SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting

arXiv:2608.26594 · cs.LG · Submitted 2026-08-27 · Read on arXiv

cs.LG

Submitted: 2026-08-27

Updated: 2026-09-25

Code: https://github.com/hiepdang-ml/SimCast-S2S

Terminology

• Fine-Tuning — 17× in this paper · explained in 5 episodes
• Latent Space — 17× in this paper · explained in 7 episodes
• Transfer Learning — 12× in this paper · explained in 10 episodes
• Generative Model — 10× in this paper · explained in 2 episodes
• Calibration — 9× in this paper · explained in 7 episodes
• Deep Learning — 9× in this paper
• Latent Representation — 9× in this paper · explained in 2 episodes
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Uncertainty Quantification — 7× in this paper · explained in 8 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Generative Modeling — 6× in this paper · explained in 3 episodes
• Generative Models — 6× in this paper · explained in 2 episodes
• Ranked Probability Skill Score — 6× in this paper
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Loss Function — 4× in this paper · explained in 2 episodes
• Denoising Diffusion Probabilistic Models — 3× in this paper
• Diffusion Process — 3× in this paper · explained in 2 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Representations — 3× in this paper · explained in 3 episodes
• Reconstruction Error — 3× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Low-Rank Adaptation (LoRA) — 2× in this paper · explained in 9 episodes
• Variational Bayes — 2× in this paper · explained in 2 episodes
• AI Weather Models — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Conservation Laws — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Assimilation — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Probabilistic Forecasting — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Improving Subseasonal Forecasting in the Western U.S. with Machine Learning
• Long-Range Distillation: Distilling 10,000 Years of Simulated Climate into Long Timestep AI Weather Models
• PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling

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