Innovation-Weight Parametrization in Data Assimilation: Formulation & Analysis with NAVDAS-AR/NAVGEM**This work was supported by the Naval Research Laboratory Atmospheric Effects, Analysis, and Prediction BAA #75-11-01 under award N00173-13-1-G903. Support for the second author from the sponsor ONR-PR-0602435N is gratefully acknowledged.

: An innovation-weight parametrization is introduced as a practical approach to account for deficiencies in the representation of both background error and observation error covariance in a variational data assimilation system. The adjoint-based evaluation of the forecast error sensitivity provides a computationally efficient diagnosis to observation-space distributed parameters and guidance for tuning the analysis Kalman gain operator. Theoretical aspects are discussed and preliminary results are presented with the adjoint versions of the Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) and the Navy’s Global Environmental Model (NAVGEM).