Optimisation-based refinement of genesis indices for tropical cyclones

Abstract

Tropical cyclone genesis indices are valuable tools for studying the relationship between large-scale environmental fields and the genesis of tropical cyclones, supporting the identification of future trends of cyclone genesis. However, their formulation is generally derived from simple statistical models (e.g., multiple linear regression) and are not optimised globally. In this paper, we present a simple framework for optimising genesis indexes given a user-specified trade-off between two performance metrics, which measure how well an index captures the spatial and interannual variability of tropical cyclone genesis. We apply the proposed framework to the popular Emanuel and Nolan Genesis Potential Index, yielding new, optimised formulas that correspond to different trade-offs between spatial and interannual variability. Results show that our refined indexes can improve the performance of the Emanuel and Nolan index up to 8% for spatial variability and 16-22% for interannual variability; this improvement was found to be statistically significant (p<0.01). Lastly, by analysing the formulas found, we give some insights into the role of the different inputs of the index in maximising one metric or the other.

Tropical Cyclone Genesis Potential Indices in a New High‐Resolution Climate Models Ensemble: Limitations and Way Forward

“…Recent work from Ascenso et al. (2023) using advanced optimization techniques showed that this is indeed the case for TC interannual variability and spatial patterns. Moreover, they provide a framework to quantify and select the desired trade‐off, depending on the desired application of the index.…”

Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

“…In Meng et al (2023), different Gradient Boosting approaches have been proposed for probabilistic forecasting of TC intensity from different predictive variables such as sea surface temperature data, satellite bright temperature data, and data from other models and satellite-derived variables. Finally, in Ascenso et al (2023), a ML framework based on evolutionary computation techniques (genetic algorithms Del Ser et al (2019)) is applied to the optimization of TC genesis indexes. This approach is shown to obtain an index which captures the spatial and interannual variability of tropical cyclone genesis.…”