Di Lorenzo, E., A. M. Moore, H. G. Arango, B. D. Cornuelle, A. J. Miller,
B. Powell, B. S. Chua and A. F. Bennett, 2007:
Weak and strong constraint data assimilation in the inverse Regional
Ocean Modeling System (ROMS): Development and application for a
baroclinic coastal upwelling system
Ocean Modelling, 16, 160-187.
We describe the development and preliminary application of the inverse Regional Ocean
Modeling System (ROMS), a four dimensional variational (4DVAR) data assimilation system for
high-resolution basin-wide and coastal oceanic flows. Inverse ROMS makes use of the recently
developed perturbation tangent linear (TL), representer tangent linear (RP) and adjoint (AD)
models to implement an indirect representer-based generalized inverse modeling system. This
modeling framework is modular. The TL, RP and AD models are used as stand-alone sub-models
within the Inverse Ocean Modeling (IOM) system described in Chua and Bennett (2001). The
system allows the assimilation of a wide range of observation types and uses an iterative
algorithm to solve nonlinear assimilation problems. The assimilation is performed either under
the perfect model assumption (strong constraint) or by also allowing for errors in the model
dynamics (weak constraints). For the weak constraint case the TL and RP models are modified
to include additional forcing terms on the right hand side of the model equations. These terms
are needed to account for errors in the model dynamics.
Inverse ROMS is tested in a realistic 3D baroclinic upwelling system with complex bottom
topography, characterized by strong mesoscale eddy variability. We assimilate synthetic data for
upper ocean (0-450m) temperatures and currents over a period of 10 days using both a high
resolution and a spatially and temporally aliased sampling array. During the assimilation period
the flow field undergoes substantial changes from the initial state. This allows the inverse
solution to extract the dynamically active information from the synthetic observations and
improve the trajectory of the model state beyond the assimilation window. Both the strong and
weak constraint assimilation experiments show forecast skill greater than persistence and
climatology during the 10-20 days after the last observation is assimilated.
Further investigation in the functional form of the background error covariance and in the
use of the representer tangent linear model may lead to improvement in the forecast skill.