A Predictive Statistical Model for Tropical Cyclone Genesis

Tropical cyclones (TCs) are important extreme weather phenomena that have significant negative impact on humans, infrastructure, and society. Multidecadal simulations from high resolution regional and global climate models are now being used to better understand how TC statistics change due to anthropogenic climate change. Aspects of tropical cyclones such as their genesis, evolution, intensification, and dissipation over land are important and challenging problems in climate science. Although various studies have been conducted on the climatology of tropical cyclone genesis (TCG), their analyses are often limited in that they focus on basin-specific models, discard high resolution data in favor of aggregate measures, or bias their investigation towards variables and metrics that are motivated by mathematical physics.

This study investigates environmental conditions associated with TCG by testing how accurately a statistical model can predict TCG in the Community Atmospheric Model (CAM) Version 5.1. The defining feature of this study is the use of multi-variate high resolution data without any imposed criteria or physical constraints. TC trajectories in CAM5.1 are defined using the Toolkit for Extreme Climate Analysis (TECA) software [TECA: Petascale Pattern Recognition for Climate Science, Prabhat et al., Computer Analysis of Images and Patterns, 2015] and are based on standard criteria used by the community (thresholds on pressure, temperature, vorticity, etc.). L1-regularized logistic regression (L1LR) is applied to distinguish between TCG events and non-developing storms. In this study we define a developing storm event as a tropical depression that matures into a TC (Cat 0 through 5) and a non-developing storm event as that which does not. We assess our model on two sets of test data. First, when tested on data with no TC track association (no storm events) the model has near perfect accuracy. Secondly, when differentiating between developing and non-developing storm events, it predicts with high accuracy.

The model’s active variables are generally in agreement with current leading hypotheses on favorable conditions for TCG, such as cyclonic wind velocity patterns and local pressure minima. However, other variables such as a sea surface temperature, precipitation, and vertical wind shear are seen to have marginal influence. We note that this is not contradictory with existing physical understanding of the mechanisms of TCG because the goal of this model is to achieve high predictive accuracy with no imposed physical constraints. Hence the model discovers the variables and spatial patterns that maximize predictive accuracy, irrespective of their role in the physics of TCG. Therefore it is also reasonable to expect that the model may only use the variables that are most influential in predicting TCG and not necessarily all the variables associated with the physics of TCG. Furthermore, our model’s predictions of the climatology of TCG exceed the predictive ability of instantaneous versions of other physically and climatologically-motivated indices such as the Genesis Potential Index (GPI), Ventilation Index, and Entropy Excess.