mlsanityData to 7 Oct 2026

Data-driven Precipitation Nowcasting Using Satellite Imagery

arXiv 2412.11480 ↗First posted 16 Dec 2024cs.CV eess.IVcited by 6

Not on the map. Why?

Abstract (arXiv)

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 μ m), upper- (6.3 μ m), and lower- (7.3 μ m) 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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 13 of its 31 references that are in the map. First posted on arXiv 16 Dec 2024.

Builds on

13 of 31 references.

References · 13

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  2. 2021Skillful Precipitation Nowcasting using Deep Generative Models of Radarcited by 308
  3. 2022BBDM: Image-to-image Translation with Brownian Bridge Diffusion Modelscited by 222
  4. 2020Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Predictioncited by 192
  5. 2021PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learningcited by 181
  6. 2022SimVP: Simpler yet Better Video Predictioncited by 181
  7. 2020MetNet: A Neural Weather Model for Precipitation Forecastingcited by 128
  8. 2022Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learningcited by 84
  9. 2023DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcastingcited by 57
  10. 2023OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learningcited by 53
  11. 2023SwinLSTM:Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTMcited by 32
  12. 2023Deterministic Guidance Diffusion Model for Probabilistic Weather Forecastingcited by 9
1 more
  1. 2024Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Datacited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Precipitation nowcasting of satellite data using physically-aligned neural networkscited by 1
  2. 2025Enhancing Heavy Rain Nowcasting with Multimodal Data: Integrating Radar and Satellite Observationscited by 0
  3. 2025PIANO: Physics-informed Dual Neural Operator for Precipitation Nowcastingcited by 0
  4. 2026Station2Radar: query conditioned gaussian splatting for precipitation fieldcited by 0
  5. 2026MAGPIE-Net: Predicting short-duration heavy-rainfall events in station neighborhoods from multitemporal FY-4A AGRI observationscited by 0
  6. 2026QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalitiescited by 0