Adaptive Ensemble Optimal Interpolation for efficient data assimilation in the Red Sea
Ensemble optimal interpolation (EnOI) have been introduced to drastically reduce the computational cost of the ensemble Kalman filter (EnKF). The idea is to use a static (pre-selected) ensemble to parameterize the background covariance matrix, which avoids the costly integration step of the ensemble members with the dynamical model. To better represent the strong variability of the Red Sea circulation, we propose new adaptive EnOI schemes in which the ensemble members are adaptively selected at every assimilation cycle from a large dictionary of ocean states describing the variability of the Red Sea system. Those members would account for the strong eddy and seasonal variability of the Red Sea circulation and enforce climatological smoothness in the filter update. We implement and test different schemes to adaptively choose the ensemble members based on (i) the similarity to the forecast, or (ii) an Orthogonal Matching Pursuit (OMP) algorithm. Results of numerical experiments assimilating remote sensing data into a high-resolution MIT general circulation model (MITgcm) of the Red Sea will be presented to demonstrate the efficiency of the proposed approach.
Dr. Peng Zhan KAUST