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Title page for ETD etd-07142008-143835


Type of Document Thesis
Author Brodowski, Christopher Michael
Author's Email Address cb05k@garnet.acns.fsu.edu
URN etd-07142008-143835
Title Forecast Skill of the NCEP GFS model in the 2006 Southern Hemisphere Stratosphere
Degree Master of Science
Department Meteorology, Department of
Advisory Committee
Advisor Name Title
Ming Cai Committee Chair
Jon Ahlquist Committee Member
Philip Cunningham Committee Member
Keywords
  • Forecast
  • GFS
  • Mode
  • Annular
  • Southern
  • Stratosphere
  • Anomaly
  • EOF
  • NCEP
  • Oscillation
  • Polar
  • Vortex
Date of Defense 2008-07-10
Availability unrestricted
Abstract
This study diagnoses the anomaly fields of the Southern Hemispheric stratosphere in the period from 1 January 2006 to 28 February 2007 and examines the forecasting skill of the NCEP GFS model for the Southern Hemisphere (SH) during this period. Observational and model forecast data were converted into anomaly field data in a semi-Lagrangian θ-PVLAT coordinate. The first two EOF modes of the potential vorticity (PV) anomaly in the θ-PVLAT coordinate constitute over 80% of the total variance. It was found that the SH stratospheric polar vortex is quite stable (with a period twice as long as its NH counterpart) and persistent, and that the NCEP GFS model forecasts exclusively dynamical processes such as the equatorial quasi-biennial oscillation (QBO) with extremely high success but possesses a cold bias in the troposphere and a warm bias in the stratosphere due to a lack of updated greenhouse gas information. Forecasting skill in the stratosphere was far higher than in the troposphere, and the second EOF modes possess higher forecast skill than the first. The good forecast skill in the stratosphere is due to the GFS’s ability of capturing the slow poleward propagation of stratospheric thermal anomalies associated with the polar vortex oscillation. Overall forecast skill scores were high enough to indicate that the NCEP GFS model has great potential in stratospheric climate prediction, but the model needs timely evolving greenhouse gas information to improve further.

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