Harmonizing Multisatellite Geostationary Observations Using Zero-Shot Video Frame Interpolation

Geostationary satellites such as GOES, GK2A, Himawari, and MTG/MSG have been providing a wealth of observational data over the past few decades, capturing the evolution and movement of clouds and precipitation systems with unprecedented detail. This extensive record has been instrumental in advancing our understanding of atmospheric dynamics and supporting the continuous monitoring of extreme weather events and natural disasters. Despite their capability to observe wide areas and the advantage of covering the entire globe with data from just three satellites, utilizing these geostationary satellites to model the Earth system on a global scale has proven challenging due to differences in observation intervals, spectral channels, and coverage footprints. To address these challenges, we propose a zero-shot Video Frame Interpolation (VFI) framework designed to harmonize imagery from multiple geostationary satellites. This method adapts the Many-to-Many Splatting VFI model, originally developed for RGB video processing, to work with single-channel infrared satellite imagery. By generating intermediate frames at high temporal resolutions (e.g., 2–5-minute intervals), our approach enables near-synchronous global coverage with improved temporal consistency. This method offers two key benefits. First, it enhances uniform sampling across satellites, creating a cohesive global view that is particularly valuable in data-sparse regions. Second, the interpolated frames improve the ability to capture and track critical meteorological phenomena. In addition, we address practical considerations such as computational efficiency, consistency with radiative or brightness temperature fields, and the robustness of zero-shot generalization. Our findings suggest that this zero-shot VFI framework can significantly advance global nowcasting providing a pathway to more accurate and timely Earth system modeling.