This presentation will discuss the operational RAP and HRRR changes contained in this upgrade. The RAP domain is being expanded to encompass the NAM domain and the forecast lengths of both the RAP and HRRR are being extended. RAP and HRRR assimilation enhancements have focused on (1) extending surface data assimilation to include mesonet observations and improved use of all surface observations through better background estimates of 2-m temperature and dewpoint including projection of 2-m temperature observations through the model boundary layer and (2) extending the use of radar observations to include both radial velocity and 3-D retrieval of rain hydrometeors from observed radar reflectivities in the warm-season. The RAP hybrid EnKF 3D-variational data assimilation will increase weighting of GFS ensemble-based background error covariance estimation and introduce this hybrid data assimilation configuration in the HRRR.
Enhancement of RAP and HRRR model physics include improved land surface and boundary layer prediction using the updated Mellor-Yamada-Nakanishi-Niino (MYNN) parameterization scheme, Grell-Freitas-Olson (GFO) shallow and deep convective parameterization, aerosol-aware Thompson microphysics and upgraded Rapid Update Cycle (RUC) land-surface model. The presentation will highlight improvements in the RAP and HRRR model physics to reduce certain systematic forecast biases including a warm and dry daytime bias over the central and eastern CONUS during the warm season along with improved convective forecasts in more weakly-forced diurnally-driven events. Examples of RAP and HRRR forecast improvements will be demonstrated through both retrospective and real-time statistics and case-study examples.