Author(s)

M. van Loon A. W. Heemink, P.J.H. Builtjes

Abstract

The paper presents first results on data assimilation with a highly nonlinear test model using the (extended) Kalman filter algorithm. The test model has been designed such that the essential characteristics of the LOTOS model, including stiff chemistry, have been retained. Application of the standard algorithm for Kalman filtering is infeasible because of the huge computational and storage re- quirements. Instead, a reduced rank approximation of the covariance matrix is used, which reduces the computational burden to an acceptable amount of CPU time. Attention is paid to different ways to

Keywords