TY - JOUR
T1 - A posteriori error covariances in variational data assimilation
AU - Shutyaev, V.P.
AU - Le Dimet, F.X.
AU - Gejadze, I.Yu.
AU - Russian Foundation for Basic Research (Funder)
AU - MOISE Project (Funder)
AU - Scottish Funding Council via GRPE (Funder)
PY - 2009/7
Y1 - 2009/7
N2 - The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find some unknown parameters of the model. The equation for the error of the optimal solution is derived through the statistical errors of the input data (background, observation, and model errors). A numerical algorithm is developed to construct an a posteriori covariance operator of the analysis error using the Hessian of an auxiliary control problem based on tangent linear model constraints.
AB - The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find some unknown parameters of the model. The equation for the error of the optimal solution is derived through the statistical errors of the input data (background, observation, and model errors). A numerical algorithm is developed to construct an a posteriori covariance operator of the analysis error using the Hessian of an auxiliary control problem based on tangent linear model constraints.
KW - variational data assimilation
KW - optimal solution error
KW - model error
KW - a posteriori covariance
U2 - 10.1515/RJNAMM.2009.011
DO - 10.1515/RJNAMM.2009.011
M3 - Article
SN - 0927-6467
VL - 24
SP - 161
EP - 169
JO - Russian Journal of Numerical Analysis and Mathematical Modelling
JF - Russian Journal of Numerical Analysis and Mathematical Modelling
IS - 2
ER -