Assimilation of multiple linearly dependent data vectors
Peer reviewed, Journal article
Published version
Permanent lenke
https://hdl.handle.net/11250/2650140Utgivelsesdato
2019Metadata
Vis full innførselSamlinger
Sammendrag
Assimilation of a sequence of linearly dependent data vectors, {dl}Ll=1 such that dl=BldLL−1l=1 , is considered for a parameter estimation problem. Such a data sequence can occur, for example, in the context of multilevel data assimilation. Since some information is used several times when linearly dependent data vectors are assimilated, the associated data-error covariances must be modified. I develop a condition that the modified covariances must satisfy in order to sample correctly from the posterior probability density function of the uncertain parameter in the linear-Gaussian case. It is shown that this condition is a generalization of the well-known condition that must be satisfied when assimilating the same data vector multiple times. I also briefly discuss some qualitative and computational issues related to practical use of the developed condition.