Four-dimensional ensemble Kalman filtering

Abstract

ABSTRACT Ensemble Kalman filtering was developed as a way to assimilateobserved data to track the current state in a computational model.In this paper we show that the ensemble approach makes possible anadditional benefit: the timing of observations, whether they occurat the assimilation time or at some earlier or later time, can beeffectively accounted for at low computational expense. In the caseof linear dynamics, the technique is equivalent to instantaneouslyassimilating data as they are measured. The results of numericaltests of the technique on a simple model problem are shown.