Local ice formation via liquid water growth in slowly ascending humid aerosol/liquid water layers observed with ground‐based lidars and radiosondes

Wu, Cheng; Yi, Fan

doi:10.1002/2016jd025765

Cited by 12 publications

(10 citation statements)

References 57 publications

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“…In addition, observations of cloud evolution by ground‐based lidar have also been shown by Ansmann et al. (2008) and Wu and Yi (2017).…”

Section: Observation Resultsmentioning

confidence: 56%

“…According to the parameters of outgoing beam divergence (0.75 mrad) and receiving the FOV (1 mrad) of the polarization lidar system, liquid water clouds should have a similar upper limit for δ (0.21 or 0.25) (Ansmann et al., 2009; Naud et al., 2010), which is suggested as the threshold value for penetrable cloud to identify whether the backscatter is caused by nonspherical shapes or multiple‐scattering effects. Therefore, the discrimination criteria in terms of the volume depolarization ratio are δ < 0.03 for water droplets and δ > 0.3 for ice crystals (Ansmann et al., 2008; Wu & Yi, 2017).…”

Section: Instrumentation and Methodologymentioning

confidence: 99%

“…δ characterizes the nonspherical shape of backscattering targets, which can effectively distinguish dust aerosols and ice crystals from liquid water and other types of aerosols (Sassen, 2005). Multiple‐scattering‐induced depolarization ratio profiles have been calculated for liquid water clouds based on the Monte Carlo model (Sun & Li, 1989; Ramella‐Roman et al., 2005; Donovan et al., 2015) by Wu and Yi (2017). According to the parameters of outgoing beam divergence (0.75 mrad) and receiving the FOV (1 mrad) of the polarization lidar system, liquid water clouds should have a similar upper limit for δ (0.21 or 0.25) (Ansmann et al., 2009; Naud et al., 2010), which is suggested as the threshold value for penetrable cloud to identify whether the backscatter is caused by nonspherical shapes or multiple‐scattering effects.…”

Section: Instrumentation and Methodologymentioning

confidence: 99%

“…A detailed description of the lidar system and a performance verification through a comparison with simultaneous and nearly collocated CALIPSO measurements were presented by Zhang et al (2014), Kong and Yi (2015), and Zhuang and Yi (2016). Over the years, this lidar system has been widely used in observational studies of clouds and aerosols, including the evolution of convective boundary layers (Kong & Yi, 2015), aerosol layers in the free troposphere (Shao et al, 2020), transported dust aerosols (He & Yi, 2015), volcanic aerosol event (Zhuang & Yi, 2016), ice cloud formation in the local atmosphere (Wu & Yi, 2017), and falling ice virga (Cheng & Yi, 2020). The emitted laser is a linearly polarized light at 532 nm with a pulse energy of ∼120 mJ, a pulse width of 6 ns, and a repetition rate of 20 Hz.…”

Section: Zenith-pointed Polarization Lidarmentioning

confidence: 99%

“…In other words, cloud evolution can almost be modeled as a synchronous process of both Lagrangian and Eulerian models (Andrejczuk et al, 2010). In addition, observations of cloud evolution by groundbased lidar have also been shown by Ansmann et al (2008) and Wu and Yi (2017).…”

Section: (A) (B) (C) (D)mentioning

confidence: 99%

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Heterogeneous Nucleation of Midlevel Cloud Layer Influenced by Transported Asian Dust Over Wuhan (30.5°N, 114.4°E), China

Yang

et al. 2021

JGR Atmospheres

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Mixed-phase clouds, which contain both supercooled liquid water droplets and ice crystals, cover >34% of the Earth's surface (Zhang et al., 2018) and significantly impact the atmospheric radiation budget: they cool the Earth by reflecting incoming solar radiation and warm the Earth by prohibiting the escape of outgoing longwave radiation (IPCC, 2013; Rosenfeld et al., 2014; Zhao et al., 2018). Mixed-phase clouds are also essential to cloud electrification and the production of precipitation (Liu et al., 2019; Rosenfeld et al., 2008). Ice formation in the atmosphere is primarily the result of competition between the activation energy and interface energy for water molecules (DeMott, 2002; Seifert, 2011). To generate ice particles, the energy barrier needs to be broken by increasing the interface energy, which depends on temperature and the logarithm of supersaturation. This breakage can be realized either by homogeneous nucleation occurring at temperatures below −38°C (Pruppacher, 1995) or by heterogeneous nucleation at warmer temperatures ranging from −38°C to 0°C with some insoluble aerosols acting as ice-nucleating particles (INPs). For mixed-phase clouds, ice crystals are produced by heterogeneous nucleation process via four basic ice nucleation modes, e.g., deposition nucleation, immersion nucleation, contact nucleation, and condensation nucleation (Pruppacher & Klett, 1997). However, the heterogeneous nucleation process is still poorly understood due to the complexity of the INP nucleation efficiency corresponding to its chemical and microphysical properties (Cantrell & Heymsfield, 2005; Kanji et al., 2017). Moreover, current assessments suggest that the net radiation forcing related to the interaction between aerosols and mixed-phase clouds shows a large uncertainty and even exhibits a contradictory variation from −0.67 to +0.70 W m −2 (Fan et al., 2016; IPCC, 2013). Dust, soot, and biological particles are considered the three most effective INPs in the atmosphere. The yearly averaged emission of dust aerosols is estimated to be 1,000-3,000 Tg, which is far more than that of either soot (∼50 Tg) or organic material (∼60 Tg) (Seifert, 2011), and thus provides a promising opportunity to study heterogeneous nucleation in an actual atmospheric environment. Numerous studies concerning dust-related heterogeneous ice formation have involved laboratory experiments (

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“…In addition, observations of cloud evolution by ground‐based lidar have also been shown by Ansmann et al. (2008) and Wu and Yi (2017).…”

Section: Observation Resultsmentioning

confidence: 56%