Nowformer : A Locally Enhanced Temporal Learner for Precipitation Nowcasting (Papers Track)

Jinyoung Park (KAIST); Inyoung Lee (KAIST); Minseok Son (KAIST); Seungju Cho (KAIST); Changick Kim (KAIST)

Paper PDF Slides PDF Recorded Talk NeurIPS 2022 Poster Topia Link Cite
Computer Vision & Remote Sensing Earth Observation & Monitoring


The precipitation video datasets have distinctive meteorological patterns where a mass of fluid moves in a particular direction across the entire frames, and each local area of the fluid has an individual life cycle from initiation to maturation to decay. This paper proposes a novel transformer-based model for precipitation nowcasting that can extract global and local dynamics within meteorological characteristics. The experimental results show our model achieves state-of-the-art performances on the precipitation nowcasting benchmark.

Recorded Talk (direct link)