Monday, 11 January 2016
Understanding the physical mechanisms that drive rapid intensification (RI) of tropical cyclones has plagued the research community over the past couple of decades; therefore in an effort to improve prediction of RI through statistical models, distinguishing the meteorological processes separating RI from non-RI tropical cyclones is necessary. Recent research aimed to identify these important diagnostic variables in the North Atlantic basin, not only at which levels, but also at which spatial points in proximity to the cyclone. Using GEFS reforecast data from 1979–2009, for a 24 hour lead time before RI, rotated principal component analysis (RPCA) was performed on 1-dimensional and 3-dimensional atmospheric output, finding the maximum variability within the dataset. Using hierarchical clustering techniques on these base state variables to group events exhibiting similar physical structures, the groups were averaged to generate composite maps. Analysis of the composites was completed for the different RI and non-RI base state variables, as well as composite derived fields including divergence, relative vorticity, equivalent potential temperature, static stability, and vertical shear. Results suggest that vorticity in the mid-levels, divergence in the upper-levels, and specific humidity play critical roles in successfully discriminating between RI and non-RI events, as well as latent heat and sea level pressure. These findings give key insights to which variables should be used in developing a prognostic classification scheme to assist with operational forecasts of cyclone RI.
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