Colin Guider, UNC Chapel Hill – GMA & Visions Seminar

Title: Data Assimilation with an Application to Sea Ice Modelling

Abstract: The abundance of oceanic and atmospheric data presents the question of how to incorporate this data into scientific models.  Data assimilation is the process by which observations are fused with these models.  We consider both variational and statistical approaches to data assimilation, which are equivalent under reasonable assumptions.  We then derive the Kalman filter, one of the most famous and useful algorithms in the field.  A practical implementation of this process, the Ensemble Kalman Filter (EnKF), can be modified for use in neXtSIM, an arctic sea ice model.