Abstract
Abstract. Particle filters have become a popular algorithm in data assimilation for their ability to handle nonlinear or non-Gaussian state-space models, but they have significant disadvantages. In this work, an improved particle filter algorithm is proposed. To overcome the particle degeneration and improve particles' efficiency, the processes of particle resampling and particle transfer are updated. In this improved algorithm, particle propagation and the resampling method are ameliorated. The new particle filter is applied to the Lorenz-63 model, and its feasibility and effectiveness are verified using only 20 particles. The root-mean-square difference (RMSD) of estimations converges to stable when there are more than 20 particles. Finally, we choose a peristaltic landslide model and carry out an assimilation experiment of 20 days. Results show that the estimations of states can effectively correct the running offset of the model and the RMSD is convergent after 3 days of assimilation.
Highlights
Mountainous areas all over the world suffer frequent landslide disasters
The problems of particle degeneration and efficient expression of posterior probability density function (PDF) are long-term difficulties that affect the performance of particle filters
We propose two approaches to improve the particle filter process
Summary
Mountainous areas all over the world suffer frequent landslide disasters. Works of landslide monitoring, analysis and forecasting are crucial. Xue et al.: Data assimilation with an improved particle filter the range of observation error (van Leeuwen, 2010) This method can achieve good results using only 10–20 particles in high-dimensional assimilation experiments. The number of key particles is reduced when the system variance is larger than the observed variance, and the values of added items are uncertain Another improvement is to replace the duplicating process by generating a Halton sequence in residual resampling (Zhang et al, 2013). To predict the safety factor of peristaltic landslide, a simulation experiment, applied to the Lorenz-63 model using different numbers of particles, ranging between 10 and 200, is explained, which demonstrates that the new method shows efficiency and sensitivity to the number of particles. The improved assimilation algorithm is applied to the TRIGRS program to evaluate the change of factor of safety (FS) in the experimental model
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