Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 33, issue 1
Articles | Volume 33, issue 1
https://doi.org/10.5194/npg-33-33-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Collection:
https://doi.org/10.5194/npg-33-33-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 33, issue 1
Research article
 | 
26 Jan 2026
Research article |  | 26 Jan 2026

Localization in the mapping particle filter

Juan M. Guerrieri, Manuel Pulido, Takemasa Miyoshi, Arata Amemiya, and Juan J. Ruiz

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Amezcua, J., Ide, K., Bishop, C. H., and Kalnay, E.: Ensemble clustering in deterministic ensemble Kalman filters, Tellus A: Dynamic Meteorology and Oceanography, 64, 18039, https://doi.org/10.3402/tellusa.v64i0.18039, 2012. a
Ba, J., Erdogdu, M. A., Ghassemi, M., Sun, S., Suzuki, T., Wu, D., and Zhang, T.: Understanding the variance collapse of SVGD in high dimensions, International Conference on Learning Representations, https://openreview.net/forum?id=Qycd9j5Qp9J (last access: 20 January 2026), 2022. a, b
Bengtsson, T., Snyder, C., and Nychka,D.: Toward a nonlinear ensemble filter for high-dimensional systems, J. Geophys. Res., 108, 8775, https://doi.org/10.1029/2002JD002900, 2003. a
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Monthly Weather Review, 129, 420–436, https://doi.org/10.1175/1520-0493(2001)129<0420:aswtet>2.0.co;2, 2001. a
Cotter, C. and Crisan, D., Holm, D., Pan, W., and Shevchenko, I.: A Particle Filter for Stochastic Advection by Lie Transport: A Case Study for the Damped and Forced Incompressible Two-Dimensional Euler Equation, SIAM/ASA Journal on Uncertainty Quantification, 8, 1446–1492, https://doi.org/10.1137/19M1277606, 2020. a
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This work extends the Mapping Particle Filter to account for local dependencies. Two localization methods are tested: a global particle flow with local kernels, and iterative local mappings based on correlation radius. Using a two-scale Lorenz-96 truth and a one-scale forecast model, experiments with full/partial and linear/nonlinear observations show Root Mean Square Error reductions using localized Gaussian mixture priors, achieving competitive performance against Gaussian filters.

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