日本地球惑星科学連合2023年大会

講演情報

[E] 口頭発表

セッション記号 M (領域外・複数領域) » M-GI 地球科学一般・情報地球科学

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

2023年5月22日(月) 09:00 〜 10:30 301B (幕張メッセ国際会議場)

コンビーナ:中野 慎也(情報・システム研究機構 統計数理研究所)、藤井 陽介(気象庁気象研究所)、三好 建正(理化学研究所)、加納 将行(東北大学理学研究科)、座長:加納 将行(東北大学理学研究科)、中野 慎也(情報・システム研究機構 統計数理研究所)

09:20 〜 09:35

[MGI26-02] An Efficient Estimation of Time-Varying Parameters of Dynamic Models by Combining Offline Batch Optimization and Online Data Assimilation

*澤田 洋平1 (1.東京大学 工学系研究科)

キーワード:粒子フィルタ、パラメータ最適化

It is crucially important to estimate unknown parameters in process-based models by integrating observation and numerical simulation. For many applications in earth system sciences, a parameter estimation method which allows parameters to temporally change is required. In the present paper, an efficient and practical method to estimate time-varying parameters of relatively low dimensional models is presented. In the newly proposed method, called Hybrid Offline Online Parameter Estimation with Particle Filtering (HOOPE-PF), an inflation method to maintain the spread of ensemble members in a Sampling-Importance-Resampling Particle Filter (SIRPF) is improved using a non-parametric posterior probabilistic distribution of time-invariant parameters obtained by comparing simulated and observed climatology. HOOPE-PF outperforms the original SIRPF in synthetic experiments with toy models and a real-data experiment with a conceptual hydrological model when an ensemble size is small. The advantage of HOOPE-PF is that its performance is not greatly affected by the size of perturbation to be added to ensemble members to maintain their spread while it is important to get the optimal performance in the original particle filter. Since HOOPE-PF is the extension of the existing particle filter which has been extensively applied to many models in earth system sciences such as land, ecosystem, hydrology, and paleoclimate reconstruction, HOOPE-PF can be applied to improve the simulation of these process-based models by considering time-varying model parameters.