Implementation of a local gain-form ensemble transform Kalman filter in JEDI and validation of initial cycling tests with the UFS

The current operational NOAA global atmospheric data assimilation system uses the local gain-form ensemble transform Kalman filter (LGETKF), with model-space localization in the vertical and observation-error localization in the horizontal, to update the atmospheric ensemble used in the Global Data Assimilation System (GDAS). In this talk we will describe the implementation of a generic (model-agnostic) version of this algorithm in JEDI, and present initial results of cycling atmospheric ensemble data assimilation experiments with the Unified Forecast System (UFS) at low resolution comparing the JEDI implementation with the current operational GSI-based implementation. The current status of the JEDI implementation and the plans for further development will be summarized.