Data assimilation with an improved particle filter and its application in the TRIGRS landslide model
<p>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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-10-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://www.nat-hazards-earth-syst-sci.net/18/2801/2018/nhess-18-2801-2018.pdf |
| Summary: | <p>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.</p> |
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| ISSN: | 1561-8633 1684-9981 |