Wednesday, 16 January 2002
Multivariate forecast error covariances for an ocean model estimated by Monte-Carlo simulation
One of the most difficult aspects of ocean state estimation
is the prescription of the model forecast error covariances.
The paucity of ocean observations limits our ability to
estimate the covariance structures from model-data differences.
In most practical applications, simple covariances are
usually prescribed. Rarely are cross-covariances between different
model variables used. Consistent with other groups that conduct seasonal
forecasts with coupled models,
the NASA Seasonal-to-Interannual (SI) Prediction Project (NSIPP) currently
assumes that the model forecast error has a Gaussian spatial structure,
and only the temperature field is analyzed in a univariate optimal
interpolation (OI). However, it has been found that the univariate OI has a
detrimental effect on the salinity and velocity fields of
the model for assimilation cycles longer than 2 months.
A multivariate OI algorithm, which allows
for salinity and current fields to be corrected as a result
of the temperature assimilation has been implemented.
The multivariate algorithm uses anisotropic, inhomogeneous model error
covariances obtained by a Monte Carlo simulation. An ensemble
of ocean states was generated by forcing the ocean model with an
ensemble of air-sea
fluxes reflecting the internal atmospheric variability. In all,
en ensemble of
160 members was constructed. This matrix is
naturally represented by a set of eofs, fewer in number than the
size, allowing for an efficient analysis of its properties.
The multivariate OI outperforms the univariate
assimilation. The robustness of the empirical forecast error
covariance estimate will be discussed. In particular, no significant
seasonal dependence was found in the dominant covariance structures.