  The study of complex systems in nature is essential to understand the
interactions between different elements and how they influence one another.
Complex network theory is a powerful tool that helps us to analyze these
interactions and gain insights into the behavior of such systems. Surprisingly,
this theory has been underutilized in the field of weather science, which
focuses on the immediate state of the atmosphere. Our research aims to fill
this gap by exploring the use of complex network theory in weather science.
Specifically, we employ weather radar data to construct event-based
geographical networks. By analyzing the relations between meteorological
properties and network metrics in these event-based networks, we can gain a
better understanding of the behavior of precipitation events. Our findings
reveal significant correlations between various meteorological properties and
network metrics, shedding light on the underlying mechanisms that govern
precipitation events. Through our work, we hope to demonstrate the potential of
complex network theory in weather science and inspire further research in this
field.
