[M-GI26] Data assimilation: A fundamental approach in geosciences

[MGI26-P05] Online state and time-varying parameter estimation

*Mineto Satoh¹, Peter Jan van Leeuwen², Shin ya Nakano³ (1.The Graduate University for Advanced Studies, 2.Colorado State University, 3.The Institute of Statistical Mathematics)

Keywords:Data assimilation, Particle filter, Optimization

A method is proposed for resilient and efficient estimation of the state- and time-varying parameters in nonlinear high-dimensional systems through a sequential data assimilation process. The importance of estimating time-varying parameters lies not only in improving prediction accuracy but also in determining when model characteristics change. We propose a particle-filter-based method that incorporates an optimization algorithm from machine learning into “the particle filter” by exploiting the freedom of the proposal density in particle filtering. However, as the model resolution and number of observations increase, filter degeneracy tends to be the main obstacle to implementing the particle filter. Therefore, this proposed method is combined with the implicit equal-weights particle filter (IEWPF), in which all particle weights are equal. The method is validated using the 1000-dimensional Lorenz-96 model, where the forcing term is parameterized. The proposed approach is shown to be capable of resilient and efficient parameter estimation for parameter changes over time, leading to the conjecture that it is applicable to realistic geophysical, climate, and other problems.