Error Covariance Sensitivity and Forecast Impact Estimation with Adjoint 4D-Var

The adjoint-data assimilation system (adjoint-DAS) approach is presented as an efficient tool to obtain sensitivity information and first-order forecast impact estimates of variations in the observation- and background-error covariance parameters in a four-dimensional variational (4D-Var) DAS. A synergistic link is established between various methodologies to analyze the DAS performance: observation sensitivity and impact assessment, error covariance sensitivity, and a posteriori consistency diagnosis. Numerical results and analysis are presented with the adjoint versions of the Naval Research Laboratory Atmospheric Variational Data Assimilation System - Accelerated Representer (NAVDAS-AR) and the Navy Operational Global Atmospheric Prediction System (NOGAPS). The adjoint approach is used to provide guidance on the forecast impact of weighting the radiance data in the DAS according to error covariance estimates derived from an a posteriori diagnosis. The results indicate that information extracted from both error covariance diagnosis and forecast sensitivity is necessary to design parameter tuning procedures that are effective in reducing the forecast errors. Diagnostic and a priori first-order forecast impact estimates of the observation error correlations will be also presented for the AIRS and IASI instruments.