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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.21664 |
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| _version_ | 1866917031454965760 |
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| author | Ephrati, Sagy Woodfield, James |
| author_facet | Ephrati, Sagy Woodfield, James |
| contents | This paper demonstrates the feasibility of trajectory learning for ensemble forecasts by employing the continuous ranked probability score (CRPS) as a loss function. Using the two-scale Lorenz '96 system as a case study, we develop and train both additive and multiplicative stochastic parametrizations to generate ensemble predictions. Results indicate that CRPS-based trajectory learning produces parametrizations that are both accurate and sharp. The resulting parametrizations are straightforward to calibrate and outperform derivative-fitting-based parametrizations in short-term forecasts. This approach is particularly promising for data assimilation applications due to its accuracy over short lead times. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21664 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trajectory learning for ensemble forecasts via the continuous ranked probability score: a Lorenz '96 case study Ephrati, Sagy Woodfield, James Numerical Analysis Machine Learning 65M75, 37M05 This paper demonstrates the feasibility of trajectory learning for ensemble forecasts by employing the continuous ranked probability score (CRPS) as a loss function. Using the two-scale Lorenz '96 system as a case study, we develop and train both additive and multiplicative stochastic parametrizations to generate ensemble predictions. Results indicate that CRPS-based trajectory learning produces parametrizations that are both accurate and sharp. The resulting parametrizations are straightforward to calibrate and outperform derivative-fitting-based parametrizations in short-term forecasts. This approach is particularly promising for data assimilation applications due to its accuracy over short lead times. |
| title | Trajectory learning for ensemble forecasts via the continuous ranked probability score: a Lorenz '96 case study |
| topic | Numerical Analysis Machine Learning 65M75, 37M05 |
| url | https://arxiv.org/abs/2508.21664 |