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

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

*佐藤 峰斗¹、Peter Jan van Leeuwen²、中野 慎也³ (1.総合研究大学院大学、2.コロラド州立大学、3.統計数理研究所)

キーワード：データ同化、粒子フィルタ、最適化

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.