Combined Parameter and State Estimation in Lagrangian Data Assimilation

Tuesday, November 19, 2013 - 3:30pm - 3:50pm
Lind 305
Naratip Santitissadeekorn (University of North Carolina, Chapel Hill)
Inferring parameters in a geophysical flow model is a challenge for Lagrangian data assimilation
(LaDA). We present a filtering-based method that combines particle filter and ENKF to track time-varying state vectors (positions of drifters) and fixed model parameters in a quasi-geostrophic two-layer shallow water model. Our method uses a dual strategy that performs parameter estimation by particle filtering and subsequently use the best parameter to track the position of drifters by ENKF. This method will suit a situation where the parameter space is low-dimensional but the state vector (the drifters) is high-dimensional.
MSC Code: