Localizing the impact of satellite radiance observations using a global group ensemble filter

Assimilation of satellite radiances has been proven to have positive impacts on the forecast skill, especially for regions with sparse conventional observations. Localization is an essential component to effectively assimilate satellite radiances in ensemble Kalman filters with affordable ensemble sizes. However, localizing the impact of radiance observations is not straightforward, since their location and separation from grid point model variables are not well defined. A global group filter (GGF) is applied here to provide a theoretical estimate of vertical localization functions for radiance observations being assimilated for global numerical weather prediction. As an extension of the hierarchical ensemble filter, the GGF uses groups of climatological ensembles to provide an estimated localization function that reduces the erroneous increments due to ensemble correlation sampling error. Results from an idealized simulation with known background error covariances show that the GGF localization function is superior to the optimal Gaspari and Cohn (GC) localization function. When the GGF is applied to the AMSU-A radiances, it can provide different localization functions for different channels, which indicates the complexity and large computational cost of tuning the localization scales for radiance observations. The GC, GGF, and fitted GGF (FGGF) localization functions are compared using experiments with the NCEP GFS and the NOAA operational EnKF. Verifications relative to the conventional observations, AMSU-A radiances, and the ECMWF analyses show that the GGF and FGGF have smaller errors than GC except in the tropics, and the advantages of the GGF and FGGF persist through 120 h forecast lead time.