivated by a principal component analysis that indicates the seasonal spatial average is the dominant mode of variability across the domain. We find this modification to be effective in capturing the nonstationarity prevalent in interseasonal precipitation and temperature data. We further illustrate the ability of this weather generator to act as a spatiotemporal downscaler of seasonal forecasts and multidecadal projections, both of which are generally of coarse resolution.

Original languageEnglish (US)
Pages (from-to)835-846
Number of pages12
JournalJournal of Hydrology
StatePublished - Jan 2018


  • Conditional simulation
  • Daily precipitation
  • Daily temperature
  • Downscaling seasonal forecasts
  • Generalized linear models
  • Stochastic weather generator

ASJC Scopus subject areas

  • Water Science and Technology


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