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\begin{document}

\title{An Alternative Similar Tropical Cyclone Identification Algorithm for
Statistical TC Rainfall Prediction}



\author[1]{Jose Angelo Arocena Hokson}%
\author[1]{Shinjiro Kanae}%
\affil[1]{Tokyo Institute of Technology}%


\vspace{-1em}



  
  \date{May 19, 2024}


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\begin{abstract}
There is a need to improve the prediction of tropical cyclone (TC)
rainfall as climate change has led to increased TC rainfall rates.
Enhanced reliability in predicting TC tracks has paved the way for
statistical methodologies to utilize them in estimating current TC
rainfall, achieved by identifying similar past TC tracks and obtaining
their corresponding rainfall data. The widely used Fuzzy C Means (FCM)
clustering algorithm, though popular, has limitations stemming from its
clustering-centric design, hindering its ability to pinpoint the most
appropriate similar TCs. Our study introduces the Sinkhorn Distance as a
novel measure of TC similarity in rainfall prediction. Our findings
indicate that the incorporation of Sinkhorn Distance significantly
enhances the accuracy of TC rainfall predictions across WNP. When
compared to the conventional approach using FCM, our Sinkhorn
Distance-based methodology consistently yields better results, as
demonstrated by metrics like RMSE and correlation coefficients.
Collectively, the inclusion of Sinkhorn Distance stands as a valuable
addition to our toolkit for discerning similar TC tracks, thus elevating
the precision of TC rainfall predictions. With ongoing advancements in
statistical and AI techniques, we anticipate even more refined
approaches to further enhance our predictive capabilities. This study
represents a leap forward in meeting the critical need for more accurate
TC rainfall forecasts in the WNP Region.%
\end{abstract}%



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