Classification Analysis of Southwest Pacific Tropical Cyclone Intensity Changes Prior to Landfall

被引:2
|
作者
Bhowmick, Rupsa [1 ]
Trepanier, Jill C. [2 ]
Haberlie, Alex M. [3 ]
机构
[1] Los Alamos Natl Lab, Computat Phys & Methods CCS 2, Los Alamos, NM 87545 USA
[2] Louisiana State Univ, Dept Geog & Anthropol, Baton Rouge, LA 70803 USA
[3] Northern Illinois Univ, Dept Earth Atmosphere & Environm, De Kalb, IL 60115 USA
关键词
tropical cyclone forecasting; landfalling intensity; machine learning classification; random forest classifier; Southwest Pacific Ocean; PART I; CROSS-VALIDATION; CLIMATE-CHANGE; IMPACT; OCEAN; TEMPERATURE; MODEL; SIMULATIONS; TERRAIN; LAYER;
D O I
10.3390/atmos14020253
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study evaluates the ability of a random forest classifier to identify tropical cyclone (TC) intensification or weakening prior to landfall over the western region of the Southwest Pacific Ocean (SWPO) basin. For both Australia mainland and SWPO island cases, when a TC first crosses land after spending >= 24 h over the ocean, the closest hour prior to the intersection is considered as the landfall hour. If the maximum wind speed (V-max) at the landfall hour increased or remained the same from the 24-h mark prior to landfall, the TC is labeled as intensifying and if the V-max at the landfall hour decreases, the TC is labeled as weakening. Geophysical and aerosol variables closest to the 24 h before landfall hour were collected for each sample. The random forest model with leave-one-out cross validation and the random oversampling example technique was identified as the best-performing classifier for both mainland and island cases. The model identified longitude, initial intensity, and sea skin temperature as the most important variables for the mainland and island landfall classification decisions. Incorrectly classified cases from the test data were analyzed by sorting the cases by their initial intensity hour, landfall hour, monthly distribution, and 24-h intensity changes. TC intensity changes near land strongly impact coastal preparations such as wind damage and flood damage mitigations; hence, this study will contribute to improve identifying and prioritizing prediction of important variables contributing to TC intensity change before landfall.
引用
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页数:25
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