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

Abstract

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
Volume556
DOIs
StatePublished - Jan 2018

Bibliographical note

Funding Information:
This research is funded by National Science Foundation Award # 1211613. The authors would like to thank the Met Service of Argentina for providing the weather data. The authors are grateful for Drs. Linda Means and Seth McGinnis of the National Center for Atmospheric Research (NCAR) for providing the CMIP5 regional climate model projections. This work utilized the Janus supercomputer, which is supported by the National Science Foundation (award number CNS-0821794) and the University of Colorado Boulder. The Janus supercomputer is a joint effort of the University of Colorado Boulder, the University of Colorado Denver and the National Center for Atmospheric Research. We thank the two anonymous reviewers for their insightful comments, which significantly improved the manuscript.

Funding Information:
This research is funded by National Science Foundation Award # 1211613 . The authors would like to thank the Met Service of Argentina for providing the weather data. The authors are grateful for Drs. Linda Means and Seth McGinnis of the National Center for Atmospheric Research (NCAR) for providing the CMIP5 regional climate model projections. This work utilized the Janus supercomputer, which is supported by the National Science Foundation (award number CNS-0821794 ) and the University of Colorado Boulder . The Janus supercomputer is a joint effort of the University of Colorado Boulder, the University of Colorado Denver and the National Center for Atmospheric Research. We thank the two anonymous reviewers for their insightful comments, which significantly improved the manuscript.

Publisher Copyright:
© 2015 Elsevier B.V.

Keywords

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

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