Event
01_1
| 제출번호(No.) | 0081 |
|---|---|
| 분류(Section) | Contributed Talk |
| 분과(Session) | Applied Mathematics (AM) |
| 영문제목 (Title(Eng.)) |
Performance of Ensemble-based Filters for an idealized Ocean Thermohaline Circulation model |
| 저자(Author(s)) |
Sangil Kim1, Il-Hyo Jung2 Hankuk University of Foreign Studies1, Pusan National University2 |
| 초록본문(Abstract) | The sequential filtering scheme provides a suitable framework for estimating and tracking geophysical states of systems as new data become available on line. Mathematical foundations of sequential Bayesian filtering are reviewed with emphasis on practical issues for both particle filters and Kalman-based filters. In this study, the sequential Importance resampling method (SIR), Ensemble Kalman Filter (EnKF), and the Maximum Entropy Filter (MEF) are tested in a relatively high dimensional ocean model that conceptually represents the Atlantic thermohaline circulation. The model exhibits large-amplitude transitions between strong (thermo-dominated) and weak (salinity-dominated) circulations that represent climate states between ice-age and normal climate. The performance of the particle-based schemes is compared with the convergent results from SIR based on measurement errors, observation locations, and particle sizes in various sets of twin experiments. The sensitivity analysis shows strength and weakness of each filtering method when applied to multimodal non-linear systems. As the number of particles is increased, SIR achieves the convergent results that are mathematically optimal solutions. EnKF shows suboptimal results regardless of sample sizes, and MEF achieves the optimal solution even with a small sample size. Both EnKF, and MEF produces robust results with a relatively small sample size or increased measurement locations. Small measurement errors or short intervals of observations (or, more frequent observations) significantly improve the performances of SIR and EnKF, and MEF still show robust results even with a relatively small sample size or sparse measurement locations when the system experiences the transition between one region to the other region. |
| 분류기호 (MSC number(s)) |
35R30 |
| 키워드(Keyword(s)) | ocean state estimation, sequential Monte Carlo methods, sequential importance resampling, ensemble Kalman filter, maximum entropy filter |
| 강연 형태 (Language of Session (Talk)) |
Korean |