A singular evolutive interpolated Kalman filter for efficient data assimilation in a 3-D complex physical–biogeochemical model of the Cretan Sea

Abstract

A singular evolutive interpolated Kalman (SEIK) filter is used to assimilate pseudo-observations via twin simulation experiments in a complex three-dimensional coupled physical–biogeochemical model of the Cretan Sea. The simulation system comprises two on-line coupled sub-models: the three-dimensional Princeton Model and the European Regional Seas Ecosystem Model (ERSEM). In the SEIK filter, the estimation error is represented by an ensemble of state vectors, which are drawn randomly at every filtering step. In the twin experiments performed the predictions of the coupled model were corrected every 2 days using synthetic measurements extracted from a model reference run according to a network of 23 stations in the Cretan Sea. The filter is shown to be very efficient, with the assimilation results exhibiting a continuous decrease of the estimation error during the experimental period.