  We present an efficient computational framework to quantify the impact of
individual observations in four dimensional variational data assimilation. The
proposed methodology uses first and second order adjoint sensitivity analysis,
together with matrix-free algorithms to obtain low-rank approximations of ob-
servation impact matrix. We illustrate the application of this methodology to
important applications such as data pruning and the identification of faulty
sensors for a two dimensional shallow water test system.
