Papers › Data-driven Precipitation Nowcasting Using Satellite Imagery

Data-driven Precipitation Nowcasting Using Satellite Imagery

16 Dec 2024arXiv:2412.11480archive 2025-07-28

Young-Jae Park, Doyi Kim, Minseok Seo, Hae-Gon Jeon, Yeji Choi

Accurate precipitation forecasting is crucial for early warnings of disasters, such as floods and landslides. Traditional forecasts rely on ground-based radar systems, which are space-constrained and have high maintenance costs. Consequently, most developing countries depend on a global numerical model with low resolution, instead of operating their own radar systems. To mitigate this gap, we propose the Neural Precipitation Model (NPM), which uses global-scale geostationary satellite imagery. NPM predicts precipitation for up to six hours, with an update every hour. We take three key channels to discriminate rain clouds as input: infrared radiation (at a wavelength of 10.5 μm), upper- (6.3 μm), and lower- (7.3 μm) level water vapor channels. Additionally, NPM introduces positional encoders to capture seasonal and temporal patterns, accounting for variations in precipitation. Our experimental results demonstrate that NPM can predict rainfall in real-time with a resolution of 2 km. The code and dataset are available at https://github.com/seominseok0429/Data-driven-Precipitation-Nowcasting-Using-Satellite-Imagery.

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