TY - GEN
AU - Y. Trémolet
AB - Current operational implementations of 4D-Var rely on the assumption that the numerical model representing the evolution of the atmospheric flow is perfect, or at least that model errors are small enough to be neglected compared to other errors in the system. This paper describes a formulation of weak constraint 4D-Var that removes that assumption by explicitly representing model error as part of the 4D-Var control variable. The consequences of this choice of control variable on the implementation of incremental 4D-Var are discussed. It is shown that the background error covariance matrix cannot be used as an approximate model error covariance matrix. Another model error covariance matrix based on statistics of model tendencies is proposed. Experimental results are presented which show that this approach to accounting for and estimating model error does capture some known model errors and improves the fit to observations both in the analysis and in the background but it also captures part of the observation bias. We show that the model error estimated in this approach varies rapidly and cannot be applied to correct medium or long range forecasts. It is also shown that, because it relies on the tangent linear assumption for the entire assimilation window, the incremental formulation of weak constraint 4D-Var is not the best suited for long assimilation windows.
BT - ECMWF Technical Memoranda
DA - 03/2007
DO - 10.21957/1j5cznxtb
LA - eng
M1 - 520
N2 - Current operational implementations of 4D-Var rely on the assumption that the numerical model representing the evolution of the atmospheric flow is perfect, or at least that model errors are small enough to be neglected compared to other errors in the system. This paper describes a formulation of weak constraint 4D-Var that removes that assumption by explicitly representing model error as part of the 4D-Var control variable. The consequences of this choice of control variable on the implementation of incremental 4D-Var are discussed. It is shown that the background error covariance matrix cannot be used as an approximate model error covariance matrix. Another model error covariance matrix based on statistics of model tendencies is proposed. Experimental results are presented which show that this approach to accounting for and estimating model error does capture some known model errors and improves the fit to observations both in the analysis and in the background but it also captures part of the observation bias. We show that the model error estimated in this approach varies rapidly and cannot be applied to correct medium or long range forecasts. It is also shown that, because it relies on the tangent linear assumption for the entire assimilation window, the incremental formulation of weak constraint 4D-Var is not the best suited for long assimilation windows.
PB - ECMWF
PY - 2007
EP - 19
T2 - ECMWF Technical Memoranda
TI - Model error estimation in 4D-Var
UR - https://www.ecmwf.int/node/12819
ER -