气候变化领域集成服务门户(CCPortal): Evaluating Variations in Tropical Cyclone Precipitation in Eastern Mexico Using Machine Learning Techniques

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DOI	10.1029/2021JD034604
	Evaluating Variations in Tropical Cyclone Precipitation in Eastern Mexico Using Machine Learning Techniques
	Zhu L.; Aguilera P.
发表日期	2021
ISSN	2169897X
卷号	126 期号:7
英文摘要	Tropical Cyclone Precipitation (TCP) is one of the major triggers of flash flooding and landslide in eastern Mexico. We apply different statistical and machine learning techniques to study a 99 years TCP climatology in high resolution. Strong correlations exist between location variables and annual mean TCP, as well as between dynamic variables and event TCP. Topographic variables observe mixed signals with the elevation variances positively correlated with TCP. The Random Forest (RF) model is a powerful tool with excellent fitting and predicting skills for TCP variations. It has a very small out-of-sample cross-validation error and well captures the spatial variations of historical TCP events. Only three location variables are needed to construct the best model for the annual mean TCP while the best model needs 18 variables to explain the complex variations in the event TCP. The distance to the track is the most important variable for the event TCP model and many other factors contribute to the TCP collectively and nonlinearly, which can't be captured fully by the previous correlation analysis. They include translation characteristics of the storms, locations of the precipitation grid, and topography. Event TCP is generally larger in storms with slower translation speed and more variance in their tracks. While the lower coastal area generally has a higher probability of TCP, the higher inland has elevation variances that enhance less frequent but extreme TCP events. The RF algorithm is an efficient machine learning approach showing potentials for future Quantitative Precipitation Forecasting (QPF). © 2021. American Geophysical Union. All Rights Reserved.
英文关键词	Climatology; machine learning; Mexico; precipitation; prediction; tropical cyclone
语种	英语
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/185351
作者单位	Department of Geography, Environment, and Tourism, Western Michigan University, Kalamazoo, MI, United States; Department of Physics, Western Michigan University, Kalamazoo, MI, United States
推荐引用方式 GB/T 7714	Zhu L.,Aguilera P.. Evaluating Variations in Tropical Cyclone Precipitation in Eastern Mexico Using Machine Learning Techniques[J],2021,126(7).
APA	Zhu L.,&Aguilera P..(2021).Evaluating Variations in Tropical Cyclone Precipitation in Eastern Mexico Using Machine Learning Techniques.Journal of Geophysical Research: Atmospheres,126(7).
MLA	Zhu L.,et al."Evaluating Variations in Tropical Cyclone Precipitation in Eastern Mexico Using Machine Learning Techniques".Journal of Geophysical Research: Atmospheres 126.7(2021).