J. Climate, 23, 2335-2357.

A Poisson regression index for tropical cyclone genesis and the role of large-scale vorticity in genesis

Michael K. Tippett
International Research Institute for Climate and Society, Columbia University, Palisades, NY

Suzana J. Camargo
Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY

Adam H. Sobel
Department of Applied Physics and Applied Mathematics, Department of Earth and Environmental Sciences, and Lamont-Doherty Earth Observatory, Columbia University, New York, NY.


A Poisson regression between the observed climatology of tropical cyclogenesis (TCG) and largescale climate variables is used to construct a TCG index. The regression methodology is objective and provides a framework for the selection of the climate variables in the index. Broadly following earlier work, four climate variables appear in the index: low-level absolute vorticity, relative humidity, relative sea surface temperature (SST) and vertical shear. Several variants in the choice of predictors are explored, including relative SST vs. potential intensity and satellite-based column integrated relative humidity vs. reanalysis relative humidity at a single level; these choices make modest differences in the performance of the index. The feature of the new index which leads to the greatest improvement is a functional dependence on low-level absolute vorticity that causes the index response to absolute vorticity to saturate when absolute vorticity exceeds a threshold. This feature reduces some biases of the index and improves the fidelity of its spatial distribution to the observed climatology of genesis. Physically, this result suggests that once low-level environmental vorticity reaches a sufficiently large value, other factors become rate-limiting so that further increases in vorticity (at least on a monthly mean basis) do not increase the probability of genesis.

Although the index is fit to climatological data, it reproduces some aspects of interannual variability when applied to interannually varying data. Overall the new index compares positively to the genesis potential index (GPI) whose derivation, computation and analysis is more complex in part due to its dependence on potential intensity.