气候变化领域集成服务门户(CCPortal): Tracking Pyrometeors With Meteorological Radar Using Unsupervised Machine Learning

CCPortal

DOI	10.1029/2019GL084305
	Tracking Pyrometeors With Meteorological Radar Using Unsupervised Machine Learning
	McCarthy N.F.; Guyot A.; Protat A.; Dowdy A.J.; McGowan H.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:8
英文摘要	Pyrometeors are the large (>2 mm) debris lofted above wildfires that are composed of the by-products of combustion of the fuels. One speciation of pyrometeor is firebrands, which are burning debris that lead to ignitions ahead of the surface fire and can be the dominant mechanism of fire spread and structure loss. Pyrometeors are observed by meteorological radar. To date, there have been no investigations into identification of pyrometeor speciation with radar. Here we present an unsupervised machine learning method (Gaussian mixture model) to classify pyrometeor modes using X-band radar data. The coherent features of the mode of pyrometeor identified most likely to transport firebrands were tracked in time and space. The radar classification and tracking method shows that wildfires do produce signatures in radar returns that could be used for spot fire risk prediction. In wildfires, different types of debris (known as pyrometeors) are lofted in the smoke plumes and transported downwind. Some types of pyrometeors may, when in the air, still be burning and capable of starting new wildfires. Here we investigate the potential for meteorological radar to classify different types of pyrometeors and to track them to determine their potential for starting new fires downwind of the main fire front. © 2019. American Geophysical Union. All Rights Reserved.
英文关键词	Debris; Fires; Gaussian distribution; Machine learning; Radar tracking; Smoke; Tracking radar; Dominant mechanism; Gaussian Mixture Model; Most likely; Radar returns; Smoke plume; Tracking method; Unsupervised machine learning; X-band Radars; Meteorological radar; instrumentation; machine learning; radar; tracking; wildfire
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/170460
作者单位	Atmospheric Observations Research Group, School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD, Australia; Department of Civil Engineering, Monash University, Melbourne, VIC, Australia; Australian Bureau of Meteorology, Melbourne, VIC, Australia
推荐引用方式 GB/T 7714	McCarthy N.F.,Guyot A.,Protat A.,et al. Tracking Pyrometeors With Meteorological Radar Using Unsupervised Machine Learning[J],2020,47(8).
APA	McCarthy N.F.,Guyot A.,Protat A.,Dowdy A.J.,&McGowan H..(2020).Tracking Pyrometeors With Meteorological Radar Using Unsupervised Machine Learning.Geophysical Research Letters,47(8).
MLA	McCarthy N.F.,et al."Tracking Pyrometeors With Meteorological Radar Using Unsupervised Machine Learning".Geophysical Research Letters 47.8(2020).