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Using the Kalman filter for parameter estimation in biogeochemical models
10.1002/env.910.abs
We investigate application of nonlinear variants of the Kalman filter (KF) to sequential parameter estimation in biogeochemical models, with particular focus on two components of the statistical model: Q, the covariance of the stochastic forcing which we use to represent model error, and R, the observation error covariance matrix. We explored sensitivity of parameter estimates from the extended and ensemble Kalman filters (EKF and EnKF) to the choice of Q, R, initial parameters and ensemble size using pseudo‐data from a simple yet highly nonlinear test model with many characteristics similar to real terrestrial biogeochemistry models. We found for our application that the use of inflated observation uncertainties led to the best and most stable parameter estimates. Although this reduced the rate of convergence to a solution, it also reduced the sensitivity of the solution to model error or ensemble size in the EnKF. Neither the use of model error for the parameters nor inflation of the state error covariance was particularly successful. Copyright © 2008 John Wiley & Sons, Ltd.
Using the Kalman filter for parameter estimation in biogeochemical models
10.1002/env.910.abs
We investigate application of nonlinear variants of the Kalman filter (KF) to sequential parameter estimation in biogeochemical models, with particular focus on two components of the statistical model: Q, the covariance of the stochastic forcing which we use to represent model error, and R, the observation error covariance matrix. We explored sensitivity of parameter estimates from the extended and ensemble Kalman filters (EKF and EnKF) to the choice of Q, R, initial parameters and ensemble size using pseudo‐data from a simple yet highly nonlinear test model with many characteristics similar to real terrestrial biogeochemistry models. We found for our application that the use of inflated observation uncertainties led to the best and most stable parameter estimates. Although this reduced the rate of convergence to a solution, it also reduced the sensitivity of the solution to model error or ensemble size in the EnKF. Neither the use of model error for the parameters nor inflation of the state error covariance was particularly successful. Copyright © 2008 John Wiley & Sons, Ltd.
Using the Kalman filter for parameter estimation in biogeochemical models
Trudinger, C. M. (author) / Raupach, M. R. (author) / Rayner, P. J. (author) / Enting, I. G. (author)
Environmetrics ; 19 ; 849-870
2008-12-01
22 pages
Article (Journal)
Electronic Resource
English
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