Physics-Guided Flow-Map Matching for Precipitation Nowcasting

arXiv:2609.37487 · cs.CV, cs.LG · Submitted 2026-09-27 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-27

Updated: 2026-09-27

Comments: Accepted by ACCV 2026

Code: https://github.com/Neurogica/PG-FMM

Project page: https://neurogica.github.io/PG-FMM

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Ablation — 17× in this paper · explained in 1 episode
• Consistency — 16× in this paper · explained in 3 episodes
• Benchmark — 14× in this paper · explained in 2 episodes
• Flow Matching — 9× in this paper · explained in 7 episodes
• Generative Model — 9× in this paper · explained in 2 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• Generative Models — 8× in this paper · explained in 2 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Verification — 6× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• Decoupling — 5× in this paper · explained in 2 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Generative Modeling — 4× in this paper · explained in 3 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Optical Flow — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Critical Success Index — 2× in this paper
• Distillation — 2× in this paper · explained in 2 episodes
• Heidke Skill Score — 2× in this paper
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Self-consistency — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Chaotic Dynamics — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification
• Flow Matching Guide and Code
• Neural Stochastic Processes for Satellite Precipitation Refinement
• FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
• Deep Learning and Foundation Models for Weather Prediction: A Survey

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