Method to retrieve 
cloud condensation nuclei number concentrations
using lidar
measurements

Tan, Wangshu; Zhao, Gang; Yu, Yingli; Li, C.; Li, Jian; Liu, Kang; Zhu, Tong; Zhao, Chunsheng

doi:10.5194/amt-2019-21

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…If only data with an FMF above 0.6 are considered (blue dots in Figure 2), a valid relationship can be found between δ p and the rebound fraction. It has been reported that the backscatter properties at 532 nm contain mainly information for large particles 36 (over 60% of information is contributed by particles with dry diameters greater than 400 nm, and over 80% is contributed by particles with dry diameters greater than 300 nm). Therefore, we examined only the relationship between δ p and the rebound fraction for 400 nm particles.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Potential of Polarization Lidar to Profile the Urban Aerosol Phase State during Haze Episodes

Tan

Liu

et al. 2019

Environ. Sci. Technol. Lett.

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The phase states of atmospheric aerosol particles affect their physical, chemical, and optical properties. Particles with different phase states exhibit different viscosities and various shapes that cause differences in their scattering polarization. In this study, a novel method for inferring the phase state of submicrometer particles using the particle linear depolarization ratio (δ p ) retrieved from polarization lidar is proposed. The values of δ p during several haze episodes showed good correlation with the in situ-measured rebound fraction and ambient relative humidity. Two case studies verify that polarization lidar has the potential to infer the phase state profiles of submicrometer particles and that the particle phase state in the upper boundary layer may differ from that near the ground during haze episodes.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Potential of Polarization Lidar to Profile the Urban Aerosol Phase State during Haze Episodes

Tan

Liu

et al. 2019

Environ. Sci. Technol. Lett.

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“…Cloud microphysical relevant parameters such as CCN and INP number concentrations can be also estimated by means of lidar observations (Andreae, 2009;Ghan et al, 2006;Mamouri & Ansmann, 2017& 2015Tan et al, 2019). To this end, these calculations are accompanied with large uncertainties linked to both lidar related retrieval errors and errors introduced by the methodologies themselves (Ansmann et al, 2019;Tan et al, 2019). In fact, uncertainties to the retrieved CCN number concentration amounts to 50-200 % and regarding the INP number concentrations up to a factor of 3 higher than CCN retrievals, has been determined (Ansmann et al, 2019).…”

Section: Microphysical Propertiesmentioning

confidence: 99%

Atmospheric profiling using the lidar technique

Filioglou¹

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Atmospheric aerosol particles absorb and scatter solar radiation, altering directly the radiation balance. Indirectly, these particles have a complex interplay in cloud formation, affecting cloud reflectivity and cloud lifetime. Apart from the climatic effects, atmospheric particles pose negative health effects and they reduce visibility with adverse effects in road traffic and aviation safety. To improve the understanding of the aerosol effect on climate four different studies have been conducted. The main instrument utilized to retrieve vertical profiles of the aerosols was a multi-wavelength Polly XT lidar. The hygroscopic effect of the aerosol particles in the retrieved optical properties which is relevant to cloud studies can be assessed using the water vapor capabilities of the lidar. Lidar water vapor retrieval requires initial calibration. An evaluation of the different lidar water vapor signal calibration techniques was performed to quantify the uncertainty in the retrieved water vapor profiles. Moreover, two measurement campaigns were held in Finland and the United Arab Emirates in order to characterize the properties of understudied aerosol types (pollen and Arabian dust). Lastly, the effectiveness of the different aerosol types to the formation of ice, water, or mixedphase clouds in the Arctic was determined using a synergy of a spaceborne lidar (CALIOP) and a cloud radar (CloudSat). The study on water vapor showed that accurate water vapor retrievals are subject to the calibration factor. Operational on-site radiosondes are the best option, but robust retrievals are possible using data from the nearest radiosonde site or modelled data. Satellite-derived water vapor profiles performed the poorest, yet they could serve as an option in the absence of better information. The analysis of the pollen observations showed that the classification of various pollen types is possible, although challenging. Characterization requires shape information from at minimum two linear particle depolarization wavelengths, as well as external information such as airmass backward trajectories to ensure that other non-spherical aerosol particles such as dust are not present over the measurement site. Regarding the Arabian dust optical properties, it was found that this aerosol type exhibits different optical properties, specifically concerning the lidar ratios, than the dust originating from the Saharan region. Consequently, the universal lidar ratio of 55 sr currently used in lidar-based applications may lead to biases for dust originating from the Arabian Peninsula. The Arctic study on aerosol-cloud interactions showed that higher aerosol load was associated with higher occurrence of mixed-phase clouds. On the contrary, moderate association was found with varying the aerosol type. Nevertheless, meteorology outweighed the aerosol load importance over less stable atmospheric conditions, for example, over open ocean.

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Method to retrieve cloud condensation nuclei number concentrations using lidar measurements

Cited by 2 publications

References 25 publications

Potential of Polarization Lidar to Profile the Urban Aerosol Phase State during Haze Episodes

Potential of Polarization Lidar to Profile the Urban Aerosol Phase State during Haze Episodes

Atmospheric profiling using the lidar technique

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