Unique data assimilation features from the RUC, including the digital filter initialization (DFI)-based radar reflectivity assimilation and cloud analysis procedures, have been ported to the RR system (with updates and enhancements). The RR cloud analysis benefits 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. Another new feature for the data assimilation system is the use of a partial cycling mechanism which improves upper-level verification. Also, a significant increase in the number and coverage of aircraft observations that feed the RR occurred in spring of 2011. Extensive pre-implementation testing and improvement of the RR have yielded an NCEP implementation configuration that improves significantly upon the RUC forecast skill, especially in upper-air, cloud, and precipitation verification.
In this paper, we will provide a description and a detailed evaluation of the NCEP implementation version of the RR. The evaluation will include an analysis of RR forecast skill (upper-air verification, surface verification, precipitation and cloud verification). Composite statistical results will be complemented by case study examples. Particular emphasis will be given to documenting RR characteristics and skill for specific aviation hazards, including icing, low ceiling, and turbulence. We will conclude by discussing ongoing / planned work to provide further enhancements for a 2nd generation version of the Rapid Refresh, RR-2 (operational implementation expected at NCEP in late 2012).
This research is partially in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA.