A methodology for cloud masking uncalibrated lidar signals

Most lidar processing algorithms, such as those included in EARLINET’s Single Calculus Chain, can be applied only to cloud-free atmospheric scenes. In this paper, we present a methodology for masking clouds in uncalibrated lidar signals. First, we construct a reference dataset based on manual inspec...
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Gespeichert in:

Autor*in:	Binietoglou Ioannis [verfasserIn] D’Amico Giuseppe [verfasserIn] Baars Holger [verfasserIn] Belegante Livio [verfasserIn] Marinou Eleni [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Übergeordnetes Werk:	In: EPJ Web of Conferences - EDP Sciences, 2010, 176, p 05048(2018)
Übergeordnetes Werk:	volume:176, p 05048 ; year:2018

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1051/epjconf/201817605048

Katalog-ID:	DOAJ068734212

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author	Binietoglou Ioannis
spellingShingle	Binietoglou Ioannis misc QC1-999 misc Physics A methodology for cloud masking uncalibrated lidar signals
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topic_title	QC1-999 A methodology for cloud masking uncalibrated lidar signals
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title	A methodology for cloud masking uncalibrated lidar signals
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title_full	A methodology for cloud masking uncalibrated lidar signals
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title_sort	methodology for cloud masking uncalibrated lidar signals
callnumber	QC1-999
title_auth	A methodology for cloud masking uncalibrated lidar signals
abstract	Most lidar processing algorithms, such as those included in EARLINET’s Single Calculus Chain, can be applied only to cloud-free atmospheric scenes. In this paper, we present a methodology for masking clouds in uncalibrated lidar signals. First, we construct a reference dataset based on manual inspection and then train a classifier to separate clouds and cloud-free regions. Here we present details of this approach together with an example cloud masks from an EARLINET station.
abstractGer	Most lidar processing algorithms, such as those included in EARLINET’s Single Calculus Chain, can be applied only to cloud-free atmospheric scenes. In this paper, we present a methodology for masking clouds in uncalibrated lidar signals. First, we construct a reference dataset based on manual inspection and then train a classifier to separate clouds and cloud-free regions. Here we present details of this approach together with an example cloud masks from an EARLINET station.
abstract_unstemmed	Most lidar processing algorithms, such as those included in EARLINET’s Single Calculus Chain, can be applied only to cloud-free atmospheric scenes. In this paper, we present a methodology for masking clouds in uncalibrated lidar signals. First, we construct a reference dataset based on manual inspection and then train a classifier to separate clouds and cloud-free regions. Here we present details of this approach together with an example cloud masks from an EARLINET station.
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title_short	A methodology for cloud masking uncalibrated lidar signals
url	https://doi.org/10.1051/epjconf/201817605048 https://doaj.org/article/3c7fa3a916844107b2a61200ddcd09dc https://doaj.org/toc/2100-014X
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