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\begin{document}

\title{The System for Classification of Low-Pressure Systems (SyCLoPS): An
All-in-One Objective Framework for Large-scale Datasets}



\author[1]{Yushan Han}%
\author[2]{Paul Ullrich}%
\affil[1]{University of California, Davis}%
\affil[2]{University of California Davis}%


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  \date{May 25, 2024}


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\begin{abstract}
We propose the first unified objective framework (SyCLoPS) for detecting
and classifying all types of low-pressure systems (LPSs) in a given
dataset. We use the state-of-the-art automated feature tracking software
TempestExtremes (TE) to detect and track LPS features globally in ERA5
and compute 16 parameters from commonly-found atmospheric variables for
classification. A Python classifier is implemented to classify all LPSs
at once. The framework assigns 16 different labels (classes) to each LPS
data point (node) and designates four different types of high-impact LPS
tracks, including tropical cyclone (TC) tracks, Monsoon System (MS)
tracks, subtropical tropical-like cyclone (STLC) tracks, and polar low
(PL) tracks. The classification process involves disentangling
high-altitude and drier LPSs, differentiating tropical and non-tropical
LPSs using novel criteria, and optimizing for the detection of the four
types of high-impact LPS. We compare our labels to those in the
International Best Track Archive for Climate Stewardship (IBTrACS) and
find that they are in good agreement. TC detection using SyCLoPS
produces better tropical cyclone detection skill compared to the
previous algorithms. Finally, we demonstrate that the output of SyCLoPS
is valuable for investigating various aspects of LPSs, such as the
evolution of a single LPS track, patterns and trends in LPS activities,
and precipitation or wind influence associated with a particular LPS
class.%
\end{abstract}%



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