Title: Accounting for correlated satellite observation error in NAVGEM

Author: Elizabeth Satterfield (Naval Research Laboratory)
William F. Campbell (Naval Research Laboratory)
Nancy Baker (Naval Research Laboratory)

Typically, data assimilation schemes assume observation error covariance matrix (denoted R) is diagonal. Compensating for correlated observation errors (off diagonal terms) under this assumption is generally done by thinning (discarding) or averaging data, and/or inflation of the assigned observation error variance. This assumption is especially problematic for high resolution satellite data. Recently, several NWP centers have incorporated correlation terms in the observation error covariance matrix. Specifically, the Met Office, has been using vertically correlated observation error for IASI since January 2013 (Weston et al., 2014).

We will show initial results from the inclusion of vertical (interchannel) correlation terms in the observation error covariance matrix for the Advanced Technology Microwave Satellite (ATMS) and Infrared Atmospheric Sounding Interferometer (IASI) in the Navy's 4D-Var data assimilation system (NAVDAS-AR).The vertical observation error covariance matrix for the ATMS and IASI instruments was estimated using the Desroziers method (Desroziers et al., 2005) and an archive of historical satellite and NAVGEM model data. The results suggested lowering the error variance (diagonal of R) and introducing strong correlations, especially in the moisture-sensitive channels. Preliminary results were evaluated with NRL's observation sensitivity tool, and showed the positive impact of correctly accounting for vertically correlated observation error in both the ATMS and IASI instruments. Full cycling data assimilation experiments using standard forecast metrics are planned for later this year.

GMAO Head: Steven Pawson Global Modeling and Assimilation Office NASA Goddard Space Flight Center

Curator: Nikki Privé Last Updated: Feb 9 2015