Data assimilation is a method to combine the numerical solutions calculated by circulation models with observations from different platforms in order to obtain an optimal estimate of the state of the system. The computational and numerical difficulties associated with processing the increasing number of observations necessitate the use of thinning techniques to reduce the number of data assimilated. In this thesis the impact of thinning two types of particularly voluminous data sets on the overall performance of an ocean data assimilation system were evaluated. In particular, an analysis of the ocean circulation along the U.S. West Coast with a 10 km resolution grid was performed, spanning a period of one year. Two different thinning methods were tested: an intelligent data thinning (IDT) algorithm to thin gridded satellite sea surface temperature (SST) and a simple thinning algorithm to reduce the volume of radial sea surface velocity measured by a network of coastal high frequency radars. The SST data were thinned by discarding data in regions with low spatial variability while retaining data in regions of high spatial variability. Conversely, the radar observations were averaged to create “super observations” consistent with the resolution of the model grid and prior assumptions about observation errors. The full and thinned data sets were assimilated using a 4-dimensional variational (4D-Var) data assimilation algorithm in the Regional Ocean Modeling System (ROMS). A statistical analysis of the diagnosed background and observation errors showed that the thinning experiments were well-behaved. Furthermore, the innovation and residual vectors (i.e. the difference between each observation and its prior and posterior model counterpart) in all cases generally satisfied the assumption of Gaussian distributions. Additionally, the topology of a the total error covariance matrix of the data assimilation system was explored via its eigen space. The thinning experiments amplified the eigen spectrum, modified the condition number, and in particular thinning SST changed the aspect ratio of the hyperellipse defined by the covariance matrix to change. Lastly the impact of each type of observation on the analyses was quantified for the different thinning methods, suggesting that the radial velocities thinning was perhaps too severe, while the thinning of SST levelled the impact different observations had on the DA analysis. Overall results showed that the thinning did not significantly degrade the analysis, hence the next step will be to test these algorithms in a near-real time forecasting system
