TY - JOUR
TI - Data assimilation for non-linear systems with a hybrid non-linear particle filter
AU - Zhou Meng-Ge
AU - Cao Xiao-Qun
AU - Chen Yan
AU - Luo Yu-Chen
AU - Yao Jia-Le
JN - Thermal Science
PY - 2026
VL - 30
IS - 2
SP - 973
EP - 981
PT - Article
AB - The proposed methodology utilizes a novel data assimilation technique based  on hybrid non-linear particle filters, a framework that has demonstrated  efficacy in non-linear and non-Gaussian scenarios within the domain of Earth  sciences. In numerical sensitivity experiments conducted on a non-linear  dynamical system (Lorenz 63), the new method prevents filter divergence  using only 10 particles for both dense and sparse observation networks. A  comparison of the newly developed hybrid non-linear method with the local  ensemble transform Kalman filter (LETKF) reveals the merits of the former in  data assimilation applications analogous to geophysical data. Specifically,  the newly developed filter exhibits significant advantages over the LETKF,  particularly when the observation network consists of densely spaced  measurements that are non-linearly related to the model state, akin to  remote sensing data frequently employed in atmospheric analyses.
DO - 10.2298/TSCI2602973Z
ER -