Diagnosis of observation, background and analysis‐error statistics in observation space

TLDR

Most operational assimilation schemes rely on linear estimation theory. The authors derive cost‑free consistency diagnostics for observation, background, and estimation error covariances in observation space, apply them to French 4D‑Var analyses, and test procedures for refining error variances and diagnosing cross‑correlations in a toy framework. The diagnostics are nearly cost‑free and, through a spectral interpretation, reveal how scale separation between background and observation errors influences the covariances. © 2005 Royal Meteorological Society.

Abstract

Abstract Most operational assimilation schemes rely on linear estimation theory. Under this assumption, it is shown how simple consistency diagnostics can be obtained for the covariances of observation, background and estimation errors in observation space. Those diagnostics are shown to be nearly cost‐free since they only combine quantities available after the analysis, i.e. observed values and their background and analysis counterparts in observation space. A first application of such diagnostics is presented on analyses provided by the French 4D‐Var assimilation. A procedure to refine background and observation‐error variances is also proposed and tested in a simple toy analysis problem. The possibility to diagnose cross‐correlations between observation errors is also investigated in this same simple framework. A spectral interpretation of the diagnosed covariances is finally presented, which allows us to highlight the role of the scale separation between background and observation errors. Copyright © 2005 Royal Meteorological Society