Using the Kalman filter for parameter estimation in biogeochemical models
CM Trudinger, MR Raupach, PJ Rayner, IG Enting
ENVIRONMETRICS | WILEY | Published : 2008
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 obs..View full abstract
We thank Mathew Williams and Peter Oke for Valuable comments on the manuscript. The Centre of Excellence for Mathematics and Statistics of Complex Systems (MASCOS) is funded by the Australian Research Council. lan Enting's fellowship at MASCOS is Supported by CSIRO sponsorship.