Radar Reflectivity–Based Model Initialization Using Specified Latent Heating (Radar-LHI) within a Diabatic Digital Filter or Pre-Forecast Integration

Abstract A technique for model initialization using three-dimensional radar reflectivity data has been developed and applied within the NOAA 13-km Rapid Refresh (RAP) 3-km High-Resolution (HRRR) regional forecast systems. This enabled first assimilation of operational models, critical especially more accurate short-range prediction convective storms. For RAP, uses a diabatic digital filter (DFI) procedure originally deployed to control initial inertial gravity wave noise. Within forward-model integration portion DFI, temperature tendencies obtained from cloud/precipitation processes are replaced by specified latent heating–based derived data, where available. To further refine conditions convection-allowing HRRR model, similar is used in HRRR, but without DFI. Both these procedures, together called “Radar-LHI” (latent heating initialization) technique, have essential ongoing precipitation systems, all versions RAP models extending through latest implementation upgrade at NCEP 2020. Application heat–derived tendency induces vertical circulation with low-level convergence upper-level divergence Retrospective tests Radar-LHI show significant improvement (0–6 h) system forecasts, as revealed verification scores. Results presented document impact on forecasts alone, both HRRR. Significance Statement The large uncertainty situations, even short lead times, coupled hazardous weather they produce, makes storm one most challenges confronting numerical community. Prediction heavy events also requires An innovative initialize described. which (and can accommodate lightning other convection/precipitation indicators), was implemented 2009 NOAA/NCEP continues be 2022 NCEP-operational making it backbone rapidly updated capability.