Results are presented from a decade-long assimilation run
with an 64-member OGCM ensemble in a global configuration.
The intended purpose of the assimilation system is to produce
ocean initial conditions for seasonal forecasts.
The model ensemble is constructed with the Max Planck
Institute Ocean Model, where each member is forced by differently
perturbed ERA40 atmospheric fields over sequential 10-day intervals
to produce ensemble hindcasts. Along-track altimetric data from the ERS and
TOPEX/POSEIDON satellites, as well as quality-controlled subsurface
temperature and salinity profiles are subsequently assimilated using
the standard formulation of the EnKF. The applied forcing
perturbation method, and data selection and processing procedures are
described, as well as a framework for the construction of
appropriate data constraint error models for all three data types.
The results indicate that the system is
stable, does not experience a tendency towards ensemble collapse, and
provides smooth analyses that are closer to the data than an unconstrained
control run. Both the bias and time-dependent errors are
reduced by the assimilation but not entirely removed.
Time series of assimilation and ensemble statistics also indicate that
the model is not very strongly constrained by the data because of an
overspecification of the data errors.
A comparison with insitu current meter data shows
a deterioration in the equatorial zonal velocity profiles in the
central Pacific. This is primarily associated with a shift in the time-mean
profile to lower values resulting from an assimilation-induced bias.
O Leeuwenburgh. Validation of an EnKF system for OGCM initialisation assimilating temperature, salinity, and surface height measurements
published, Mon. Wea. Rev., 2007, 135