Sunghyun Park (KAIST); Kangyeol Kim (KAIST); Sookyung Kim (Lawrence Livermore National Laboratory); Joonseok Lee (Google Research); Junsoo Lee (KAIST); Jiwoo Lee (Lawrence Livermore National Laboratory); Jaegul Choo (KAIST)
Fast and accurate prediction of extreme climate events is critical especially in the recent globally warming environment. Considering recent advancements in deep neural networks, it is worthwhile to tackle this problem as data-driven spatio-temporal prediction using neural networks. However, a nontrivial challenge in practice lies in irregular time gaps between which climate observation data are collected due to sensor errors and other issues. This paper proposes an approach for spatio-temporal hurricane prediction that can address this issue of irregular time gaps in collected data with a simple but robust end-to-end model based on Neural Ordinary Differential Equation and video prediction model based on Retrospective Cycle-GAN.