An Ensemble Machine Learning Approach for Tropical Cyclone Detection Using ERA5 Reanalysis Data

TL;DR

This paper introduces an ensemble machine learning method for detecting and locating tropical cyclones using ERA5 reanalysis data, improving accuracy and generalization over traditional deterministic tracking methods.

Contribution

The study presents a novel ensemble ML approach that combines multiple models for simultaneous TC classification and localization, enhancing detection accuracy and robustness.

Findings

Effective detection of lower-category TCs.

Improved localization accuracy with ensemble methods.

Good generalization to out-of-sample data.

Abstract

Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger, larger and more destructive. The accurate detection and tracking of such phenomena have become a relevant and interesting area of research in weather and climate science. Traditionally, TCs have been identified in large climate datasets through the use of deterministic tracking schemes that rely on subjective thresholds. Machine Learning (ML) models can complement deterministic approaches due to their ability to capture the mapping between the input climatic drivers and the geographical position of the TC center from the available data. This study presents a ML ensemble approach for locating TC center coordinates, embedding both TC classification and localization in a single end-to-end learning task. The ensemble combines TC center estimates of different ML models that agree about the presence of a TC in input data. ERA5 reanalysis were used for model training and testing jointly with the International Best Track Archive for Climate Stewardship records. Results showed that the ML approach is well-suited for TC detection providing good generalization capabilities on out of sample data. In particular, it was able to accurately detect lower TC categories than those used for training the models. On top of this, the ensemble approach was able to further improve TC localization performance with respect to single model TC center estimates, demonstrating the good capabilities of the proposed approach.