In this work, we use a tempering-based adaptive particle filter to infer from a partially observed stochastic rotating shallow water (SRSW) model which has been derived using the Stochastic Advection by Lie Transport (SALT) approach. The methodology we present here validates the applicability of tempering and sample regeneration via a Metropolis-Hastings algorithm to high-dimensional models used in stochastic fluid dynamics. The methodology is first tested on the Lorenz '63 model with both full and partial observations. Then we discuss the efficiency of the particle filter the SALT-SRSW model.
翻译:在这项工作中,我们使用一个基于温带的适应性粒子过滤器,从使用 " 利用谎言运输的蒸汽对流法 " (SALT)方法得出的部分观测到的随机旋转浅水模型(SRSW)中推断出。我们在此介绍的方法证实,通过大都会-哈斯廷斯算法,调节和样品再生适用于在蒸气流动态中使用的高维模型。该方法首先在Lorenz'63 模型中测试,同时进行全部和部分观察。然后我们讨论粒子过滤SALT-SRSW模型的效率。