Earth System Model Tuning Without Hyperparameters
Abstract This article introduces a new algorithm, KalmRidge, and demonstrates its ability to tune an Earth system model using idealized experiments. Unlike similar algorithms, KalmRidge eliminates the need for offline hyperparameter selection, thereby substantially reducing computational expense. Th...
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2025-07-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024MS004607 |
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| _version_ | 1849729093142052864 |
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| author | Nikki Lydeen Timothy DelSole Benjamin Cash |
| author_facet | Nikki Lydeen Timothy DelSole Benjamin Cash |
| author_sort | Nikki Lydeen |
| collection | DOAJ |
| description | Abstract This article introduces a new algorithm, KalmRidge, and demonstrates its ability to tune an Earth system model using idealized experiments. Unlike similar algorithms, KalmRidge eliminates the need for offline hyperparameter selection, thereby substantially reducing computational expense. This is done by rewriting the update equations for the ensemble Kalman filter as an equivalent ridge regression problem, then applying standard cross‐validation techniques to adaptively choose the regularization parameter. We propose that this algorithm, with time‐mean spherical harmonic projections as tuning targets, provides a promising, tractable approach for parameter estimation. |
| format | Article |
| id | doaj-art-594555f7f238428aa52e5ecfb5ab1fb9 |
| institution | DOAJ |
| issn | 1942-2466 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj-art-594555f7f238428aa52e5ecfb5ab1fb92025-08-20T03:09:19ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662025-07-01177n/an/a10.1029/2024MS004607Earth System Model Tuning Without HyperparametersNikki Lydeen0Timothy DelSole1Benjamin Cash2George Mason University Fairfax VA USAGeorge Mason University Fairfax VA USAGeorge Mason University Fairfax VA USAAbstract This article introduces a new algorithm, KalmRidge, and demonstrates its ability to tune an Earth system model using idealized experiments. Unlike similar algorithms, KalmRidge eliminates the need for offline hyperparameter selection, thereby substantially reducing computational expense. This is done by rewriting the update equations for the ensemble Kalman filter as an equivalent ridge regression problem, then applying standard cross‐validation techniques to adaptively choose the regularization parameter. We propose that this algorithm, with time‐mean spherical harmonic projections as tuning targets, provides a promising, tractable approach for parameter estimation.https://doi.org/10.1029/2024MS004607parameter estimationKalman filterhyperparameters |
| spellingShingle | Nikki Lydeen Timothy DelSole Benjamin Cash Earth System Model Tuning Without Hyperparameters Journal of Advances in Modeling Earth Systems parameter estimation Kalman filter hyperparameters |
| title | Earth System Model Tuning Without Hyperparameters |
| title_full | Earth System Model Tuning Without Hyperparameters |
| title_fullStr | Earth System Model Tuning Without Hyperparameters |
| title_full_unstemmed | Earth System Model Tuning Without Hyperparameters |
| title_short | Earth System Model Tuning Without Hyperparameters |
| title_sort | earth system model tuning without hyperparameters |
| topic | parameter estimation Kalman filter hyperparameters |
| url | https://doi.org/10.1029/2024MS004607 |
| work_keys_str_mv | AT nikkilydeen earthsystemmodeltuningwithouthyperparameters AT timothydelsole earthsystemmodeltuningwithouthyperparameters AT benjamincash earthsystemmodeltuningwithouthyperparameters |