LAUSR

AbstractSpurious long-distance correlations in estimates of the background error covariance can deteriorate the performance of ensemble-based data assimilation methods. In this study, a localization method, called Monte Carlo (MC) localization, is presented to remove these correlations. It is particularly useful for use in high-dimensional ensemble–variational data assimilation systems. In this method, raw ensemble members are truncated by multiplying them with functions having compact support. This creates a larger ensemble, in which points spaced farther apart than the size of the compact support have zero correlation. The localized background error covariance is then estimated as the sample covariance of this larger ensemble. It is hypothesized that this localized background error covariance can be approximated by the MC approximation method using a limited set of the truncated ensemble members. This hypothesis is tested here on a grid with 1001 grid points and assuming a Gaussian true background error...