An Efficient and Robust Estimation of Spatio‐Temporally Distributed Parameters in Dynamic Models by an Ensemble Kalman Filter

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 mo...

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Veröffentlicht in:Journal of advances in modeling earth systems Jg. 16; H. 2
Hauptverfasser: Sawada, Yohei, Duc, Le
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Washington John Wiley & Sons, Inc 01.02.2024
American Geophysical Union (AGU)
Schlagworte:
Data assimilation
Dynamic models
ensemble Kalman filter
General circulation models
Kalman filters
Optimization
Parameter estimation
Parameters
Weather forecasting
ISSN:1942-2466, 1942-2466
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Zusammenfassung: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. Plain Language Summary Earth system models help us understand and predict the behavior of our planet, but their accuracy is limited due to unknown or unresolved factors. Adjusting the parameters of these models based on the changing patterns in time and space can help improve their accuracy. However, this has been a challenging task. In this study, we introduce a new method called Hybrid Offline and Online Parameter Estimation with ensemble Kalman filtering (HOOPE‐EnKF) that efficiently estimates these changing parameters. This method combines an existing EnKF with offline batch optimization, which compares long‐term simulation with observations to fine‐tune the model. Our experiments show that HOOPE‐EnKF performs better than the original EnKF and is less sensitive to certain hyperparameters, reducing the need for time‐consuming calibrations. HOOPE‐EnKF has a great potential for improving the accuracy of models and predictions in real‐world applications, like weather forecasting, and can help scientists better understand our planet's complex systems. Key Points Ensemble Kalman filter (EnKF) is extended to estimate high‐dimensional time‐varying model parameters The proposed method is insensitive to the choice of covariance inflation factors The proposed method successfully quantifies systematic model errors from observations
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ISSN:1942-2466
1942-2466
DOI:10.1029/2023MS003821