HR: 16:45h
AN: A14A-06    [Abstracts]
TI: Performance of the Florida State University Hurricane Superensemble During 2004
AU: * Mackey, B P
EM: mackey@met.fsu.edu
AF: Department of Meteorology, FSU, Love Building Florida State University, Tallahassee, FL 32306-4520 United States
AU: Biswas, M K
EM: biswas@earl.met.fsu.edu
AF: Department of Meteorology, FSU, Love Building Florida State University, Tallahassee, FL 32306-4520 United States
AU: Krishnamurti, T N
EM: tnk@io.met.fsu.edu
AF: Department of Meteorology, FSU, Love Building Florida State University, Tallahassee, FL 32306-4520 United States
AB: The performance of the FSU hurricane superensemble (SE) is evaluated for the active 2004 Atlantic-basin season. During this memorable season in which five hurricanes made landfall over the US -- including two major hurricanes striking Florida -- the FSU multi-model SE established itself as one of the very best forecasting tools in use at the National Hurricane Center (NHC). This statistical post-processing technique utilizes the NHC suite of real-time model predictions of hurricane track locations (lat/lon pairs) and intensity values. Unequal weights are then applied to each forecast model for each 12-hour forecast interval. These weights are pre-determined through a training phase, during which a statistical relation is obtained among past model forecasts and observed data using a multiple linear regression approach. This year, a new non-linear technique was implemented for the first three days of the track forecast. This had a positive impact in improving the SE performance. For the 2004 season as a whole, the track SE had comparable RMS errors to the GUNA consensus model through forecast hour 108, meanwhile outperforming all other model forecasts, including the ensemble mean. The strength of the track SE was evident in the longer range, where on average it showed a 5.5% improvement over the best model at 120 hours (122 cases). When examining the cases where the intensity of a cyclone was at least minimal hurricane strength, the SE demonstrated an 11.1% improvement over the best model at 120 hours (92 cases). Additionally, the average 72-hour SE intensity forecast exhibited a 17.4% improvement over the best intensity model (200 cases). Moreover, the superensemble performed very impressively with the long-track Cape Verde-type hurricanes. The landfall predictions of these hurricanes were quite accurate even well before 72 hours. For instance, the SE predicted well the landfall point of Frances over the Florida east coast about 84 hours in advance. With Ivan, the FSU SE did not forecast any landfall over peninsular Florida while the member models incorrectly did so. Accordingly, the SE held a clear advantage at the 120-hour lead-time with average errors for Ivan about 170 km less than the best model (35 cases). Also, the looping of Jeanne was well-predicted even when the member model tracks had a large spread. Upon examining the forecasts of a particular tropical cyclone over its entire life cycle, it is evident that the spread of the SE is much less than the other models. This indicates the greater forecast consistency and reliability of the FSU SE.
DE: 3210 Modeling
DE: 3230 Numerical solutions
DE: 3337 Numerical modeling and data assimilation
DE: 3374 Tropical meteorology
SC: Atmospheric Sciences [A]
MN: 2005 Joint Assembly