Error Estimates for an Ocean General Circulation Model from Altimeter and Acoustic Tomography Data

Abstract

Abstract An offline approach is proposed for the estimation of model and data error covariance matrices whereby covariance matrices of model data residuals are “matched” to their theoretical expectations using familiar least-squares methods. This covariance matching approach is both a powerful diagnostic tool for addressing theoretical questions and an efficient estimator for real data assimilation studies. Provided that model and data errors are independent, that error propagation is approximately linear, and that an observability condition is met, it is in theory possible to fully resolve covariance matrices for both model and data errors. In practice, however, due to large uncertainties in sample estimates of covariance matrices, the number of statistically significant parameters that can be estimated is two to three orders of magnitude smaller than the total number of independent observations. The covariance matching approach is applied in the North Pacific (5°–60°N, 132°–252°E) to TOPEX/Poseidon sea ...