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DOI	10.3390/rs13163128
	The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation
	Piontek, Dennis; Bugliaro, Luca; Kar, Jayanta; Schumann, Ulrich; Marenco, Franco; Plu, Matthieu; Voigt, Christiane
通讯作者	Piontek, D (corresponding author), Deutsch Zent Luft & Raumfahrt DLR, Inst Phys Atmosphare, D-82234 Wessling Oberpfaffenhofe, Germany.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:16
英文摘要	Volcanic ash clouds can damage aircrafts during flight and, thus, have the potential to disrupt air traffic on a large scale, making their detection and monitoring necessary. The new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) using the geostationary instrument MSG/SEVIRI and artificial neural networks is introduced in a companion paper. It performs pixelwise classifications and retrieves (indirectly) the mass column concentration, the cloud top height and the effective particle radius. VACOS is comprehensively validated using simulated test data, CALIOP retrievals, lidar and in situ data from aircraft campaigns of the DLR and the FAAM, as well as volcanic ash transport and dispersion multi model multi source term ensemble predictions. Specifically, emissions of the eruptions of Eyjafjallajokull (2010) and Puyehue-Cordon Caulle (2011) are considered. For ash loads larger than 0.2 g m(-2) and a mass column concentration-based detection procedure, the different evaluations give probabilities of detection between 70% and more than 90% at false alarm rates of the order of 0.3-3%. For the simulated test data, the retrieval of the mass load has a mean absolute percentage error of similar to 40% or less for ash layers with an optical thickness at 10.8 mu m of 0.1 (i.e., a mass load of about 0.3-0.7 g m(-2), depending on the ash type) or more, the ash cloud top height has an error of up to 10% for ash layers above 5 km, and the effective radius has an error of up to 35% for radii of 0.6-6 mu m. The retrieval error increases with decreasing ash cloud thickness and top height. VACOS is applicable even for overlaying meteorological clouds, for example, the mean absolute percentage error of the optical depth at 10.8 mu m increases by only up to similar to 30%. Viewing zenith angles >60 degrees increase the mean percentage error by up to similar to 20%. Desert surfaces are another source of error. Varying geometrical ash layer thicknesses and the occurrence of multiple layers can introduce an additional error of about 30% for the mass load and 5% for the cloud top height. For the CALIOP data, comparisons with its predecessor VADUGS (operationally used by the DWD) show that VACOS is more robust, with retrieval errors of mass load and ash cloud top height reduced by >10% and >50%, respectively. Using the model data indicates an increase in detection rate in the order of 30% and more. The reliability under a wide spectrum of atmospheric conditions and volcanic ash types make VACOS a suitable tool for scientific studies and air traffic applications related to volcanic ash clouds.
英文关键词	volcanic ash cloud passive satellite remote sensing artificial neural network validation Eyjafjallajokull Puyehue-Cordon Caulle lidar in situ transport and dispersion model
类型	Article
语种	英语
开放获取类型	Green Published, Green Accepted, gold
收录类别	SCI-E
WOS记录号	WOS:000690223500001
WOS关键词	COMPLEX REFRACTIVE-INDEX ; CLOUD ; AEROSOL ; ICELAND ; HEIGHT ; PLUME
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/364478
作者单位	[Piontek, Dennis; Bugliaro, Luca; Schumann, Ulrich; Voigt, Christiane] Deutsch Zent Luft & Raumfahrt DLR, Inst Phys Atmosphare, D-82234 Wessling Oberpfaffenhofe, Germany; [Kar, Jayanta] Sci Syst & Applicat Inc, Hampton, VA 23666 USA; [Kar, Jayanta] NASA, Sci Directorate, Langley Res Ctr, Hampton, VA 23666 USA; [Marenco, Franco] Met Off, Space Applicat & Nowcasting, Exeter EX1 3PB, Devon, England; [Marenco, Franco] Climate & Atmosphere Res Ctr CARE C, Cyprus Inst, CY-2121 Nicosia, Cyprus; [Plu, Matthieu] Univ Toulouse, Ctr Natl Rech Meteorol CNRM, F-31057 Toulouse, France; [Voigt, Christiane] Johannes Gutenberg Univ Mainz, Inst Phys Atmosphare, D-55099 Mainz, Germany
推荐引用方式 GB/T 7714	Piontek, Dennis,Bugliaro, Luca,Kar, Jayanta,et al. The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation[J],2021,13(16).
APA	Piontek, Dennis.,Bugliaro, Luca.,Kar, Jayanta.,Schumann, Ulrich.,Marenco, Franco.,...&Voigt, Christiane.(2021).The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation.REMOTE SENSING,13(16).
MLA	Piontek, Dennis,et al."The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation".REMOTE SENSING 13.16(2021).