Citation: | Yuxin Zhao, Shuo Yang, Renfeng Jia, Di Zhou, Xiong Deng, Chang Liu, Xinrong Wu. The statistical observation localized equivalent-weights particle filter in a simple nonlinear model[J]. Acta Oceanologica Sinica, 2022, 41(2): 80-90. doi: 10.1007/s13131-021-1876-1 |
[1] |
Ades M, Van Leeuwen P J. 2013. An exploration of the equivalent weights particle filter. Quarterly Journal of the Royal Meteorological Society, 139(672): 820–840. doi: 10.1002/qj.1995
|
[2] |
Ades M, Van Leeuwen P J. 2015. The equivalent-weights particle filter in a high-dimensional system. Quarterly Journal of the Royal Meteorological Society, 141(687): 484–503. doi: 10.1002/qj.2370
|
[3] |
Chen Yan, Zhang Weimin, Wang Pingqiang. 2020. An application of the localized weighted ensemble Kalman filter for ocean data assimilation. Quarterly Journal of the Royal Meteorological Society, 146(732): 3029–3047. doi: 10.1002/qj.3824
|
[4] |
Chorin A J, Tu Xuemin. 2009. Implicit sampling for particle filters. Proceedings of the National Academy of Sciences of the United States of America, 106(41): 17249–17254. doi: 10.1073/pnas.0909196106
|
[5] |
De Freitas N, Andrieu C, Højen-Sørensen P, et al. 2001. Sequential monte Carlo methods for neural networks. In: Doucet A, De Freitas N, Gordon N, eds. Sequential Monte Carlo Methods in Practice. New York, NY, USA: Springer, 359–379
|
[6] |
Evensen G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5): 10143–10162. doi: 10.1029/94JC00572
|
[7] |
Gaspari G, Cohn S E. 1999. Construction of correlation functions in two and three dimensions. Quarterly Journal of the Royal Meteorological Society, 125(554): 723–757. doi: 10.1002/qj.49712555417
|
[8] |
Gordon N J, Salmond D J, Smith A F M. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2): 107–113. doi: 10.1049/ip-f-2.1993.0015
|
[9] |
Houtekamer P L, Mitchell H L. 1998. Data assimilation using an ensemble Kalman filter technique. Monthly Weather Review, 126(3): 796–811. doi: 10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2
|
[10] |
Law K, Stuart A, Zygalakis K. 2015. Data Assimilation: A Mathematical Introduction. Cham, Switzerland: Springer
|
[11] |
Lei Jing, Bickel P. 2011. A moment matching ensemble filter for nonlinear non-Gaussian data assimilation. Monthly Weather Review, 139(12): 3964–3973. doi: 10.1175/2011MWR3553.1
|
[12] |
Lorenz E N. 1995. Predictability: a problem partly solved. In: Proceedings Seminar on Predictability. Reading, UK: ECMWF
|
[13] |
Nakano S, Ueno G, Higuchi T. 2007. Merging particle filter for sequential data assimilation. Nonlinear Processes in Geophysics, 14(4): 395–408. doi: 10.5194/npg-14-395-2007
|
[14] |
Poterjoy J. 2016. A localized particle filter for high-dimensional nonlinear systems. Monthly Weather Review, 144(1): 59–76. doi: 10.1175/MWR-D-15-0163.1
|
[15] |
Poterjoy J, Anderson J L. 2016. Efficient assimilation of simulated observations in a high-dimensional geophysical system using a localized particle filter. Monthly Weather Review, 144(5): 2007–2020. doi: 10.1175/MWR-D-15-0322.1
|
[16] |
Robert S, Leuenberger D, Künsch H R. 2018. A local ensemble transform Kalman particle filter for convective-scale data assimilation. Quarterly Journal of the Royal Meteorological Society, 144(713): 1279–1296. doi: 10.1002/qj.3116
|
[17] |
Shen Zheqi, Tang Youmin, Li Xiaojing. 2017. A new formulation of vector weights in localized particle filter. Quarterly Journal of the Royal Meteorological Society, 143(709): 3269–3278. doi: 10.1002/qj.3180
|
[18] |
Shen Zheqi, Zhang Xiangming, Tang Youmin. 2016. Comparison and combination of EAKF and SIR-PF in the Bayesian filter framework. Acta Oceanologica Sinica, 35(3): 69–78. doi: 10.1007/s13131-015-0757-x
|
[19] |
Stordal A S, Karlsen H A, Nævdal G, et al. 2011. Bridging the ensemble Kalman filter and particle filters: the adaptive Gaussian mixture filter. Computational Geosciences, 15(2): 293–305. doi: 10.1007/s10596-010-9207-1
|
[20] |
Van Leeuwen P J. 2010. Nonlinear data assimilation in geosciences: an extremely efficient particle filter. Quarterly Journal of the Royal Meteorological Society, 136(653): 1991–1999. doi: 10.1002/qj.699
|
[21] |
Van Leeuwen P J. 2011. Efficient nonlinear data-assimilation in geophysical fluid dynamics. Computers & Fluids, 46(1): 52–58
|
[22] |
Van Leeuwen P J. 2015. Aspects of particle filtering in high-dimensional spaces. In: First International Conference on Dynamic Data-Driven Environmental Systems Science. Cambridge, UK: Springer, 251–262
|
[23] |
Van Leeuwen P J, Evensen G. 1996. Data assimilation and inverse methods in terms of a probabilistic formulation. Monthly Weather Review, 124(12): 2898–2913. doi: 10.1175/1520-0493(1996)124<2898:DAAIMI>2.0.CO;2
|
[24] |
Van Leeuwen P J, Künsch H R, Nerger L, et al. 2019. Particle filters for high-dimensional geoscience applications: a review. Quarterly Journal of the Royal Meteorological Society, 145(723): 2335–2365. doi: 10.1002/qj.3551
|
[25] |
Whitaker J S, Hamill T M. 2012. Evaluating methods to account for system errors in ensemble data assimilation. Monthly Weather Review, 140(9): 3078–3089. doi: 10.1175/MWR-D-11-00276.1
|
[26] |
Zhang S. 2011. A study of impacts of coupled model initial shocks and state–parameter optimization on climate predictions using a simple pycnocline prediction model. Journal of Climate, 24(23): 6210–6226. doi: 10.1175/JCLI-D-10-05003.1
|
[27] |
Zhu Mengbin, Van Leeuwen P J, Amezcua J. 2016. Implicit equal‐weights particle filter. Quarterly Journal of the Royal Meteorological Society, 142(698): 1904–1919. doi: 10.1002/qj.2784
|