Session 3 Advances in Ensemble-Based Data Assimilation Methodologies for Highly Nonlinear and Large-Dimensional Systems. Part I

Monday, 13 January 2020: 2:00 PM-4:00 PM
259A (Boston Convention and Exhibition Center)
Host: 24th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS)
Yue (Michael) Ying, NCAR, Advanced Study Program, Boulder, CO and Jeffrey L. Anderson, NCAR, DAReS, Boulder, CO

Ensemble-based data assimilation methods have shown amazing skill in reducing initial condition errors for numerical weather prediction. However, challenges still remain for basic ensemble Kalman filter algorithms to perform optimally when applied to highly nonlinear dynamical systems with complex relations between observations and states. Recently, many research groups have made progress in advancing methodologies for scenarios such as nonlinear and multi-scale dynamical systems, deviation from Gaussian distribution for state variables, and complex observation operators. The shear complexity of data assimilation and modeling systems often limits the communication and collaboration among researchers with different scientific foci. Yet, inter-disciplinary collaboration is an important step in cracking the nonlinear data assimilation problem. In this session, we welcome researchers to showcase their new findings in developing ensemble filtering methods that provide better results than basic ensemble Kalman filters for problems with large dimensions and strong nonlinearity. We also encourage authors that participate in this session to provide their insights on what should be the commonly adopted benchmark test cases that reflect the current data assimilation challenges. We envision that the discussion stemming from this session will contribute to wider collaboration among researchers to push forward the frontiers of data assimilation science.

2:30 PM
A Particle Flow Data Assimilation Method for High-Dimensional Systems
Chih-Chi Hu, Colorado State Univ., Fort Collins, CO; and P. J. Van Leeuwen and M. Pulido
2:45 PM
4DEnVar with an Iterative Nonlinear Forecast Model
Sho Yokota, MRI, Tsukuba, Ibaraki, Japan; JMA, Chiyoda-ku, Tokyo, Japan; and K. Koizumi, M. Kunii, and K. Ito
3:00 PM
Why Perturbing Observations in Ensemble Kalman Filters Is Inconsistent
Peter Jan Van Leeuwen, Colorado State Univ., Fort Collins, CO; Univ. of Reading, Reading, UK
3:15 PM
Regularization and Iterative Resampling for the Local Particle Filter
Jonathan Poterjoy, Univ. of Maryland, College Park, College Park, MD; AOML, Miami, FL
3:30 PM
High-Dimensional Ensemble Filtering with Nonlinear Couplings
Ricardo Baptista, MIT, Cambridge, MA; and Y. Marzouk and A. Spantini
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner