systems were developed in the domains where TS observations were routinely
collected year-round for a long period of time. This allows for reliable estimation of
the background state, which is one of the key components of any DA system (e.g.
[32]). There are, however, many regions where the temporal and spatial distribution
of the observations is irregular and/or sparse in time and space. A prominent
example is the Bering/Chukchi Sea basin which becomes increasingly important
due to the rapid growth of the sea traffic and emerging prospects for development of
oil and mining industries on the shelves.
In this contribution, we discuss the key components of the regional Bering
Sea DA system developed by the authors in recent years [21, 22, 25] and present
results of the analysis of the Bering Sea circulation on different scales.
The Regional 4dVar Data Assimilation System
The dynamic components, which synthesize diverse oceanographic data within a
single framework are the Semi-Implicit Ocean Model (SIOM) and its adjoint
configured to optimize the SIOM trajectory with respect to observations.
The SIOM is a modification of the C-grid, z-coordinate Ocean Global Circulation
Model (OGCM) developed at the Laboratoire d’Oceanographie Dynamique et de
Climatologie [12]. The model was specifically designed for operating within the
regional 4dVar framework controlled by fluxes at the open model boundaries and
sea surface. The model is semi-implicit both in barotropic and baroclinic modes,
permitting simulations with relatively large time steps of approximately 0.1 day
[17, 20–22]. The tangent linear model was obtained by direct differentiation of the
forward model code. The adjoint code of the model was built analytically by
transposition of the operator of the tangent linear model, linearized in the vicinity
of the given solution of the forward model [32]. The spatial structure of the
background error covariances is modeled by the quadratic polynomials of the
Laplacian operator [34, 35].
The SIOM 4dVar DA system supports assimilation of diverse oceanographic
data such as: (a) In situ, Temperature and salinity observations from the database of
the Far Eastern Branch of the Russian Academy of Science; (b) Historical velocity
observations from moorings and sea surface drifters, and satellite-tracked drifter
trajectories (Fig. 1b); (c) Estimates of the mean transport through the Bering Strait.
Mean climatological estimates of 0.9 ± 0.2 Sv and corresponding seasonal transport are taken from [31]; (d) Atmospheric forcing data (momentum, heat and salt
fluxes) for assimilation are taken from the National Center for Environmental
Prediction (NCEP); (e) The SSH anomalies from the AVISO database at http://
www.aviso.altimetry.fr. All observations are supplied with corresponding spatially
varying covariances.
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G. G. Panteleev et al.
collected year-round for a long period of time. This allows for reliable estimation of
the background state, which is one of the key components of any DA system (e.g.
[32]). There are, however, many regions where the temporal and spatial distribution
of the observations is irregular and/or sparse in time and space. A prominent
example is the Bering/Chukchi Sea basin which becomes increasingly important
due to the rapid growth of the sea traffic and emerging prospects for development of
oil and mining industries on the shelves.
In this contribution, we discuss the key components of the regional Bering
Sea DA system developed by the authors in recent years [21, 22, 25] and present
results of the analysis of the Bering Sea circulation on different scales.
The Regional 4dVar Data Assimilation System
The dynamic components, which synthesize diverse oceanographic data within a
single framework are the Semi-Implicit Ocean Model (SIOM) and its adjoint
configured to optimize the SIOM trajectory with respect to observations.
The SIOM is a modification of the C-grid, z-coordinate Ocean Global Circulation
Model (OGCM) developed at the Laboratoire d’Oceanographie Dynamique et de
Climatologie [12]. The model was specifically designed for operating within the
regional 4dVar framework controlled by fluxes at the open model boundaries and
sea surface. The model is semi-implicit both in barotropic and baroclinic modes,
permitting simulations with relatively large time steps of approximately 0.1 day
[17, 20–22]. The tangent linear model was obtained by direct differentiation of the
forward model code. The adjoint code of the model was built analytically by
transposition of the operator of the tangent linear model, linearized in the vicinity
of the given solution of the forward model [32]. The spatial structure of the
background error covariances is modeled by the quadratic polynomials of the
Laplacian operator [34, 35].
The SIOM 4dVar DA system supports assimilation of diverse oceanographic
data such as: (a) In situ, Temperature and salinity observations from the database of
the Far Eastern Branch of the Russian Academy of Science; (b) Historical velocity
observations from moorings and sea surface drifters, and satellite-tracked drifter
trajectories (Fig. 1b); (c) Estimates of the mean transport through the Bering Strait.
Mean climatological estimates of 0.9 ± 0.2 Sv and corresponding seasonal transport are taken from [31]; (d) Atmospheric forcing data (momentum, heat and salt
fluxes) for assimilation are taken from the National Center for Environmental
Prediction (NCEP); (e) The SSH anomalies from the AVISO database at http://
www.aviso.altimetry.fr. All observations are supplied with corresponding spatially
varying covariances.
502
G. G. Panteleev et al.