Measuring Environmental Favorability for Tropical Cyclogenesis by Statistical Analysis of Threshold Parameters

Abstract

Abstract As the climate changes, the ability to predict changes in the frequency of tropical cyclogenesis is becoming of increasing interest. A unique approach is proposed that utilizes threshold values in potential intensity, wind shear, vorticity, and normalized saturation deficit. Prior statistical methods generally involve creating an index or equation based on averages of important meteorological parameters for a given region. The new method assumes that threshold values exist for each important parameter for which cyclogenesis is unlikely to develop. This technique is distinct from previous approaches that seek to determine how each of these parameters interdependently favors cyclogenesis. To determine three of the individual threshold values (shear, potential intensity, and vorticity), an idealized climate is first established that represents the most advantageous but realistic (MABR) environment. An initial numerical simulation of tropical cyclone genesis in the MABR environment confirms that it is highly favorable for cyclogenesis. Subsequent numerical simulations vary each parameter individually until no tropical cyclone develops, thereby determining the three threshold values. The new method of point downscaling, whereby background meteorological features are represented by a single vertical profile, is used in the simulations to greatly simplify the approach. The remaining threshold parameter (normalized saturation deficit) is determined by analyzing the climatological record and choosing a value that is statistically observed to prevent cyclogenesis. Once each threshold value is determined, the fraction of time each is exceeded in the location of interest is computed from the reanalysis dataset. The product of each fraction for each of the relevant parameters then gives a statistical probability as to the likelihood of cyclogenesis. For predicting regional and monthly variations in frequency of genesis, this approach is shown to generally meet or exceed the predictive skills of earlier statistical attempts with some failure only during several off-season months. This method also provides a more intuitive rationale of the results.