气候变化领域集成服务门户(CCPortal): Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning

CCPortal

DOI	10.1029/2020GL089102
	Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning
	Su H.; Wu L.; Jiang J.H.; Pai R.; Liu A.; Zhai A.J.; Tavallali P.; DeMaria M.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:17
英文摘要	Tropical cyclone (TC) intensity change is controlled by both environmental conditions and internal storm processes. We show that TC 24-hr subsequent intensity change (DV24) is linearly correlated with the departures in satellite observations of inner-core precipitation, ice water content, and outflow temperature from respective threshold values corresponding to neutral TCs of nearly constant intensity. The threshold values vary linearly with TC intensity. Using machine learning with the inner-core precipitation and the predictors currently employed at the National Hurricane Center (NHC) for probabilistic rapid intensification (RI) forecast guidance, our model outperforms the NHC operational RI consensus in terms of the Peirce Skill Score for RI in the Atlantic basin during 2009–2014 by 37%, 12%, and 138% for DV24 ≥ 25, 30, and 35 kt, respectively. Our probability of detection is 40%, 60%, and 200% higher than the operational RI consensus, while the false alarm ratio is only 4%, 7%, and 6% higher. ©2020. The Authors.
英文关键词	Machine learning; Storms; Tropics; Environmental conditions; False alarm ratio; Forecast guidance; Ice water content; Internal structure; Probability of detection; Rapid intensification; Satellite observations; Hurricanes; forecasting method; ice flow; machine learning; outflow; probability; satellite altimetry; storm track; tropical cyclone; Atlantic Ocean
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/169786
作者单位	Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States; IBM Global Business Services, Armonk, NY, United States; RMDS Lab, Pasadena, CA, United States; Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, United States; National Hurricane Center, Miami, FL, United States
推荐引用方式 GB/T 7714	Su H.,Wu L.,Jiang J.H.,et al. Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning[J],2020,47(17).
APA	Su H..,Wu L..,Jiang J.H..,Pai R..,Liu A..,...&DeMaria M..(2020).Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning.Geophysical Research Letters,47(17).
MLA	Su H.,et al."Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning".Geophysical Research Letters 47.17(2020).