  We consider the large-sparse symmetric linear systems of equations that arise
in the solution of weak constraint four-dimensional variational data
assimilation, a method of high interest for numerical weather prediction. These
systems can be written as saddle point systems with a 3x3 block structure but
block eliminations can be performed to reduce them to saddle point systems with
a 2x2 block structure, or further to symmetric positive definite systems. In
this paper, we analyse how sensitive the spectra of these matrices are to the
number of observations of the underlying dynamical system. We also obtain
bounds on the eigenvalues of the matrices. Numerical experiments are used to
confirm the theoretical analysis and bounds.
