We report the use of a 3-dimensional variational (3DVAR) data assimilation method as part of a numerical model off northern Chile. The numerical model is part of an ocean forecasting project that aims to understand the impact of environmental variability on the distribution of biological species in the area. We assimilated data from a simulated ocean observing system to recover a known state, obtaining a significantly smaller error when compared to a numerical run with no assimilation. Our

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... ts validate the computational implementation of the code, and allow us to evaluate the impact of the choice of data in the assimilation process: the assimilation of sea surface height being particularly important. We note that the assimilation of surface data propagates properly to greater depths and reduces the error with reference to the known state. This was possible by using covariance error matrices calculated previously for the California coastal area. The implementation of the data assimilation module is relatively simple and permits its use in operational forecasting systems, and for the design and evaluation of future ocean observational systems.