Monthly Diffusion v0.9: A Latent Diffusion Model for the First AI-MIP
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915937990475776 |
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| author | Hall, Kyle J. C. Molina, Maria J. |
| author_facet | Hall, Kyle J. C. Molina, Maria J. |
| contents | Here, we describe Monthly Diffusion at 1.5-degree grid spacing (MD-1.5 version 0.9), a climate emulator that leverages a spherical Fourier neural operator (SFNO)-inspired Conditional Variational Auto-Encoder (CVAE) architecture to model the evolution of low-frequency internal atmospheric variability using latent diffusion. MDv0.9 was designed to forward-step at monthly mean timesteps in a data-sparse regime, using modest computational requirements. This work describes the motivation behind the architecture design, the MDv0.9 training procedure, and initial results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13481 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Monthly Diffusion v0.9: A Latent Diffusion Model for the First AI-MIP Hall, Kyle J. C. Molina, Maria J. Machine Learning Artificial Intelligence Atmospheric and Oceanic Physics Here, we describe Monthly Diffusion at 1.5-degree grid spacing (MD-1.5 version 0.9), a climate emulator that leverages a spherical Fourier neural operator (SFNO)-inspired Conditional Variational Auto-Encoder (CVAE) architecture to model the evolution of low-frequency internal atmospheric variability using latent diffusion. MDv0.9 was designed to forward-step at monthly mean timesteps in a data-sparse regime, using modest computational requirements. This work describes the motivation behind the architecture design, the MDv0.9 training procedure, and initial results. |
| title | Monthly Diffusion v0.9: A Latent Diffusion Model for the First AI-MIP |
| topic | Machine Learning Artificial Intelligence Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2604.13481 |