%0 Journal Article
%T Sigma-Point Particle Filter for Parameter Estimation in a Multiplicative Noise Environment
%A Jaison Thomas Ambadan
%A Youmin Tang
%J Journal of Advances in Modeling Earth Systems
%D 2011
%I 
%X A pre-requisite for the ˇ°optimal estimateˇ± by the ensemble-based Kalman filter (EnKF) is the Gaussian assumption for background and observation errors, which is often violated when the errors are multiplicative, even for a linear system. This study first explores the challenge of the multiplicative noise to the current EnKF schemes. Then, a Sigma Point Kalman Filter based Particle Filter (SPPF) is presented as an alternative to solve the issues associated with multiplicative noise. The classic Lorenz '63 model and a higher dimensional Lorenz '96 model are used as test beds for the data assimilation experiments. Performance of the SPPF algorithm is compared against a standard EnKF as well as an advanced square-root Sigma-Point Kalman Filters (SPKF). The results show that the SPPF outperforms the EnKF and the square-root SPKF in the presence of multiplicative noise. The super ensemble structure of the SPPF makes it computationally attractive compared to the standard Particle Filter (PF).
%K Earth system modeling
%K Numerical weather prediction
%U http://www.agu.org/journals/ms/ms1112/2011MS000065/