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• W2287464669 abstract "Convective‐scale applications require data assimilation methods that can cope with nonlinear dynamics and the stochastic nature of convection. For this application, the particle filter is a promising data assimilation method because it estimates the probability density function (PDF) of the atmospheric state and not only its first two moments. However, in order to represent PDFs with a small number of particles, the particle filter is usually combined with another data assimilation technique. In this article we investigate a hybrid algorithm, the nudging proposal particle filter, which combines the sequential importance resampling particle filter with nudging. Analytic and experimental results on an idealized, nonlinear, one‐dimensional model are used to show that there exists a combination of the two methods such that the nudging proposal particle filter outperforms both of its components. In this article, a stochastic cloud model, represented through a birth–death process, serves as a first test model for the filter. The transition probability density can be calculated exactly for this model, thus providing insight into its contribution to the selection of particles during resampling. The functioning mechanism of the nudging proposal particle filter in its simplest form is investigated and the impact of the model parameters on the filter's behaviour highlighted." @default.
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• W2287464669 date "2016-03-21" @default.
• W2287464669 modified "2023-09-24" @default.
• W2287464669 title "Testing particle filters on simple convective‐scale models Part l: A stochastic cloud model" @default.
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• W2287464669 doi "https://doi.org/10.1002/qj.2745" @default.
• W2287464669 hasPublicationYear "2016" @default.
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