Comparison of Sequential Assimilation Schemes for Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM): Twin Experiments with Static Forecast Error Covariances

摘要

We assess and compare four sequential data assimilation methods developed for HYCOM in an identical twin experiment framework. The methods considered are Multi-variate Optimal Interpolation (MVOI), Ensemble Optimal Interpolation (EnOI), the fixed basis version of the Singular Evolutive Extended Kaiman Filter (SEEK) and Ensemble Reduced Oder Information Filter (EnROIF). All methods can be classified as statistical interpolation but differ mainly in how the forecast error covariances are modeled. Surface elevation and temperature data sampled form an 1/12 degree Gulf of Mexico HYCOM simulated designatedas the truth are assimilated into an identical model starting from an erroneous initial state, and convergence of assimilative runs towards the truth is tracked. We also present a discussion of the numerical implementation and the computational requirements for the use of these methods in large scale applications.