Evaluation of 13-km and 3-km Radar Data Assimilation Impacts on High Resolution Rapid Refresh (HRRR) Thunderstorm Forecasts

High Resolution Rapid Refresh (HRRR) forecasts benefit from radar reflectivity assimilation on both the 3-km HRRR grid and on the 13-km parent Rapid Refresh grid. A companion presentation describes the new 3-km HRRR data assimilation procedure including the use of radar reflectivity data during a 1-h HRRR pre-forecast and documents the forecast improvement from this assimilation, especially for reducing short-range model spin-up.

In this presentation, a more detailed analysis of the respective contributions from both the 13-km and 3-km radar reflectivity assimilation for different types of convective storms will be presented, as well as an initial assessment of the impact of 3-km radial velocity assimilation.

Initial qualitative assessment of the relative impacts of 13-km vs. 3-km reflectivity assimilation is consistent with expectation that 13-km assimilation is more beneficial for larger, mesoscale convective systems and the positive benefit is retained longer in the forecast. Conversely, while the 3-km reflectivity assimilation during the HRRR pre-forecast hour greatly reduces the model spin-up and is able to initialize smaller convective storms, the positive impact from this assimilation diminishes more quickly than the impact from the 13-km reflectivity assimilation. In addition to documenting and quantifying these characteristics for real-time and retrospective test periods, the assessment will include detailed case study analysis to examine the radar assimilation impacts on small-scale storm structure.

This assessment of the reflectivity assimilation will be complemented by an initial assessment of the inclusion of radial velocity data assimilation in the 1-h HRRR pre-forecast. In particular, we will examine the sensitivity of the forecast skill to variation in the assimilation configuration, including assumed correlation length scale, data quality control, and use of clear-air data.