An Ensemble Machine Learning Approach for Tropical Cyclone Localization and Tracking From ERA5 Reanalysis 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 and more destructive. The accurate localization 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 data sets through the use of deterministic tracking schemes that rely on subjective thresholds. This study presents a Machine Learning (ML) ensemble approach for locating TCs center coordinates. The ensemble combines TCs center estimates of different ML models that agree about the presence of a TC in input data. ERA5 reanalysis data was used for model training and testing jointly with the International Best Track Archive for Climate Stewardship (IBTrACS) records. Compared to single models estimates, the ML ensemble approach was able to improve TCs localization in terms of Euclidean Distance with respect to the observed TCs locations from IBTrACS. Moreover, a hybrid tracking scheme was defined: starting from the individual TC center locations detected by the ML ensemble approach, a deterministic tracking algorithm was used for reconstructing TC trajectories. The hybrid tracking scheme was then compared with four deterministic trackers reported in literature, achieving a Probability of Detection and a False Alarm Rate of 71.49% and 23%, respectively, over 40 years of reanalysis data.

An Adaptive Learning Approach for Tropical Cyclone Intensity Correction

“…The data format is similar to the outputs of dynamical models, so it can be a benchmark dataset to develop new methods for later applications. There have been some studies exploring the capability of tropical cyclone representations in the ERA5 reanalysis [45][46][47].…”