Lagrangian DA and UQ for Sea Ice Floes with Superfloe Parameterization
Q. Deng, N. Chen, and S. Stechmann
University of Wisconsin
Abstract
The discrete element method (DEM) is a recently developed new modeling approach for describing sea ice dynamics. It exploits particle-based methods to characterize the physical quantities of each sea ice floe along its trajectory under the Lagrangian coordinates. One major challenge in applying the DEM models is the heavy computational cost when the number of the floes becomes large. In this talk, I will present an efficient Lagrangian parameterization algorithm, which aims at reducing the compu- tational cost of simulating the DEM models while preserving the key features of the sea ice. The new parameterization takes advantage of a small number of superfloes to effectively approximate a considerable number of the floes, where the parameterization scheme satisfies several important physics constraints. The physics constraints guarantee the superfloe parameterized system to have similar short-term dynamical behavior as the full system. These constraints also allow the superfloe parameterized sys- tem to accurately quantify the long-range uncertainty, especially the non Gaussian statistical features, of the full system. In addition, the superfloe parameterization facilitates a systematic noise inflation strategy that significantly advances using the ensemble based data assimilation algorithm for recovering the unobserved ocean field underneath the sea ice. Such a new noise inflation method avoids ad hoc tunings as in many traditional algorithms and is computationally extremely efficient. I will present several numerical experiments to demonstrate the success of the superfloe parameterization. Joint work with Nan Chen and Sam Stechmann.