EnKF vs. EnOI in the Red Sea and Sensitivity to Atmospheric Forcing

We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system is composed of the MIT general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for data assimilation. In this version, the MITgcm is configured with a horizontal resolution of 4 km and 40 depth-varying vertical layers and nested in larger 10km resolution domain covering all Arabian Seas. The model is forced with real time atmospheric products from the National Center for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF) and high-resolution assimilated Weather Research Forecasting (WRF) Red Sea fields. DART has been configured to integrate all members of the ensemble Kalman filter (EnKF) in parallel, and for testing with an invariant ensemble, i.e. an ensemble Optimal Interpolation (EnOI). To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is seasonally selected from climatology of long-term model outputs. Observations of Sea surface height (SSH) and sea surface temperature (SST) are assimilated every three days. We examine the behaviors of the EnKF and EnOI and compare their performances. We further investigate the impacts of the different atmospheric forcings on the results of the assimilation system.