BayGEN: A Bayesian Space-Time Stochastic Weather Generator

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

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

We present a Bayesian hierarchical space-time stochastic weather generator (BayGEN) to generate daily precipitation and minimum and maximum temperatures. BayGEN employs a hierarchical framework with data, process, and parameter layers. In the data layer, precipitation occurrence at each site is modeled using probit regression using a spatially distributed latent Gaussian process; precipitation amounts are modeled as gamma random variables; and minimum and maximum temperatures are modeled as realizations from Gaussian processes. The latent Gaussian process that drives the precipitation occurrence process is modeled in the process layer. In the parameter layer, the model parameters of the data and process layers are modeled as spatially distributed Gaussian processes, consequently enabling the simulation of daily weather at arbitrary (unobserved) locations or on a regular grid. All model parameters are endowed with weakly informative prior distributions. The No-U Turn sampler, an adaptive form of Hamiltonian Monte Carlo, is used to maximize the model likelihood function and obtain posterior samples of each parameter. Posterior samples of the model parameters propagate uncertainty to the weather simulations, an important feature that makes BayGEN unique compared to traditional weather generators. We demonstrate the utility of BayGEN with application to daily weather generation in a basin of the Argentine Pampas. Furthermore, we evaluate the implications of crop yield by driving a crop simulation model with weather simulations from BayGEN and an equivalent non-Bayesian weather generator.

Original languageEnglish (US)
Pages (from-to)2900-2915
Number of pages16
JournalWater Resources Research
Volume55
Issue number4
DOIs
StatePublished - Apr 2019

Bibliographical note

Funding Information:
This research was funded by the National Science Foundation's Dynamics of Coupled Natural-Human Systems program (award 1211613). The authors would like to thank the Servicio Meteorol?gico Nacional (SMN) for providing the weather data. These data were extracted from the SMN database (https://www.smn.gob.ar), where it is operationally archived by an institution that has that mandate. The data cannot be uploaded to a repository by the authors, though readers may request the data through SMN. 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 are grateful to three anonymous reviewers for their insightful comments, which significantly improved the manuscript. Our special thanks to Jasper Vrugt for his detailed comments, which helped to enhance the overall presentation of the methodology and results in the manuscript.

Funding Information:
This research was funded by the National Science Foundation's Dynamics of Coupled Natural‐Human Systems program (award 1211613). The authors would like to thank the Servicio Meteorológico Nacional (SMN) for providing the weather data. These data were extracted from the SMN database (https://www.smn.gob.ar), where it is operationally archived by an institution that has that mandate. The data cannot be uploaded to a repository by the authors, though readers may request the data through SMN. 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 are grateful to three anonymous reviewers for their insightful comments, which significantly improved the manuscript. Our special thanks to Jasper Vrugt for his detailed comments, which helped to enhance the overall presentation of the methodology and results in the manuscript.

Publisher Copyright:
©2019. American Geophysical Union. All Rights Reserved.

Keywords

  • Argentine Pampas
  • Bayesian
  • agricultural decision support
  • uncertainty
  • weather generator

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