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  • 1
    Online Resource
    Online Resource
    American Meteorological Society ; 1997
    In:  Journal of Atmospheric and Oceanic Technology Vol. 14, No. 6 ( 1997-12), p. 1392-1408
    In: Journal of Atmospheric and Oceanic Technology, American Meteorological Society, Vol. 14, No. 6 ( 1997-12), p. 1392-1408
    Type of Medium: Online Resource
    ISSN: 0739-0572 , 1520-0426
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 1997
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    detail.hit.zdb_id: 48441-6
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  • 2
    Online Resource
    Online Resource
    Journal of Marine Research/Yale ; 1999
    In:  Journal of Marine Research Vol. 57, No. 4 ( 1999-7-1), p. 641-669
    In: Journal of Marine Research, Journal of Marine Research/Yale, Vol. 57, No. 4 ( 1999-7-1), p. 641-669
    Type of Medium: Online Resource
    ISSN: 1543-9542 , 0022-2402
    Language: Unknown
    Publisher: Journal of Marine Research/Yale
    Publication Date: 1999
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    SSG: 12
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  • 3
    Online Resource
    Online Resource
    American Meteorological Society ; 1995
    In:  Journal of Physical Oceanography Vol. 25, No. 6 ( 1995-06), p. 1130-1152
    In: Journal of Physical Oceanography, American Meteorological Society, Vol. 25, No. 6 ( 1995-06), p. 1130-1152
    Type of Medium: Online Resource
    ISSN: 0022-3670 , 1520-0485
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 1995
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    detail.hit.zdb_id: 184162-2
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  • 4
    Online Resource
    Online Resource
    Wiley ; 1998
    In:  Fisheries Oceanography Vol. 7, No. 3-4 ( 1998-12), p. 300-304
    In: Fisheries Oceanography, Wiley, Vol. 7, No. 3-4 ( 1998-12), p. 300-304
    Abstract: The present paper summarizes the results of several simulations of the central Black Sea pelagic food web using three different 1‐D, physical–biochemical, water column process models. The most simplified, five‐compartment version is used to explore the robust biological features of the ecosystem and the role of upper‐layer physics on the evolution of the euphotic zone biological processes. The other models, introducing additional biological complexities, show how these biologically structured models become more capable of simulating intensified subsurface summer production, more dynamic plankton structure arising after the increasing role of gelatinous carnivores in the ecosystem during the 1980s, and fairly sophisticated nitrogen cycling in the water column.
    Type of Medium: Online Resource
    ISSN: 1054-6006 , 1365-2419
    Language: English
    Publisher: Wiley
    Publication Date: 1998
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    detail.hit.zdb_id: 2020300-7
    SSG: 21,3
    SSG: 12
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  • 5
    In: Dynamics of Atmospheres and Oceans, Elsevier BV, Vol. 29, No. 2-4 ( 1999-7), p. 365-395
    Type of Medium: Online Resource
    ISSN: 0377-0265
    Language: English
    Publisher: Elsevier BV
    Publication Date: 1999
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    SSG: 16,13
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  • 6
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 1996
    In:  Journal of Geophysical Research: Oceans Vol. 101, No. C12 ( 1996-12-15), p. 28473-28488
    In: Journal of Geophysical Research: Oceans, American Geophysical Union (AGU), Vol. 101, No. C12 ( 1996-12-15), p. 28473-28488
    Abstract: In a companion paper by Gunson and Malanotte‐Rizzoli [this issue], the problem of estimating boundary and initial conditions for a regional open‐ocean model from sparse data is addressed using the adjoint method. Here the estimation of error covariances for the estimated boundary and initial conditions and interior fields, in the presence of strongly nonlinear dynamics, is investigated. The evaluation of the full error covariance matrix for the estimated control variables from the inverse Hessian matrix is presented along with its dependence upon the degree of nonlinearity in the dynamics. Major new results here obtained are the availability of off‐diagonal covariances, the successful calculation of error covariances for all boundary and initial conditions, and the estimation of errors for interior fields. The role of the Hessian matrix is assessed in gauging the sensitivity of the estimated boundary and initial conditions to the data.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 1996
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    detail.hit.zdb_id: 3094268-8
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    detail.hit.zdb_id: 3094181-7
    detail.hit.zdb_id: 3094219-6
    detail.hit.zdb_id: 3094167-2
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    SSG: 16,13
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  • 7
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 1996
    In:  Journal of Geophysical Research: Oceans Vol. 101, No. C3 ( 1996-03-15), p. 6487-6500
    In: Journal of Geophysical Research: Oceans, American Geophysical Union (AGU), Vol. 101, No. C3 ( 1996-03-15), p. 6487-6500
    Abstract: Massachusetts and Cape Cod Bays form a semienclosed coastal basin that opens onto the much larger Gulf of Maine. Subtidal circulation in the bay is driven by local winds and remotely driven flows from the gulf. The local‐wind forced flow is estimated with a regional shallow water model driven by wind measurements. The model uses a gravity wave radiation condition along the open‐ocean boundary. Results compare reasonably well with observed currents near the coast. In some offshore regions, however, modeled flows are an order of magnitude less energetic than the data. Strong flows are observed even during periods of weak local wind forcing. Poor model‐data comparisons are attributable, at least in part, to open‐ocean boundary conditions that neglect the effects of remote forcing. Velocity measurements from within Massachusetts Bay are used to estimate the remotely forced component of the flow. The data are combined with shallow water dynamics in an inverse‐model formulation that follows the theory of Bennett and McIntosh [1982], who considered tides. We extend their analysis to consider the subtidal response to transient forcing. The inverse model adjusts the a priori open‐ocean boundary condition, thereby minimizing a combined measure of model‐data misfit and boundary condition adjustment. A “consistency criterion” determines the optimal trade‐off between the two. The criterion is based on a measure of plausibility for the inverse solution. The “consistent” inverse solution reproduces 56% of the average squared variation in the data. The local‐wind‐driven flow alone accounts for half of the model skill. The other half is attributable to remotely forced flows from the Gulf of Maine. The unexplained 44% comes from measurement errors and model errors that are not accounted for in the analysis.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 1996
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    detail.hit.zdb_id: 161666-3
    detail.hit.zdb_id: 161667-5
    detail.hit.zdb_id: 2969341-X
    detail.hit.zdb_id: 161665-1
    detail.hit.zdb_id: 3094268-8
    detail.hit.zdb_id: 710256-2
    detail.hit.zdb_id: 2016804-4
    detail.hit.zdb_id: 3094181-7
    detail.hit.zdb_id: 3094219-6
    detail.hit.zdb_id: 3094167-2
    detail.hit.zdb_id: 2220777-6
    detail.hit.zdb_id: 3094197-0
    SSG: 16,13
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  • 8
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 1996
    In:  Journal of Geophysical Research: Oceans Vol. 101, No. C7 ( 1996-07-15), p. 16585-16599
    In: Journal of Geophysical Research: Oceans, American Geophysical Union (AGU), Vol. 101, No. C7 ( 1996-07-15), p. 16585-16599
    Abstract: The annual cycle of the plankton dynamics in the central Black Sea is studied by a one‐dimensional vertically resolved physical‐biological upper ocean model, coupled with the Mellor‐Yamada level 2.5 turbulence closure scheme. The biological model involves interactions between the inorganic nitrogen (nitrate, ammonium), phytoplankton and herbivorous zooplankton biomasses, and detritus. Given a knowledge of physical forcing, the model simulates main observed seasonal and vertical characteristic features, in particular, formation of the cold intermediate water mass and yearly evolution of the upper layer stratification, the annual cycle of production with the fall and the spring blooms, and the subsurface phytoplankton maximum layer in summer, as well as realistic patterns of particulate organic carbon and nitrogen. The computed seasonal cycles of the chlorophyll and primary production distributions over the euphotic layer compare reasonably well with the data. Initiation of the spring bloom is shown to be critically dependent on the water column stability. It commences as soon as the convective mixing process weakens and before the seasonal stratification of surface waters begins to develop. It is followed by a weaker phytoplankton production at the time of establishment of the seasonal thermocline in April. While summer nutrient concentrations in the mixed layer are low enough to limit production, the layer between the thermocline and the base of the euphotic zone provides sufficient light and nutrient to support subsurface phytoplankton development. The autumn bloom takes place sometime between October and December depending on environmental conditions. In the case of weaker grazing pressure to control the growth rate, the autumn bloom shifts to December–January and emerges as the winter bloom, or, in some cases, is connected with the spring bloom to form one unified continuous bloom structure during the January–March period. These bloom structures are similar to the year‐to‐year variabilities present in the data.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 1996
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    detail.hit.zdb_id: 161667-5
    detail.hit.zdb_id: 2969341-X
    detail.hit.zdb_id: 161665-1
    detail.hit.zdb_id: 3094268-8
    detail.hit.zdb_id: 710256-2
    detail.hit.zdb_id: 2016804-4
    detail.hit.zdb_id: 3094181-7
    detail.hit.zdb_id: 3094219-6
    detail.hit.zdb_id: 3094167-2
    detail.hit.zdb_id: 2220777-6
    detail.hit.zdb_id: 3094197-0
    SSG: 16,13
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  • 9
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 1996
    In:  Journal of Geophysical Research: Oceans Vol. 101, No. C12 ( 1996-12-15), p. 28457-28472
    In: Journal of Geophysical Research: Oceans, American Geophysical Union (AGU), Vol. 101, No. C12 ( 1996-12-15), p. 28457-28472
    Abstract: The problem of estimating boundary and initial conditions for a regional open‐ocean model is addressed here. With the objective of mimicking the Synoptic Ocean Prediction (SYNOP) experiment in the Gulf Stream system, a meandering jet is modeled by the fully nonlinear barotropic vorticity equation. Simulated velocity observations are taken using current meters and acoustic tomography; twin experiments are then performed in which the adjoint method is used to reconstruct the flow field. The estimated flow is forced to resemble the true flow by minimizing a cost function with respect to some control variables. First, the vorticity initial conditions are used as control variables, and the boundary conditions are specified. The strong flow is found to induce strong dependence of the model/data misfit upon the specified boundary conditions. Second, the boundary values of stream function and vorticity are then included among the control variables. Various choices of a priori information about the control variables are employed, using various observational strategies. The major new result obtained is the successful estimation of the complete set of initial and boundary conditions, which is necessary to integrate the vorticity equation forward in time. From a time‐invariant first guess for the boundary conditions the assimilation is able to create temporal variations at the boundaries that make the interior flow match well the velocity observations.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 1996
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    detail.hit.zdb_id: 3094104-0
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    detail.hit.zdb_id: 2016810-X
    detail.hit.zdb_id: 2403298-0
    detail.hit.zdb_id: 2016800-7
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    detail.hit.zdb_id: 161667-5
    detail.hit.zdb_id: 2969341-X
    detail.hit.zdb_id: 161665-1
    detail.hit.zdb_id: 3094268-8
    detail.hit.zdb_id: 710256-2
    detail.hit.zdb_id: 2016804-4
    detail.hit.zdb_id: 3094181-7
    detail.hit.zdb_id: 3094219-6
    detail.hit.zdb_id: 3094167-2
    detail.hit.zdb_id: 2220777-6
    detail.hit.zdb_id: 3094197-0
    SSG: 16,13
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  • 10
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 1995
    In:  Journal of Geophysical Research: Oceans Vol. 100, No. C4 ( 1995-04-15), p. 6777-6793
    In: Journal of Geophysical Research: Oceans, American Geophysical Union (AGU), Vol. 100, No. C4 ( 1995-04-15), p. 6777-6793
    Abstract: A practical method of data assimilation for use with large, nonlinear, ocean general circulation models is explored. A Kaiman filter based on approximations of the state error covariance matrix is presented, employing a reduction of the effective model dimension, the error's asymptotic steady state limit, and a time‐invariant linearization of the dynamic model for the error integration. The approximations lead to dramatic computational savings in applying estimation theory to large complex systems. We examine the utility of the approximate filter in assimilating different measurement types using a twin experiment of an idealized Gulf Stream. A nonlinear primitive equation model of an unstable east‐west jet is studied with a state dimension exceeding 170,000 elements. Assimilation of various pseudomeasurements are examined, including velocity, density, and volume transport at localized arrays and realistic distributions of satellite altimetry and acoustic tomography observations. Results are compared in terms of their effects on the accuracies of the estimation. The approximate filter is shown to outperform an empirical nudging scheme used in a previous study. The examples demonstrate that useful approximate estimation errors can be computed in a practical manner for general circulation models.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 1995
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    detail.hit.zdb_id: 2969341-X
    detail.hit.zdb_id: 161665-1
    detail.hit.zdb_id: 3094268-8
    detail.hit.zdb_id: 710256-2
    detail.hit.zdb_id: 2016804-4
    detail.hit.zdb_id: 3094181-7
    detail.hit.zdb_id: 3094219-6
    detail.hit.zdb_id: 3094167-2
    detail.hit.zdb_id: 2220777-6
    detail.hit.zdb_id: 3094197-0
    SSG: 16,13
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