Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908764244803584 |
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| author | Merizzi, Fabio Evangelista, Davide Loukos, Harilaos |
| author_facet | Merizzi, Fabio Evangelista, Davide Loukos, Harilaos |
| contents | In recent years, diffusion models have emerged as powerful tools for generating ensemble members in meteorology. In this work, we demonstrate how a Denoising Diffusion Implicit Model (DDIM) can effectively control ensemble variance by varying the number of diffusion steps. Introducing a theoretical framework, we relate diffusion steps to the variance expressed by the reverse diffusion process. Focusing on reanalysis downscaling, we propose an ensemble diffusion model for the full ERA5-to-CERRA domain, generating variance-calibrated ensemble members for wind speed at full spatial and temporal resolution. Our method aligns global mean variance with a reference ensemble dataset and ensures spatial variance is distributed in accordance with observed meteorological variability. Additionally, we address the lack of ensemble information in the CARRA dataset, showcasing the utility of our approach for efficient, high-resolution ensemble generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14822 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling Merizzi, Fabio Evangelista, Davide Loukos, Harilaos Applications Artificial Intelligence Machine Learning In recent years, diffusion models have emerged as powerful tools for generating ensemble members in meteorology. In this work, we demonstrate how a Denoising Diffusion Implicit Model (DDIM) can effectively control ensemble variance by varying the number of diffusion steps. Introducing a theoretical framework, we relate diffusion steps to the variance expressed by the reverse diffusion process. Focusing on reanalysis downscaling, we propose an ensemble diffusion model for the full ERA5-to-CERRA domain, generating variance-calibrated ensemble members for wind speed at full spatial and temporal resolution. Our method aligns global mean variance with a reference ensemble dataset and ensures spatial variance is distributed in accordance with observed meteorological variability. Additionally, we address the lack of ensemble information in the CARRA dataset, showcasing the utility of our approach for efficient, high-resolution ensemble generation. |
| title | Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling |
| topic | Applications Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.14822 |