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: | Eviatar Bach, Michael Ghil |
---|---|
Format: | Article |
Language: | English |
Published: |
American Geophysical Union (AGU)
2023-01-01
|
Series: | Journal of Advances in Modeling Earth Systems |
Subjects: | |
Online Access: | https://doi.org/10.1029/2022MS003123 |
Similar Items
-
On the Choice of Metric to Calibrate Time-Invariant Ensemble Kalman Filter Hyper-Parameters for Discharge Data Assimilation and Its Impact on Discharge Forecast Modelling
by: Jean Bergeron, et al.
Published: (2021-02-01) -
On the consistency of the local ensemble square root Kalman filter perturbation update
by: Marc Bocquet, et al.
Published: (2019-01-01) -
Comparison of Perturbation Strategies for the Initial Ensemble in Ocean Data Assimilation with a Fully Coupled Earth System Model
by: Shaokun Deng, et al.
Published: (2022-03-01) -
Ionospheric Assimilation of GNSS TEC into IRI Model Using a Local Ensemble Kalman Filter
by: Jun Tang, et al.
Published: (2022-07-01) -
Water Level Simulation in River Network by Data Assimilation Using Ensemble Kalman Filter
by: Yifan Chen, et al.
Published: (2023-02-01)