Airborne high spectral resolution lidar observation of pollution aerosol during EUCAARI-LONGREX

Groß, Silke; Esselborn, Michael; Abicht, Florian; Wirth, Martin; Fix, Andreas; Minikin, Andreas

doi:10.5194/acp-13-2435-2013

Cited by 23 publications

(17 citation statements)

References 35 publications

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“…overpass directly over Mexico City where the backscattering and extinction are at a maximum. In contrast to the dust, this sample has a higher lidar ratio, 51 ± 5 sr, larger backscatter color ratio, 1.8 ± 0.1, and small depolarization ratio of 0.07, consistent with other lidar measurements of urban aerosol (Burton et al, 2012(Burton et al, , 2013Groß et al, 2013a). These measurements are also shown in Table 1.…”

Section: Hsrl-1 Observations Of Dust and Pollution Mixtures During MIsupporting

confidence: 76%

Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data

et al. 2014

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Abstract. Knowledge of aerosol type is important for determining the magnitude and assessing the consequences of aerosol radiative forcing, and can provide useful information for source attribution studies. However, atmospheric aerosol is frequently not a single pure type, but instead occurs as a mixture of types, and this mixing affects the optical and radiative properties of the aerosol. This paper extends the work of earlier researchers by using the aerosol intensive parameters measured by the NASA Langley Research Center airborne High Spectral Resolution Lidar (HSRL-1) to develop a comprehensive and unified set of rules for characterizing the external mixing of several key aerosol intensive parameters: extinction-to-backscatter ratio (i.e., lidar ratio), backscatter color ratio, and depolarization ratio. We present the mixing rules in a particularly simple form that leads easily to mixing rules for the covariance matrices that describe aerosol distributions, rather than just single values of measured parameters. These rules can be applied to infer mixing ratios from the lidar-observed aerosol parameters, even for cases without significant depolarization. We demonstrate our technique with measurement curtains from three HSRL-1 flights which exhibit mixing between two aerosol types, urban pollution plus dust, marine plus dust, and smoke plus marine. For these cases, we infer a time-height cross-section of extinction mixing ratio along the flight track, and partition aerosol extinction into portions attributed to the two pure types.

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Section: Hsrl-1 Observations Of Dust and Pollution Mixtures During MIsupporting

confidence: 76%

Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data

et al. 2014

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“…Data availability. The satellite products used in this study are the CALIPSO 5 km aerosol profile product (Vaughan et al, 2019) publicly available at the NASA Atmospheric Science Data Center (Nasa Atmospheric Science data center, 2019); the DARDAR-Mask v1.1.4 product (Ceccaldi et al, 2013) as well as the DARDAR-Cloud v2.1.1 retrieval product (Delanoë, et al, 2014), both publicly available on the AERIS/ICARE database (ICARE data and services center, 2019); and The MODIS Corrected Reflectance (True Color) images (Gumley et al, 2010) publicly available on the NASA Worldview center (NASA Worldview snapshots application center, 2019). The in situ INP data used in this study can be accessed through the BACCHUS database of INP observations (BACCHUS Ice Nucleation DataBase, 2019).…”

Section: Discussionmentioning

confidence: 99%

Retrieval of ice-nucleating particle concentrations from lidar observations and comparison with UAV in situ measurements

et al. 2019

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Abstract. Aerosols that are efficient ice-nucleating particles (INPs) are crucial for the formation of cloud ice via heterogeneous nucleation in the atmosphere. The distribution of INPs on a large spatial scale and as a function of height determines their impact on clouds and climate. However, in situ measurements of INPs provide sparse coverage over space and time. A promising approach to address this gap is to retrieve INP concentration profiles by combining particle concentration profiles derived by lidar measurements with INP efficiency parameterizations for different freezing mechanisms (immersion freezing, deposition nucleation). Here, we assess the feasibility of this new method for both ground-based and spaceborne lidar measurements, using in situ observations collected with unmanned aerial vehicles (UAVs) and subsequently analyzed with the FRIDGE (FRankfurt Ice nucleation Deposition freezinG Experiment) INP counter from an experimental campaign at Cyprus in April 2016. Analyzing five case studies we calculated the cloud-relevant particle number concentrations using lidar measurements (n250,dry with an uncertainty of 20 % to 40 % and Sdry with an uncertainty of 30 % to 50 %), and we assessed the suitability of the different INP parameterizations with respect to the temperature range and the type of particles considered. Specifically, our analysis suggests that our calculations using the parameterization of Ullrich et al. (2017) (applicable for the temperature range −50 to −33 ∘C) agree within 1 order of magnitude with the in situ observations of nINP; thus, the parameterization of Ullrich et al. (2017) can efficiently address the deposition nucleation pathway in dust-dominated environments. Additionally, our calculations using the combination of the parameterizations of DeMott et al. (2015, 2010) (applicable for the temperature range −35 to −9 ∘C) agree within 2 orders of magnitude with the in situ observations of INP concentrations (nINP) and can thus efficiently address the immersion/condensation pathway of dust and nondust particles. The same conclusion is derived from the compilation of the parameterizations of DeMott et al. (2015) for dust and Ullrich et al. (2017) for soot. Furthermore, we applied this methodology to estimate the INP concentration profiles before and after a cloud formation, indicating the seeding role of the particles and their subsequent impact on cloud formation and characteristics. More synergistic datasets are expected to become available in the future from EARLINET (European Aerosol Research Lidar Network) and in the frame of the European ACTRIS-RI (Aerosols, Clouds, and Trace gases Research Infrastructure). Our analysis shows that the developed techniques, when applied on CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) spaceborne lidar observations, are in agreement with the in situ measurements. This study gives us confidence for the production of global 3-D products of cloud-relevant particle number concentrations (n250,dry, Sdry and nINP) using the CALIPSO 13-year dataset. This could provide valuable insight into the global height-resolved distribution of INP concentrations related to mineral dust, as well as possibly other aerosol types.

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“…The most reliable and therefore preferable approach is to define lidar ratio using coincident measurement by developing enhanced lidar capabilities or by obtaining missing information from other instruments (Ferrare et al, 1998a;Gobbi et al, 2003). For example, enhancement of lidar observation can be achieved by employing lidar systems registering combined elastic-Raman signals (Ansmann et al, 1992;Ferrare et al, 1998a,b;Turner et al, 2002;Müller et al, 2007) or by conducting high spectral resolution lidar observations (Shipley et al, 1983;Liu et al, 2002;Hair et al, 2008;Burton et al, 2012;Groß et al, 2013). Usage of approaches with non-elastic observations result in significant enhancement of the information contents in backscattering observations, which allows derivation of aerosol extinction profiles and even estimations of aerosol microphysical properties without a priori constraints on aerosol type or loading (Müller et al, 1999(Müller et al, , 2005Veselovskii et al, 2004).…”

Section: A Lopatin Et Al: Garrlic -Synergetic Inversion Of Lidar Anmentioning

confidence: 99%

Enhancement of aerosol characterization using synergy of lidar and sun-photometer coincident observations: the GARRLiC algorithm

et al. 2013

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Abstract. This paper presents the GARRLiC algorithm (Generalized Aerosol Retrieval from Radiometer and Lidar Combined data) that simultaneously inverts coincident lidar and radiometer observations and derives a united set of aerosol parameters. Such synergetic retrieval results in additional enhancements in derived aerosol properties because the back-scattering observations by lidar improve sensitivity to the columnar properties of aerosol, while radiometric observations provide sufficient constraints on aerosol amount and type that are generally missing in lidar signals. GARRLiC is based on the AERONET algorithm, improved to invert combined observations by radiometer and multi-wavelength elastic lidar observations. The algorithm is set to derive not only the vertical profile of total aerosol concentration but it also differentiates between the contributions of fine and coarse modes of aerosol. The detailed microphysical properties are assumed height independent and different for each mode and derived as a part of the retrieval. The GARRLiC inversion retrieves vertical distribution of both fine and coarse aerosol concentrations as well as the size distribution and complex refractive index for each mode. The potential and limitations of the method are demonstrated by the series of sensitivity tests. The effects of presence of lidar data and random noise on aerosol retrievals are studied. Limited sensitivity to the properties of the fine mode as well as dependence of retrieval accuracy on the aerosol optical thickness were found. The practical outcome of the approach is illustrated by applications of the algorithm to the real lidar and radiometer observations obtained over Minsk AERONET site.

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Airborne high spectral resolution lidar observation of pollution aerosol during EUCAARI-LONGREX

Cited by 23 publications

References 35 publications

Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data

Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data

Retrieval of ice-nucleating particle concentrations from lidar observations and comparison with UAV in situ measurements

Enhancement of aerosol characterization using synergy of lidar and sun-photometer coincident observations: the GARRLiC algorithm

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