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Nearshore Assimilation Algorithm Development and Demonstration
Understanding the flow in nearshore environments is important for commercial, ecological, and military purposes. Knowledge of nearshore oceanographic properties often is obtained from global or basin-scale operational forecast models. But due to resolution limitations, these models do not always provide an accurate picture of the environment, especially around coastal features such as bays, estuary inlets or tidal ponds.
With funding from the Office of Naval Research, SRI researchers have developed a modeling approach that combines data assimilation algorithms with hydrodynamic forecast models for more accurate estimates and modeling. The approach nests a high-resolution ocean model in fields obtained from existing global or basin-scale operational forecast models. Since the accuracy obtained using this strategy can be limited by coarse spatial and temporal resolution of the operational forecast models, our researchers have developed variational assimilation algorithms for nearshore and riverine environments.
The algorithms use local data to adjust hydrodynamic model boundary conditions to provide more precise high-resolution forecasts of nearshore oceanographic properties for coastal operations.
Research and development activities described were supported by Office of Naval Research, contracts N0014-10-C-0507 and N00014-12-C-0237.