Complex networks for tracking extreme rainfall during typhoons

Forecasting extreme rainfall remains a challenge, even with modern tools. Existing methods struggle to distinguish between typhoon-driven and frontal storm rainfall patterns. Prior research has shown that typhoons and frontal systems produce distinct rainfall distributions. However, no prior work had resolved how to isolate typhoon-related rainfall from other sources. This gap motivated the development of new analytical tools. The study addresses uncertainty in rainfall attribution during typhoon seasons. It builds on established knowledge of typhoon motion patterns. The goal is to improve the accuracy of rainfall tracking in complex weather systems.

This work aims to track extreme rainfall events linked specifically to typhoons. The study focuses on disentangling typhoon-driven rainfall from frontal storm contributions. It seeks to improve hindcasting of typhoon rainfall patterns. The research is motivated by the need for better rainfall attribution in meteorological models. The study addresses the challenge of distinguishing between rainfall sources. It builds on prior knowledge of typhoon motion and rainfall distribution. The goal is to enhance forecasting accuracy for typhoon-related events. The work proposes a novel method to isolate typhoon-driven rainfall patterns.

The study uses complex networks defined by event synchronization to track rainfall patterns. Directed networks are used to identify heavy rain events from typhoons and frontal systems. A nonlinear synchronization measure is applied to isolate typhoon-related rainfall. The radial rank method is introduced to analyze rainfall paths during typhoon seasons. The approach distinguishes between typhoon and frontal storm contributions. Data is filtered to exclude local convective storms. The method focuses on the August to November typhoon season in Japan. The study applies these tools to historical rainfall records in Japan.

Rainfall paths linked to typhoons follow a southwest-northeast motion pattern. The study found that typhoon eye tracks deviate from these rainfall paths. This deviation may distort estimates of heavy typhoon rainfall. The radial rank method revealed lower spread in rainfall tracks during ASON months. This pattern suggests better hindcasting potential for typhoon-driven rainfall. The study found that frontal storms in June and July have higher track variability. Rainfall from typhoons is more predictable than from westerly-fed frontal systems. The method successfully isolates typhoon-related rainfall from other sources.

The authors propose that rainfall paths during typhoon seasons follow typhoon motion patterns. They suggest that deviations in typhoon eye tracks may affect rainfall estimates. The radial rank method may improve hindcasting accuracy for typhoon-related events. The study concludes that typhoon-driven rainfall is more predictable than frontal storm rainfall. The findings suggest that isolating typhoon-related rainfall improves forecasting models. The authors state that their method effectively distinguishes between rainfall sources. They propose that this approach may enhance meteorological forecasting accuracy. The study highlights the importance of distinguishing between rainfall sources in complex weather systems.