Variational data assimilation and parameter estimation in an equatorial Pacific Ocean model

A variational data assimilation and parameter estimation method for a reduced gravity model is developed. The method is applied to the Equatorial Pacific Ocean. In the variational formalism a cost function measuring the "distance" between the model solution and the observations is minimized. The phase speed in the model is used as a control parameter and the optimal spatial structure giving the best fit of the model to the observations is determined. In the minimization algorithm a conjugate gradient descent direction is used. The method is computationally effective, and for the experiments considered convergence is achieved in ten iterations or less. Several experiments are performed using the model solutions as observations. It is shown that the assimilation algorithm is able to determine the large scale spatial structure of the phase speed, even if observations are available at only three stations. Real sea level observations from three stations are assimilated for two different periods. The year 1979 was chosen to represent a year without an El Nino, while 1982/83 was chosen to represent an El Nino year.