19B.2 Expanding Use of Radar Data in Deterministic and Ensemble Data Assimilation for the High-Resolution Rapid Refresh (HRRR)

Wednesday, 30 August 2017: 10:45 AM
St. Gallen 1&2 (Swissotel Chicago)
Curtis Alexander, NOAA/ESRL/GSD, Boulder, CO; and D. Dowell, M. Hu, T. Ladwig, S. Weygandt, and S. G. Benjamin

The 13-km Rapid Refresh (RAP) and 3-km convective-allowing High-Resolution Rapid Refresh (HRRR) are hourly updating weather forecast models that use a specially configured version of the Advanced Research WRF (ARW) model and assimilate many novel and most conventional observation types on an hourly basis using Gridpoint Statistical Interpolation (GSI). Included in this assimilation is a procedure for initializing ongoing precipitation systems from observed radar reflectivity data, a cloud analysis to initialize stable layer clouds from METAR and satellite observations, and special techniques to enhance retention of surface observation information. The RAP is cycled hourly with forecasts to twenty-one hours covering much of North America and the HRRR is run hourly out to eighteen forecast hours over a domain covering the entire conterminous United States using boundary conditions from the hourly-cycled RAP. Experimental RAP and HRRR model development throughout 2014 and early 2015 culminated in a set of data assimilation and model enhancements that were incorporated into the first simultaneous upgrade of both the operational RAP and HRRR (to versions three and two respectively) in mid-2016. In this presentation, we will focus on two aspects of radar data assimilation development including (1) changes to the deterministic RAP and HRRR radar data assimilation that will be contained in the next upgrade to the operational RAP and HRRR in early 2018 and (2) establishment of a real-time experimental HRRR storm-scale ensemble Kalman filter data assimilation and forecast system (HRRR-E) including the use of radar reflectivity observations. Discussion of changes to the deterministic RAP and HRRR will include modification of the model microphysics latent heating specification derived from radar reflectivity observations in the RAP to reduce RAP and HRRR model forecast bias of reflectivity and precipitation in the first few forecast hours. We will also discuss inclusion of lightning observations in the HRRR as an additional proxy for observed radar reflectivities during latent heating specification along with assimilation of radar radial velocities. In addition to the deterministic development, we will provide an overview of the new experimental real-time storm-scale (3 km) ensemble data assimilation and forecast system that uses a HRRR-like configuration for model physics while employing hourly-cycled ensemble Kalman filter data assimilation (using 36 members) of radar reflectivities and other conventional observations. Forecasts from HRRR ensemble members will be compared with the deterministic HRRR using real-time radar reflectivity verification statistics and case studies to highlight the benefits of the storm-scale ensemble data assimilation, especially within the first six forecast hours.
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