Sea Ice Modeling and Data Assimilation (SIMDA)

Integrated Foundations of Sensing, Modeling, and Data Assimilation for Sea Ice Prediction
Department of Defense Multidisciplinary University Research Initiatives program through the Office of Naval Research

Multi marginal optimal transport

17 Oct 2022 - Bohan Zhou


B. Zhou and M. Parno are working on a general multi-marginal optimal transport (MMOT) framework to obtain the continuous representation of discrete-in-time data. Given observations (called as marginals), our algorithm provides a prediction on the sea ice dynamics in a continuous time. This provides a solution to some stage in the Lagrangian Observation Mapping. The python package with its description can be found here.

Using the SAR data (every 6 days) obtained from Alaska Satellite Facility on the Robertson Channel (thanks to J. Park), the video shows the prediction dynamics every 2 day.