气候变化领域集成服务门户(CCPortal): Agile Adaptive Radar Sampling of Fast-Evolving Atmospheric Phenomena Guided by Satellite Imagery and Surface Cameras

CCPortal

DOI	10.1029/2020GL088440
	Agile Adaptive Radar Sampling of Fast-Evolving Atmospheric Phenomena Guided by Satellite Imagery and Surface Cameras
	Kollias P.; Luke E.; Oue M.; Lamer K.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:14
英文摘要	The collection of high temporal resolution radar observations without compromising data quality requires adaptability and agility. So far, radar beam steering has been mostly guided by (i) expert judgment or (ii) stand-alone automated identification and tracking algorithms operating on measurements collected by radar. The current study proposes a new paradigm, where external observations are used to optimize a radar's sampling strategy. Here the sampling strategy of a phased-array radar and a polarimetric scanning cloud radar, two different yet uniquely complementary systems, is guided by an algorithm that uses observations from a geostationary satellite, a surface camera and the radars themselves to identify and track atmospheric phenomena. The tailored pointing and increase in sensitivity realized through this framework enables the steered radars to sample a diverse set of atmospheric phenomena such as shallow cumuli, lightning-induced ice crystal orientation and a series of waterspouts. ©2020. The Authors.
英文关键词	Cameras; Crystal orientation; Geostationary satellites; Radar imaging; Radar signal processing; Radar tracking; Satellite imagery; Space-based radar; Atmospheric phenomena; Automated identification; Complementary systems; High temporal resolution; Phased array radars; Polarimetric scanning; Radar observations; Sampling strategies; Radar measurement; algorithm; automation; data quality; geostationary satellite; ice crystal; optimization; radar; satellite data; satellite imagery; tracking
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/170117
作者单位	Division of Atmospheric Sciences, Stony Brook University, Stony Brook, NY, United States; Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, United States
推荐引用方式 GB/T 7714	Kollias P.,Luke E.,Oue M.,et al. Agile Adaptive Radar Sampling of Fast-Evolving Atmospheric Phenomena Guided by Satellite Imagery and Surface Cameras[J],2020,47(14).
APA	Kollias P.,Luke E.,Oue M.,&Lamer K..(2020).Agile Adaptive Radar Sampling of Fast-Evolving Atmospheric Phenomena Guided by Satellite Imagery and Surface Cameras.Geophysical Research Letters,47(14).
MLA	Kollias P.,et al."Agile Adaptive Radar Sampling of Fast-Evolving Atmospheric Phenomena Guided by Satellite Imagery and Surface Cameras".Geophysical Research Letters 47.14(2020).