The Rapid Refresh - Replacement For The RUC Pre-implementation Development and Evaluation

Abstract

The Rapid Refresh (RR) mesoscale analysis and prediction system is scheduled for operational implementation at NCEP in early 2011. The RR will replace the Rapid Update Cycle (RUC) system, which is currently run hourly at NCEP to provide short-range â??situational awarenessâ? guidance for aviation, severe weather, and general forecast applications. The RR will occupy the same niche within the NCEP model suite, but incorporates several enhancements including: 1) an expanded domain covering all of North America including Alaska and the Caribbean, 2) coordinated use of specifically adapted versions of the Gridpoint Statistical Interpolation (GSI) analysis system and the Advanced Research WRF (ARW) prediction model, 3) inclusion of a satellite radiance assimilation package within the GSI and 4) use of a rotated cylindrical equidistant horizontal map projection to provide more uniform grid spacing across the domain. The unique data assimilation features of the RUC (digital filter-based radar reflectivity assimilation and cloud analysis using METAR and satellite data) have been ported to the RR system. The RR cloud analysis will benefit from use of a GOES-based special NASA Langley cloud product, which provides extended spatial coverage over both northern and southern portions of the RR domain. A new feature for the data assimilation system is the use of a partial cycling mechanism to prevent drift of the RR atmospheric state away from the parent GFS model atmosphere. The RR also retains the use of physical parameterizations used in the RUC: the RUC-Smirnova land surface model, the Thompson mixed-phase microphysics scheme with 2-moment treatment of rain and cloud ice, and the Grell-Devenyi ensemble cumulus scheme . Each of those parameterizations has been updated significantly over versions in the current RUC model, and all are available within the community WRF repository. The RR system has undergone extensive testing; first on the ESRL GSD linux-based supercomputer system and more recently on the NCEP IBM system. This testing has lead to a significant improvement in the interoperability of both the GSI and ARW systems and enhancements to the ARW for application as a cycled operational model. Upper-level and surface verification comparisons between the RR and RUC indicate RR superiority for nearly all variables and levels. RR precipitation forecasts are similar to the RUC. At the conference, we will provide a full statistical assessment of the RR, present case-study examples and discuss issues related to its implementation at NCEP (expected around the time of the conference).