  The feasibility of global ocean state estimation by sequential data
assimilation is demonstrated. The model componenet of the assimilator is the
GROB version of the MPIMET ocean circulation model HOPE. Assimilation uses the
Fokker-Planck representation of the Kalman Filter. This approach determines the
temporal evolution of error statistics by integration of the Fokker-Planck
Equation. Phase space advection and diffusion are obtained from histogram
techniques considering the model as a black box. For efficiency, the estimate
combines nudging and Kalman Filtering. The ocean state is estimated for the El
Nino year 1997 by dynamical extrapolation of observed sea-surface temperatures
and TAO/Triton subsurface temperatures. The model-data combination yields
improved estimates of the ocean's mean state and a realistic record of El Nino
related variability. The assimilator proves as an efficient, viable and thus
practical approach to operational global ocean state estimation.
