2016
DOI: 10.5194/amt-9-5007-2016
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Microphysical particle properties derived from inversion algorithms developed in the framework of EARLINET

Abstract: Abstract. We present a summary on the current status of two inversion algorithms that are used in EARLINET (European Aerosol Research Lidar Network) for the inversion of data collected with EARLINET multiwavelength Raman lidars. These instruments measure backscatter coefficients at 355, 532, and 1064 nm, and extinction coefficients at 355 and 532 nm. Development of these two algorithms started in 2000 when EARLINET was founded. The algorithms are based on a manually controlled inversion of optical data which a… Show more

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Cited by 55 publications
(38 citation statements)
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“…This method has been developed in the framework of EAR-LINET (Müller et al, 2016) and is based on explicitly solving the mathematical equations that relate the particle microphysical and optical properties by means of regularization techniques, an approach that is shared with Müller et al (1999) and Veselovskii et al (2002) inversion algorithms. That means that forward computations using tables containing microphysical versus optical properties are not carried out, having the advantage that particle size distribution shape is not assumed as input, but approximately calculated as output.…”
Section: Methodsmentioning
confidence: 99%
“…This method has been developed in the framework of EAR-LINET (Müller et al, 2016) and is based on explicitly solving the mathematical equations that relate the particle microphysical and optical properties by means of regularization techniques, an approach that is shared with Müller et al (1999) and Veselovskii et al (2002) inversion algorithms. That means that forward computations using tables containing microphysical versus optical properties are not carried out, having the advantage that particle size distribution shape is not assumed as input, but approximately calculated as output.…”
Section: Methodsmentioning
confidence: 99%
“…It is required, for example, for closure experiments (consistency checks between different measurement methods involving an aerosol model, e.g., Wiegner et al, 2009;Gasteiger et al, 2011b;Müller et al, 2012;Bell et al, 2013;Ma et al, 2014;Zieger et al, 2014;Düsing et al, 2018), radiative transfer studies (e.g., Otto et al, 2009;Emde et al, 2010), the inversion of remote-sensing measurements (e.g., Dubovik et al, 2006;Gasteiger et al, 2011a;Müller et al, 2016), the inversion of in situ data (e.g., Weinzierl et al, 2009;Szymanski et al, 2009;Kassianov et al, 2014), aerosol layer visibility simulations (e.g., Weinzierl et al, 2012), dynamic aerosol transport models (e.g., Heinold et al, 2007;Balzarini et al, 2015), aerosol characterization (e.g., Gasteiger et al, 2017;Che et al, 2018;Zhuang et al, 2018), and solar energy (e.g., Polo et al, 2016;Kosmopoulos et al, 2017).…”
Section: Applicationsmentioning
confidence: 99%
“…Desert dust emissions and transport are described with the BSC-DREAM8b model (Nickovic et al, 2001;Pérez et al, 2006;Basart et al, 2012a). The BSC-DREAM8b model is embedded into the Eta/NCEP atmospheric model and solves the mass balance equation for dust, taking into account the different processes of the dust cycle (i.e.…”
Section: Desert Dust Modelmentioning
confidence: 99%
“…Dubovik et al, 2006), whereas the lidar is capable of providing vertical profiles of the backscatter and extinction coefficients, along with vertical profiles of the particle microphysical properties, mainly for the fine mode (e.g. Müller et al, 2016). The combination of active and passive remote sensing has been tried so far mostly by using the sun-photometer-measured aerosol optical depth (AOD) as ancillary information for the lidar retrieval (e.g.…”
Section: Introductionmentioning
confidence: 99%