ハリケーンの強さを予測する(Predicting the Intensity of Hurricanes)

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2024-04-05 パシフィック・ノースウェスト国立研究所(PNNL)

熱帯低気圧(TCs)の急激な発達(RI)は、沿岸地域への深刻な経済的および社会的脅威をもたらす可能性があります。この研究では、環境パラメーターの同時発生に基づく新しいモデル(MCE)が開発され、これによりリアルタイムでRIの可能性が評価されます。MCEは、環境の好適な要因と不利な要因の数を簡単にカウントし、これらを組み合わせることで、現行のモデリング技術と比較して誤報の数が減少します。この新しい手法により、大規模な環境が有利または不利である場合に、RIがどのように発展するかについてより良い理解が得られ、MCEは他の機械学習手法よりも14%正確性が高いことが示されました。

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共存する環境パラメータに基づくハリケーンの急速な激化を予測する新しい手法 A new method for predicting hurricane rapid intensification based on co-occurring environmental parameters

Anushka Narayanan,Karthik Balaguru,Wenwei Xu & L. Ruby Leung
Natural Hazards  Published:27 September 2023
DOI:https://doi.org/10.1007/s11069-023-06100-z

ハリケーンの強さを予測する(Predicting the Intensity of Hurricanes)

Abstract

Tropical cyclones (TCs) that undergo Rapid Intensification (RI) can pose serious socioeconomic threats and can potentially result in major damaging impacts along coastal areas. Considering the complexity of various physical mechanisms that play a role in RI and its relatively low probability of occurrence, predicting RI remains a major operational challenge. In this study, we propose a simple deterministic binary classification model based on the co-occurrence of environmental parameters (MCE) to predict an RI event. More specifically, the model determines the possibility of RI based on a simple count of the number of environmental predictors deemed favorable and unfavorable. We compare our model results to logistic regression (LR) and decision tree (DT) models, well-trained using the same set of environmental predictors. Results reveal that at an RI threshold of 30 kt, the MCE exhibits a critical success index score of 0.233 which is 14% higher than DT and LR model performances. When tested at multiple RI thresholds, the MCE displays relatively higher skill scores across multiple metrics. By simultaneously evaluating the favorability of predictors, the MCE is able to comparatively reduce the number of false alarms predicted when certain predictors are unfavorable toward RI. Interpreting these model results to gain a physical understanding of how co-occurring environmental parameters can affect RI, we highlight future directions for using models based on the MCE approach to understand and predict TC RI as well as other meteorological extremes.

1702地球物理及び地球化学
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