Under the support of NOAA JTTI, Warn-on-Forecast and GOES-R program fundings, CAPS has been developing capabilities for directly assimilating radar reflectivity, radial velocity, and GOES-R Geostationary Lightning Mapper (GLM) observations directly into the GSI EnKF and hybrid EnVar systems, for HRRR-like CAM forecasting systems including the future FV3-based Rapid Refresh Forecast System (RRFS). Special techniques and treatments have to be devised and implemented in GSI hybrid EnVar to be able to effectively assimilate radar reflectivity data directly within the variational framework due to the high nonlinearity of reflectivity observation operator. The operator also needs to be consistent with the preferred multi-moment microphysics scheme used. For GLM lightning flesh extent density (FED) data, tuned observation operators based on graupel mass and graupel volume are implemented within GSI and experiments show that the assimilation of FED data can achieve similar level of impacts as assimilating radar data. To effectively assimilate observations sampling synoptic (e.g. rawinsonde) through convective (e.g., radar) scales on continental-scale CAM grids utilizing ensemble error covariances, a multi-scale algorithm is developed and tested with GSI EnKF. Assimilation and forecasting results with above schemes with individual cases in an extended period will be presented.
Most recently, the direct radar reflectivity assimilation capabilities in GSI have been tentatively implemented within the first public release of JEDI, and single-time 3DVar and En3DVar analyses of hydrometeors yield reasonable results, on a local FV3-LAM grid or a stretched global FV3 grid. The presentation will also briefly report on results of FV3-LAM forecasts with multiple physics configurations for the HMT realtime forecast experiments.
