Nonlocal Controls on Tropical Cyclogenesis: A Trajectory-Based Genesis Potential Index

Abstract

Abstract Tropical cyclone (TC) genesis is initiated by convective precursors or “seeds” and influenced by environmental conditions along the seed-to-TC trajectories. Genesis potential indices (GPIs) provide a simple way to evaluate TC genesis likelihood from environmental conditions but have two limitations that may introduce bias. First, the globally fixed GPIs fail to represent interbasin differences in the relationship between environments and genesis. Second, existing GPIs are only functions of local environmental conditions, whereas nonlocal factors may have a significant impact. We address the first limitation by constructing basin- and time-scale-specific GPIs (local-GPIs) over the eastern North Pacific (ENP) and North Atlantic (NA) using Poisson regression. A sequential feature selection (SFS) algorithm identifies vertical wind shear and a heating condition as leading factors controlling TC genesis in the ENP and the NA, respectively. However, only a slight improvement in performance is achieved, motivating us to tackle the second limitation with a novel trajectory-based GPI (traj-GPI). We merge adjacent nonlocal environments into each grid point based on observed seed trajectory densities. The seed activity, driven mainly by upward motion, and the transition to TCs, controlled primarily by vertical wind shear or heating conditions, are captured simultaneously in the traj-GPI, yielding a better performance than the original GPIs. This study illustrates the importance of seed activity in modeling TC genesis and identifies key environmental factors that influence the process of TC genesis at different stages. Significance Statement The genesis potential index (GPI) is an effective tool for modeling the likelihood of tropical cyclone (TC) genesis for a given time and location. This study reveals that existing GPIs are primarily biased by a lack of information about nonlocal TC seed activity, since they are based only on local large-scale environmental variables. According to our study, upward motion and vertical wind shear are the most influential environmental factors in seed genesis and the transition from seed to TC, respectively. Based on the observed seed trajectories, we build trajectory-based GPIs that include the information from seed activity. Spatiotemporal performances of TC genesis are significantly improved over the original GPIs.