BayGEN: A Bayesian Space‐Time Stochastic Weather Generator. Issue 4 (8th April 2019)

• Record Type: Journal Article
• Authors: Verdin, Andrew; Rajagopalan, Balaji; Kleiber, William; Podestá, Guillermo; Bert, Federico
• Is Part Of: Water resources research. Volume 55:Issue 4(2019)
• Page Start: 2900
• Page End: 2915
• Publication Date: 2019-04-08
• Subjects: Bayesian -- weather generator -- uncertainty -- agricultural decision support -- Argentine Pampas; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2017WR022473
• Languages: English
• ISSNs: 0043-1397

Abstract

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. Key Points: Uncertainty in model parameters propagates to weather simulations Spatial processes on model parameters enable gridded simulation BayGEN is coupled with crop simulation models for agricultural decision support