A comparative study of stochastic and deep generative models for multisite precipitation synthesis (Papers Track)
Jorge Luis Guevara Diaz (IBM Research); Dario Augusto Borges Oliveira (IBM Research); Bianca Zadrozny (IBM Research); Campbell Watson (IBM Reserch)
Future climate change scenarios are usually hypothesized using simulations from weather generators. However, there only a few works comparing and evaluating multisite weather generators, and there is no existing work contrasting promising deep learning approaches for weather generation against classical stochastic weather generators. This study shows preliminary results evaluating stochastic weather generators and deep generative models for multisite precipitation synthesis. Using a variety of metrics, we compare two open source weather generators: XWeathergen (an extension of the Weathergen library) and RGeneratePrec, and two deep generative models: GAN and VAE. Our preliminary results can serve as a guide for improving the design of deep learning architectures and algorithms for the multisite precipitation synthesis task.