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The long-term goal of this project is to develop and apply practical methods for data assimilation to improve the short-range prediction of mesoscale ocean variability. The primary objective of this work is to develop an ocean data assimilation system that exploits the strengths of both the ensemble-based (e.g., Evensen 2003; Houtekamer and Mitchell 1998; Tippett et al. 2003) and variational (e.g., Bennett 2002) approaches to data assimilation. The first step in this project is to perform a comprehensive inter-comparison of an ensemble-based data assimilation system with a 4d-var system for a suite of coastal model configurations. The second step is to identify the strengths and weaknesses of each system and to improve both systems by borrowing components from the other system. Ultimately, a single ensemble-var system will be developed. We will investigate the extent to which the ensemble-var system can outperform both the ensemble-based and variational approaches, both in terms of forecast skill (accuracy) and computational efficiency (throughput).

The long-term goals of this research project are to advance data assimilation (DA) methods for coastal ocean circulation models. After several years of research focused on various aspects of advanced, variational DA with nonlinear coastal ocean models, we have come close to development of an optimal, versatile, and relocatable DA system based on a primitive equation model with a turbulence submodel. The system can be used efficiently both for operational needs (forecasting, search and rescue, environmental response) and for fundamental studies of coastal ocean dynamics. The modeling system will have the capability to assimilate time-series measurements from moorings and coastal based high frequency (HF) standard and long-range radars, satellite information (SSH, SST), and hydrographic survey data (including observations from underwater gliders).

The long-term goals of this research project are to advance data assimilation (DA) methods for coastal ocean circulation models. After several years of research focused on various aspects of advanced, variational DA with nonlinear coastal ocean models, we are coming close to development of an optimal, versatile, and relocatable DA system based on a primitive equation model with a turbulence submodel. The system can be used efficiently both for operational needs (forecasting, search and rescue, environmental response) and for fundamental studies of coastal ocean dynamics. The modeling system will have the capability to assimilate time-series measurements from moorings and coastally-based high frequency (HF) standard- and long-range radars, satellite information (SSH, SST), and hydrographic survey data (including observations from underwater gliders).

The goal of this research project is to develop data assimilation (DA) methods for coastal circulation models and to apply these methods to measurements from the Oregon shelf. We envision the development of an optimal, versatile, and relocatable DA system based on a primitive equation model with a turbulence submodel. The planned system could be used efficiently both for operational needs (forecasting, search and rescue, environmental response) and for fundamental studies of coastal ocean dynamics., The original document contains color images.

The immediate scientific objectives of this research project are to develop practical, but still nearly optimal methods for the assimilation of data into coastal circulation models, and to apply these methods to time-series measurements from moorings and coastally-based high frequency (HF) standard and long range radars, satellite data (SSH, SST), and hydrographic survey data (e.g., from autonomous underwater vehicles, AUVs). An important additional scientific objective is to utilize data assimilation to study the physics of coastal ocean circulation processes.