A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting
Abstract Data assimilation (DA) aims to optimally combine model forecasts and observations that are both partial and noisy. Multi‐model DA generalizes the variational or Bayesian formulation of the Kalman filter, and we prove that it is also the minimum variance linear unbiased estimator. Here, we f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2023-01-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2022MS003123 |
| _version_ | 1811164459367923712 |
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| author | Eviatar Bach Michael Ghil |
| author_facet | Eviatar Bach Michael Ghil |
| author_sort | Eviatar Bach |
| collection | DOAJ |
| description | Abstract Data assimilation (DA) aims to optimally combine model forecasts and observations that are both partial and noisy. Multi‐model DA generalizes the variational or Bayesian formulation of the Kalman filter, and we prove that it is also the minimum variance linear unbiased estimator. Here, we formulate and implement a multi‐model ensemble Kalman filter (MM‐EnKF) based on this framework. The MM‐EnKF can combine multiple model ensembles for both DA and forecasting in a flow‐dependent manner; it uses adaptive model error estimation to provide matrix‐valued weights for the separate models and the observations. We apply this methodology to various situations using the Lorenz96 model for illustration purposes. Our numerical experiments include multiple models with parametric error, different resolved scales, and different fidelities. The MM‐EnKF results in significant error reductions compared to the best model, as well as to an unweighted multi‐model ensemble, with respect to both probabilistic and deterministic error metrics. |
| first_indexed | 2024-04-10T15:22:50Z |
| format | Article |
| id | doaj.art-98a2f75b957d4cbd8aa219ada67a5d29 |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-04-10T15:22:50Z |
| publishDate | 2023-01-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-98a2f75b957d4cbd8aa219ada67a5d292023-02-14T13:45:31ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662023-01-01151n/an/a10.1029/2022MS003123A Multi‐Model Ensemble Kalman Filter for Data Assimilation and ForecastingEviatar Bach0Michael Ghil1Geosciences Department and Laboratoire de Météorologie Dynamique (CNRS and IPSL) École Normale Supérieure and PSL University Paris FranceGeosciences Department and Laboratoire de Météorologie Dynamique (CNRS and IPSL) École Normale Supérieure and PSL University Paris FranceAbstract Data assimilation (DA) aims to optimally combine model forecasts and observations that are both partial and noisy. Multi‐model DA generalizes the variational or Bayesian formulation of the Kalman filter, and we prove that it is also the minimum variance linear unbiased estimator. Here, we formulate and implement a multi‐model ensemble Kalman filter (MM‐EnKF) based on this framework. The MM‐EnKF can combine multiple model ensembles for both DA and forecasting in a flow‐dependent manner; it uses adaptive model error estimation to provide matrix‐valued weights for the separate models and the observations. We apply this methodology to various situations using the Lorenz96 model for illustration purposes. Our numerical experiments include multiple models with parametric error, different resolved scales, and different fidelities. The MM‐EnKF results in significant error reductions compared to the best model, as well as to an unweighted multi‐model ensemble, with respect to both probabilistic and deterministic error metrics.https://doi.org/10.1029/2022MS003123ensemble Kalman filtermulti‐model ensemble |
| spellingShingle | Eviatar Bach Michael Ghil A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting Journal of Advances in Modeling Earth Systems ensemble Kalman filter multi‐model ensemble |
| title | A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting |
| title_full | A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting |
| title_fullStr | A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting |
| title_full_unstemmed | A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting |
| title_short | A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting |
| title_sort | multi model ensemble kalman filter for data assimilation and forecasting |
| topic | ensemble Kalman filter multi‐model ensemble |
| url | https://doi.org/10.1029/2022MS003123 |
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