Data-driven Precipitation Nowcasting Using Satellite Imagery
This addresses the need for accurate, low-cost precipitation nowcasting for disaster warnings, particularly in developing countries that lack ground-based radar systems.
The paper tackles the problem of precipitation forecasting by proposing the Neural Precipitation Model (NPM), which uses global-scale geostationary satellite imagery to predict rainfall for up to six hours with a resolution of 2 km, achieving real-time predictions.
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.