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Filtering by: Funding statement This work was partially funded by the National Science Foundation REU Site program under Grant No. NSF OCE-0648515. Remove constraint Funding statement: This work was partially funded by the National Science Foundation REU Site program under Grant No. NSF OCE-0648515. Committee Member Kosro, P. Michael Remove constraint Committee Member: Kosro, P. Michael

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  • Optics, structure and variability of the offshore Columbia River plume

    Creator:
    Saldías, Gonzalo
    Abstract:
    The Columbia River delivers the greatest amount of freshwater to the coastal ocean along the U.S. Pacific coast. This freshwater forms the Columbia River plume, a mesoscale plume with significant implications on coastal ocean physical, biological, chemical, and geological processes. The plume is transported south and offshore during the upwelling...
    Resource Type:
    Dissertation
    Full Text:
    opportunities to teach and this was a great experience for me. The professors of all my classes in Physics of

  • Interactions of wind-driven and tidally-driven circulation in the Oregon coastal ocean

    Creator:
    Osborne, John J.
    Abstract:
    Influences of tidal and slower (subtidal) oceanic flows over the continental shelf and slope off Oregon are studied using a high-resolution ocean circulation model and comparative model-data analyses. The model is based on the Regional Ocean Modeling System (ROMS), a fully nonlinear, three-dimensional model (using hydrostatic and Boussinesq approximations). The...
    Resource Type:
    Dissertation
    Full Text:
    variability in the tidal frequency band may be partially driven by high-frequency atmospheric forcing, which

  • Ensemble-variational data assimilation for dynamically challenging coastal ocean circulation

    Creator:
    Pasmans, Ivo
    Abstract:
    Currently, forecasts produced by the Oregon-Washington (OR-WA) Coastal Ocean Forecast System are constrained by assimilation of only surface observations. The 4-dimensional variational (4DVAR) data assimilation (DA) algorithm is utilized to combine the model and the data, with the time-independent forecast ("background'') error covariance B. In this study, two possible improvements...
    Resource Type:
    Dissertation
    Full Text:
    - sessment Program (QOSAP), National Science Foundation (NSF) grants OCE-0527168 and OCE-0961999, Integrated