GOES-East full-disk AI nowcasting of cloud evolution in observation space

Abstract

Clouds affect aviation, solar energy, remote sensing, and storm prediction, yet they remain among the hardest atmospheric features to forecast, particularly at convective scales. Because clouds are shaped by processes spanning a wide range of space and time scales, numerical weather prediction, extrapolation methods, and existing machine learning (ML) approaches are each limited by some combination of accuracy, domain size, and temporal resolution. We present DOP+, an ML approach for clouds that forecasts GOES-East full-disk infrared brightness temperatures by extending direct observation prediction (DOP) with conditioning on meteorological fields. The domain covers tropical, midlatitude, and marine regimes across $\sim 10^8 ~ km^2$, roughly a fifth of Earth's surface. DOP+ forecasts cloud evolution at 10-minute resolution and outperforms persistence, synoptic-scale NWP, and a pure DOP baseline across 0-6 h lead times in fractions skill score and mean absolute error skill score. Convective structure is retained out to 2-3 h. DOP+ thus achieves a state-of-the-art combination of accuracy, temporal resolution, and spatial coverage. Our work lays the foundation for fully global cloud nowcasting at convective timescales.