Testing particle filters on simple convective-scale models Part l: A stochastic cloud model

摘要

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.