  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.
