An Automated High-Resolution, Rapidly Relocatable Meteorological Nowcasting and Prediction System

Abstract

An automated, rapidly relocatable nowcasting and prediction system, whose cornerstone is the full-physics, nested-grid, nonhydrostatic fifth-generation Pennsylvania State University?National Center for Atmospheric Research (PSU?NCAR) Mesoscale Model (MM5), has been under development at the Pennsylvania State University since the late 1990s. In the applications presented in this paper, the Rapidly Relocatable Nowcast-Prediction System (RRNPS) provides a continuous stream of highly detailed nowcasts, defined here as gridded meteorological fields produced by a high-resolution mesoscale model assimilating available observations and staying just ahead of the clock to provide immediately available current meteorological conditions. The RRNPS, configured to use 36-, 12-, and 4-km nested domains, is applied over the Great Plains for 18 case days in August 2001, over the East Coast region for 8 case days in April 2002, and for 12 case days during the winter and summer of 2003. The performance of the RRNPS is evaluated using subjective and statistical methods for runs with and without the use of continuous four-dimensional data assimilation (FDDA). A statistical evaluation of the dependence of RRNPS skill on the length of model integration yields further insight into the value added by FDDA in RRNPS nowcasts. It must be emphasized that unlike typical operational analysis systems, none of the current data are used in the nowcasts since the nowcasts are made available just ahead of the clock for immediate use. Because none of the verification data are assimilated into the RRNPS at the time of verification, this evaluation is a true test of the time-integrated effects of previous FDDA on current model solutions. Furthermore, the statistical evaluations also utilize independent data completely withheld from the system at all times. Evaluation of the RRNPS versus observations on the 4- and 12-km grids shows that there is little difference in statistical skill between the two resolutions for the two application regions. However, subjective case evaluations indicate that mesoscale detail is added to the wind and mass fields on the 4-km domain of the RRNPS as compared to the coarser 12-km domain. Statistics suggest that 4-km resolution provides slightly more accurate meteorology for the domain including complex terrain and coastlines. The statistics also show that the use of continuous FDDA in a high-resolution mesoscale model improves the accuracy of the RRNPS nowcasts, and that this unique nowcast prediction system provides immediately available forecast-analysis products that are comparable or superior to those produced at operational centers, especially for the surface and the boundary layer. Finally, the RRNPS is also designed to run locally and on demand in a highly automated mode on modest computing platforms (e.g., a dual-processor PC) with potentially very limited data resources and nonstandard data communications.