HR: 13:50h
AN: A43B-02    [Abstracts]
TI: Seasonal Predictions over the Southeast U.S. using the FSU Regional Spectral Model
AU: * Cocke, S D
EM: scocke@mailer.fsu.edu
AF: Florida State University, Dept. Meteorology Rm. 410 Love Bldg, Tallahassee, Fl 32306 United States
AU: LaRow, T
EM: larow@coaps.fsu.edu
AF: Florida State University, Dept. Meteorology Rm. 410 Love Bldg, Tallahassee, Fl 32306 United States
AU: Shin, D
EM: shin@coaps.fsu.edu
AF: Florida State University, Dept. Meteorology Rm. 410 Love Bldg, Tallahassee, Fl 32306 United States
AB: We have developed a regional spectral model for weather and climate studies and prediction. The regional model is embedded in the FSU Global Spectral Coupled Model (FSUGCM), though in principle it can be coupled to other models or analyses, or nested within the regional model itself. The Florida State University Regional Spectral Model (FSUNRSM) utilizes the spectral method in the horizontal direction using Double Fourier trigonometric series. The regional model is a perturbation model, meaning that only deviations from the global, or base, solution are represented by the spectral functions. The regional model was designed to be compatible with the FSUGSCM. As a result, the regional model has available to it the same array of physical parameterizations, including six convection schemes, the FSU physics package and most of the NCAR CCM3.6 atmospheric physics package. The regional model also shares the same sigma-coori dinate vertical structure with Charney-Phillips staggering. The FSUNRSM is very similar in concept to the NCEP Regional Spectral Model, though with some significant differences in implementation. We will provide a brief overview of the model, including some recent enhancements, such as the inclusion of the Community Land Model version 2. We will also present some results for seasonal predictions of rainfall over the Southeast U.S. In these experiments, we ran 12 4-month integrations for the years 1986-1997 starting November 1 of each year. The seasonal precipitation anomalies (December-February) were reasonably well simulated by both the global and regional models, with the regional model performing somewhat better. More importantly, the regional model was better able to simulate the frequency of rainfall events than the global model, and in reasonable agreement with cooperative station data.
DE: 3337 Numerical modeling and data assimilation
DE: 3354 Precipitation (1854)
SC: Atmospheric Sciences [A]
MN: 2005 Joint Assembly