Chris G. Walter
Bruce M. Howe
James A. Mercer
Appl. Phys. Lab., Univ. of Washington, Seattle, WA 98105
Robert N. Miller
Oregon State Univ.
Observing and predicting the ocean's meso-scale circulation presents a formidable task for oceanographers. Ocean acoustic tomography uses travel times between acoustic sources and receivers to measure the ocean's low wave-number sound speed (temperature) and current fields. Experiments are being performed with simulated ocean data to determine the relative effectiveness of tomographic and point measurements in constraining a numerical circulation model. The primary tool is data assimilation, i.e., an optimized combination of model output and observations. Relative weights of observation and model output are based on a priori estimates of the model and observation error covariance. Results from identical twin simulations using tomographic and point measurements are presented. In the twin experiment, two assimilation schemes are examined, one in which the estimated model error covariance is fixed (optimal interpolation) and another in which a Monte Carlo [G. Evensen, J. Geophys. Res. 99, 10 143--10 162 (1994)] method is used to estimate the model error covariance dynamically. Error statistics of the energy and flux fields are used to quantify the performance for each observation and updating scheme. The number of measurements required to achieve a target error level is estimated for each method. [Work supported by ONR and by the Strategic Environmental Research and Development Program through ARPA.]