Precipitation Nowcasting with Satellite Imagery

Abstract

Precipitation nowcasting is a short-range forecast of rain/snow (up to 2 hours), often displayed on top of the geographical map by the weather service. Modern precipitation nowcasting algorithms rely on the extrapolation of observations by ground-based radars via optical flow techniques or neural network models. Dependent on these radars, typical nowcasting is limited to the regions around their locations. We have developed a method for precipitation nowcasting based on geostationary satellite imagery and incorporated the resulting data into the Yandex.Weather precipitation map (including an alerting service with push notifications for products in the Yandex ecosystem), thus expanding its coverage and paving the way to a truly global nowcasting service.

arXiv commentFinal KDD 2019 version

References in corpus (2)

• Road Extraction by Deep Residual U-Net
• Learning Aerial Image Segmentation from Online Maps

Cited by in corpus (5)

• Skillful Precipitation Nowcasting using Deep Generative Models of Radar
• Broad-UNet: Multi-scale feature learning for nowcasting tasks
• Nowcasting-Nets: Deep Neural Network Structures for Precipitation Nowcasting Using IMERG
• Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies
• From Swath to Full-Disc: Advancing Precipitation Retrieval with Multimodal Knowledge Expansion