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On the scale dynamics of the tropical cyclone intensity

  • * Corresponding author: ckieu@indiana.edu

    * Corresponding author: ckieu@indiana.edu 

This research was supported by the NOAA HFIP funding (Award NA16NWS4680026), the ONR funding (Grant N000141410143), and the Vice Provost for Research through the Indiana University Faculty Research Support Program

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  • This study examines the dynamics of tropical cyclone (TC) development in a TC scale framework. It is shown that this TC-scale dynamics contains the maximum potential intensity (MPI) limit as an asymptotically stable point for which the Coriolis force and the tropospheric stratification are two key parameters responsible for the bifurcation of TC development. In particular, it is found that the Coriolis force breaks the symmetry of the TC development and results in a larger basin of attraction toward the cyclonic (anticyclonic) stable point in the Northern (Southern) Hemisphere. Despite the sensitive dependence of intensity bifurcation on these two parameters, the structurally stable property of the MPI critical point is maintained for a wide range of parameters.

    Mathematics Subject Classification: Primary: 76U05, 76E09; Secondary: 58F15, 58F17.


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  • Figure 1.  A bifurcation diagram in terms of the $v$ component of the critical points as a function of the stratification parameter $s$ with fixed values $f =0.01$ and $r = 0.01$

    Figure 2.  A bifurcation diagram in terms of the $v$ component of the critical points as a function of the Coriolis parameter $f$ with fixed values $s = 0.1, r = 0.01$

    Figure 3.  a) Flow trajectories for four different initial points in the phase space of $(u, v, b)$ that represent an incipient weak anticyclonic vortex (-0.1, -0.1, 0.1) (red); a mature TC near the MPI equilibrium with a weak warm core (-1, 1, 0.5) (cyan); a mature TC with intensity significant above the MPI equilibrium limit (-1, 1.4, 1) (green); and a mature TC near the MPI equilibrium limit with too weak low level convergence (-0.1, 1, 1) (blue) for the case of $f = 0.05$; (b) Time series of $v$ during the entire simulation; and (c)-(d) Similar to (a)-(b) but for the case of $f=0$

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