Variational data assimilation in a reduced gravity model of the northwestern Indian Ocean: experiment with Kelvin waves

Abstract

Variational method of assimilating data in a reduced gravity model of the northwestern Indian Ocean is discussed. A cost function representing the misfit between the model and the data is minimised with the model equations acting as constraints. The initial conditions of the model are used as control parameters and the best fit initial conditions are determined as a result of minimisation carried out using a variable-storage quasi-Newton method. Experiment has been done with simulated data generated from a control sun of the model describing propagation of Kelvin waves. Assimilation of upper layer thickness data alone has been found to be sufficient for a good recovery of the initial state. The method has been found to have good predictive potential and is particularly suitable for assimilating data from satellite altimeters.