Particle Filtering and Gaussian Mixtures – On a Localized Mixture Coefficients Particle Filter (LMCPF) for Global NWP
Author:
Affiliation:
1. Data Assimilation Unit, Deutscher Wetterdienst, Germany
2. Departement of Atmospheric Science, Colorado State University, Colorado, USA
Publisher
Meteorological Society of Japan
Subject
Atmospheric Science
Link
https://www.jstage.jst.go.jp/article/jmsj/101/4/101_2023-015/_pdf
Reference33 articles.
1. Anderson, J. L., and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741–2758.
2. Bain, A., and D. Crisan, 2009: Fundamentals of Stochastic Filtering. Stochastic Modelling and Applied Probability, vol. 60, Springer, 403 pp.
3. Bengtsson, T., C. Snyder, and D. Nychka, 2003: Toward a nonlinear ensemble filter for high-dimensional systems. J. Geophys. Res., 108, D24, doi:10.1029/2002JD002900.
4. Bickel, P., B. Li, and T. Bengtsson, 2008: Sharp failure rates for the bootstrap particle filter in high dimensions. Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh. vol.3, IMS Collections, 318–329.
5. Bishop, C. H., 2016: The GIGG-EnKF: Ensemble Kalamn filtering for highly skewed non-negative uncertainty distributions. Quart. J. Roy. Meteor. Soc., 142, 1395–1412.
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