Rethinking Tropical Cyclone Genesis Potential Indices via Feature Selection

Tropical Cyclones (TCs) are synoptic-scale, rapidly rotating storm systems primarily driven by air-sea heat and moisture exchanges. They are among the deadliest geophysical hazards, causing substantial economic losses and several fatalities due to their associated strong winds, heavy precipitation, and storm surges, leading to coastal and inland flooding. Because of the severe consequences of their impacts, accurately predicting the occurrence, intensity, and trajectory of TCs is of crucial socio-economic importance. Over the past few decades, advancements in Numerical Weather Prediction models, coupled with the availability of high-quality observational data from past events, have increased the accuracy of short-term forecasts of TC tracks and intensities. However, this level of improvement has not yet been mirrored in long-term climate predictions and projections. This can be attributed to the substantial computational resources required for running high-resolution climate models with numerous ensemble members over long periods. Additionally, the physical processes underlying TC formation are still poorly understood. To overcome these challenges, the future occurrence of TCs can instead be studied using indices, known as Genesis Potential Indices (GPIs), which correlate the likelihood of Tropical Cyclone Genesis (TCG) with large-scale environmental factors instrumental in their formation. GPIs are generally constructed as a product of atmospheric and oceanic variables accounting both for dynamic and thermodynamic processes. The variables are combined with coefficients and exponents numerically determined from past TC observations. Despite reproducing the spatial pattern and the seasonal cycle of observed TCs, GPIs fail to capture the inter-annual variability and exhibit inconsistent long-term trends.

In this work, we propose a new way to formulate these indices by using Machine Learning. Specifically, we forego all previously empirically determined coefficients and exponents and consider all the dynamic and thermodynamic factors incorporated into various indices documented in the literature. Then, using feature selection algorithms, we identify the most significant variables to explain TCG. Our analysis incorporates atmospheric variables as candidate factors to discern whether they inherently possess predictive signals for TCG. Furthermore, we also consider several climate indices that have been demonstrated to be related to TCG at the ocean basin scale. Recognizing that each factor and teleconnection has a distinct impact on TCG, we tailored our analysis to individual ocean basins. Consequently, our final model comprises a series of sub-models, each corresponding to a different tropical region. These sub-models estimate the distribution of TCG using distinct inputs, which are determined based on the outcomes of the basin-specific feature selection process. Preliminary findings indicate that the feature selection process yields distinct inputs for each ocean basin.