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) and 3-km High-Resolution Rapid Refresh (HRRR) regional forecast systems. This technique enabled the first assimilation of radar reflectivity data for operational NOAA forecast models, critical especially for more accurate short-range prediction of convective storms. For the RAP, the technique uses a diabatic digital filter initialization (DFI) procedure originally deployed to control initial inertial-gravity wave noise. Within the forward-model integration portion of diabatic DFI, temperature tendencies obtained from the model cloud/precipitation processes are replaced by specified latent-heating-based temperature tendencies derived from the three-dimensional radar reflectivity data, where available. To further refine initial conditions for the convection-allowing HRRR model, a similar procedure is used in the HRRR, but without DFI. Both of these procedures, together called the ‘Radar-LHI’ (latent-heating initialization) technique, have been essential for initialization of ongoing precipitation systems, especially convective systems, within all NOAA operational versions of the 13-km RAP and 3-km HRRR models extending through the latest implementation upgrade at NCEP in 2020. Application of the latent-heat-derived temperature tendency induces a vertical circulation with low-level convergence and upper-level divergence in precipitation systems. Retrospective tests of the Radar-LHI technique show significant improvement in short-range (0-6 hour) precipitation system forecasts, as revealed by reflectivity verification scores. Results presented document the impact on HRRR reflectivity forecasts of the radar reflectivity initialization technique applied to the RAP alone, HRRR alone, and both the RAP and HRRR.

The impact of Indian radar and lightning data assimilation on the short‐range forecasts of heavy rainfall events

“…The estimated 3D cloud information from these datasets is used to update the cloud and hydrometer fields in the cloud‐covered regions (Dowell et al ., 2022; Hu et al ., 2006; Kain et al ., 2010; Ramu et al ., 2016; Sun et al ., 2014; Weygandt et al ., 2022; Xue et al ., 2003; Yang et al ., 2015). In addition, model‐derived 3D latent‐heat temperature tendencies are replaced with the estimated temperature tendencies calculated from radar and lightning proxy reflectivity.…”

Inland lake temperature initialization via coupled cycling with atmospheric data assimilation

“…First, cloud assimilation (from satellite and ceilometer data) is performed to ensure accurate shortwave and longwave radiation fields (Benjamin et al, 2021). Second, radar reflectivity data are assimilated as part of a 3 km ensemble data assimilation system to ensure accurate short-range precipitation (Weygandt et al, 2022;D22;J22;Benjamin et al, 2016). Finally, 2 m air temperature and moisture and 10 m wind observations are effectively assimilated (i.e., producing more accurate predictions), including representation through the boundary layer using pseudoinnovations (James and Benjamin, 2017, meaning estimated observation-background forecast differences but not actual).…”

Recent advancements for tropical cyclone data assimilation

“…Zhang et al 98 Recent studies show that the assimilation of ground-based radar radial velocity in combination with the reflectivity data is complementary and can further improve the model analyses and forecasts than assimilating one data type alone, 101,102 although there are various approaches to assimilate radar reflectivity data in either variational [101][102][103] or ensemble framework, 104 or through latent heat nudging using a digital filter. 105 In a case study of Hurricane Isabel (2003) using the 3DVar method, Zhao and Jin 102 demonstrated that the assimilation of radar reflectivity improved TC rainbands intensity and coverage, while radar radial velocity assimilation improved TC intensity and dynamic structure. The combined assimilation of radar radial velocity and reflectivity led to the best forecasts, particularly improved 24-h precipitation forecasts along the path of the inner core during landfall.…”