Data-driven Precipitation Nowcasting Using Satellite Imagery
Young-Jae Park, Doyi Kim, Minseok Seo, Hae-Gon Jeon, Yeji Choi
Abstract
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 input three key channels to discriminate rain clouds: 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, reflecting variations in precipitation. Our experimental results demonstrate that NPM can predict rainfall in real-time with a resolution of 2 km.
BibTeX
@article{Park_Kim_Seo_Jeon_Choi_2025, title={Data-driven Precipitation Nowcasting Using Satellite Imagery}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35049}, DOI={10.1609/aaai.v39i27.35049}, abstractNote={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 input three key channels to discriminate rain clouds: 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, reflecting variations in precipitation. Our experimental results demonstrate that NPM can predict rainfall in real-time with a resolution of 2 km.}, number={27}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Park, Young-Jae and Kim, Doyi and Seo, Minseok and Jeon, Hae-Gon and Choi, Yeji}, year={2025}, month={Apr.}, pages={28284-28292} }