An Efficient and Robust Estimation of Spatio‐Temporally Distributed Parameters in Dynamic Models by an Ensemble Kalman Filter
Abstract The accuracy of Earth system models is compromised by unknown and/or unresolved dynamics, making the quantification of systematic model errors essential. While a model parameter estimation, which allows parameters to change spatio‐temporally, shows promise in quantifying and mitigating syst...
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2024-02-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023MS003821 |
| _version_ | 1826946664341962752 |
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| author | Yohei Sawada Le Duc |
| author_facet | Yohei Sawada Le Duc |
| author_sort | Yohei Sawada |
| collection | DOAJ |
| description | Abstract The accuracy of Earth system models is compromised by unknown and/or unresolved dynamics, making the quantification of systematic model errors essential. While a model parameter estimation, which allows parameters to change spatio‐temporally, shows promise in quantifying and mitigating systematic model errors, the estimation of the spatio‐temporally distributed model parameters has been practically challenging. Here we present an efficient and practical method to estimate time‐varying parameters in high‐dimensional spaces. In our proposed method, Hybrid Offline and Online Parameter Estimation with ensemble Kalman filtering (HOOPE‐EnKF), model parameters estimated by EnKF are constrained by results of offline batch optimization, in which the posterior distribution of model parameters is obtained by comparing simulated and observed climatological variables. HOOPE‐EnKF outperforms the original EnKF in synthetic experiments using a two‐scale Lorenz96 model and a simple global general circulation model. One advantage of HOOPE‐EnKF over traditional EnKFs is that its performance is not greatly affected by inflation factors for model parameters, thus eliminating the need for extensive tuning of inflation factors. We thoroughly discuss the potential of HOOPE‐EnKF as a practical method for improving parameterizations of process‐based models and prediction in real‐world applications such as numerical weather prediction. |
| first_indexed | 2024-03-07T21:31:11Z |
| format | Article |
| id | doaj.art-e3cdaef4bf8b472dabde78512ebb1492 |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2025-02-17T21:15:28Z |
| publishDate | 2024-02-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-e3cdaef4bf8b472dabde78512ebb14922024-12-07T16:07:03ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662024-02-01162n/an/a10.1029/2023MS003821An Efficient and Robust Estimation of Spatio‐Temporally Distributed Parameters in Dynamic Models by an Ensemble Kalman FilterYohei Sawada0Le Duc1Institute of Engineering Innovation Graduate School of Engineering the University of Tokyo Tokyo JapanInstitute of Engineering Innovation Graduate School of Engineering the University of Tokyo Tokyo JapanAbstract The accuracy of Earth system models is compromised by unknown and/or unresolved dynamics, making the quantification of systematic model errors essential. While a model parameter estimation, which allows parameters to change spatio‐temporally, shows promise in quantifying and mitigating systematic model errors, the estimation of the spatio‐temporally distributed model parameters has been practically challenging. Here we present an efficient and practical method to estimate time‐varying parameters in high‐dimensional spaces. In our proposed method, Hybrid Offline and Online Parameter Estimation with ensemble Kalman filtering (HOOPE‐EnKF), model parameters estimated by EnKF are constrained by results of offline batch optimization, in which the posterior distribution of model parameters is obtained by comparing simulated and observed climatological variables. HOOPE‐EnKF outperforms the original EnKF in synthetic experiments using a two‐scale Lorenz96 model and a simple global general circulation model. One advantage of HOOPE‐EnKF over traditional EnKFs is that its performance is not greatly affected by inflation factors for model parameters, thus eliminating the need for extensive tuning of inflation factors. We thoroughly discuss the potential of HOOPE‐EnKF as a practical method for improving parameterizations of process‐based models and prediction in real‐world applications such as numerical weather prediction.https://doi.org/10.1029/2023MS003821parameter estimationdata assimilationensemble Kalman filter |
| spellingShingle | Yohei Sawada Le Duc An Efficient and Robust Estimation of Spatio‐Temporally Distributed Parameters in Dynamic Models by an Ensemble Kalman Filter Journal of Advances in Modeling Earth Systems parameter estimation data assimilation ensemble Kalman filter |
| title | An Efficient and Robust Estimation of Spatio‐Temporally Distributed Parameters in Dynamic Models by an Ensemble Kalman Filter |
| title_full | An Efficient and Robust Estimation of Spatio‐Temporally Distributed Parameters in Dynamic Models by an Ensemble Kalman Filter |
| title_fullStr | An Efficient and Robust Estimation of Spatio‐Temporally Distributed Parameters in Dynamic Models by an Ensemble Kalman Filter |
| title_full_unstemmed | An Efficient and Robust Estimation of Spatio‐Temporally Distributed Parameters in Dynamic Models by an Ensemble Kalman Filter |
| title_short | An Efficient and Robust Estimation of Spatio‐Temporally Distributed Parameters in Dynamic Models by an Ensemble Kalman Filter |
| title_sort | efficient and robust estimation of spatio temporally distributed parameters in dynamic models by an ensemble kalman filter |
| topic | parameter estimation data assimilation ensemble Kalman filter |
| url | https://doi.org/10.1029/2023MS003821 |
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