气候变化领域集成服务门户(CCPortal): Global monitoring of deep convection using passive microwave observations

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DOI	10.1016/j.atmosres.2020.105244
	Global monitoring of deep convection using passive microwave observations
	Rysman J.-F.; Claud C.; Dafis S.
发表日期	2021
ISSN	0169-8095
卷号	247
英文摘要	In this study, we present the DEEPSTORM (DEEP moiSt aTmospheric cOnvection from micRowave radioMeter) algorithm, able to retrieve ice water path (IWP) and to detect deep moist atmospheric convection (DC) from 80°S to 80°N using observations from four spaceborne passive microwave radiometers. DEEPSTORM is based on a machine learning approach and is fitted against observations from the CPR (Cloud Profiling Radar) spaceborne radar on-board CloudSat. IWP predictions show an average root mean square error of 0.27 kg/m2 and a correlation index of 0.87. DC occurrence is detected with a probability of 59% and a false alarm rate of 24%. The prediction accuracy of IWP and DC is significantly better when the IWP exceeds 0.5 kg/m2 showing that DEEPSTORM is well suited to detect and characterise the strongest DC events. Overall DC detection is more accurate in the tropics than in mid-latitudes while the IWP retrieval works better in the mid-latitudes. Two examples illustrating the potential of DEEPSTORM are presented: the IWP is retrieved during Hurricane Matthew in 2016, and a climatology of DC occurrences between September 2016 and December 2016 is presented. This work will allow building a quasi-worldwide and 20-year long database of DC occurrence and intensity. © 2020 Elsevier B.V.
英文关键词	Atmospheric ice; Deep moist atmospheric convection; Passive microwave
语种	英语
scopus关键词	Atmospheric thermodynamics; Mean square error; Microwave devices; Microwave measurement; Radiometers; Space applications; Space-based radar; Turing machines; Atmospheric convection; Cloud Profiling Radars; Global monitoring; Machine learning approaches; Microwave radiometers; Passive microwaves; Prediction accuracy; Root mean square errors; Natural convection; accuracy assessment; air-ice interaction; atmospheric convection; error analysis; hurricane event; numerical model; probability; radiometric survey; satellite data
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141738
作者单位	LMD/IPSL, École Polytechnique, Institut Polytechnique de Paris, ENS, Université Paris-Saclay, Sorbonne Université, PSL Université, CNRS, Palaiseau, France; National Observatory of Athens, Institute for Environmental Research and Sustainable Development, Vas. Pavlou & Metaxa, Athens, Greece
推荐引用方式 GB/T 7714	Rysman J.-F.,Claud C.,Dafis S.. Global monitoring of deep convection using passive microwave observations[J],2021,247.
APA	Rysman J.-F.,Claud C.,&Dafis S..(2021).Global monitoring of deep convection using passive microwave observations.Atmospheric Research,247.
MLA	Rysman J.-F.,et al."Global monitoring of deep convection using passive microwave observations".Atmospheric Research 247(2021).