Method to retrieve cloud condensation nuclei number concentrations using lidar measurements

Tan, Wangshu; Zhao, Gang; Yu, Yingli; Li, Chengcai; Li, Jian; Kang, Ling; Zhu, Tong; Zhao, Chunsheng

doi:10.5194/amt-12-3825-2019

Cited by 14 publications

(13 citation statements)

References 54 publications

(60 reference statements)

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“…If only data with an FMF above 0.6 are considered (blue dots in Figure ), 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 (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: Resultsmentioning

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: Resultsmentioning

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 relationships between particle extinction coefficients and number concentrations of particles with a dry radius larger than 50 nm (for non-dust) and 100 nm (for dust) were parameterized based on multiyear AERONET observations for different aerosol types. However, the measurements from the single wavelength lidar also lack sufficient information to quantify particle size distribution, particle number concentration or aerosol type, resulting in large uncertainty in NCCN retrieval (Burton et al, 2012;Tan et al, 2019). However, few recent studies (Lv et al, 2018;Tan et al, 2019) have shown efforts to retrieve NCCN based on the advanced capability of multiwavelength lidar measurements, but they have been limited to ground-based observations only.…”

Section: Introductionmentioning

confidence: 99%

“…However, the measurements from the single wavelength lidar also lack sufficient information to quantify particle size distribution, particle number concentration or aerosol type, resulting in large uncertainty in NCCN retrieval (Burton et al, 2012;Tan et al, 2019). However, few recent studies (Lv et al, 2018;Tan et al, 2019) have shown efforts to retrieve NCCN based on the advanced capability of multiwavelength lidar measurements, but they have been limited to ground-based observations only. Rosenfeld et al, (2016) have attempted a new approach to retrieve satellite based NCCN using passive satellite observations.…”

Section: Introductionmentioning

confidence: 99%

Resolving Vertical Profile of Cloud Condensation Nuclei Concentrations from Spaceborne Lidar Measurements

Patel

Jiang²,

Gautam³

et al. 2022

Preprint

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Abstract. Cloud condensation nuclei (CCN) are mediators of aerosol-cloud interactions (ACI), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote sensing-based algorithm that quantifies the vertically-resolved CCN number concentrations (NCCN) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the look-up tables developed in this study, based on the observations from the Aerosol Robotic Network to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors to lidar measurements. The retrieved optically equivalent particle size distributions and aerosol type dependent particle composition are utilized to calculate critical diameter using the κ-Köhler theory and NCCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in NCCN. The potential of this algorithm is further evaluated by retrieving NCCN using airborne lidar from the NASA ORACLES campaign and validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the NCCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar - Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) - measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which help to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.

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“…In particular, the optical and microphysical properties of water clouds affect their capability to absorb and scatter radiation, and this poses a major challenge leading to uncertainty in numerical weather forecasts and climate simulations [2,3]. Testing and improving parameterization schemes for cloud variations and physical processes requires knowledge of cloud properties [4,5]. Hence, the current techniques will be validated and new ones will be developed for retrieving water properties.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data

Zhang

Tan

et al. 2021

Remote Sensing

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The remote sensing of water clouds is useful for studying their spatial and temporal variations and constraining physical processes in climate and weather prediction models. However, radar-only detection provides inadequate information for the cloud droplet size distribution. Here, we propose a novel lookup-table method, which combines lidar (1064, 532 nm) and radar (8.6 mm) to retrieve profiles of cloud optical (backscatter coefficient and extinction coefficient) and microphysical properties (effective diameter and liquid water content). Through the iteration of the extinction-to-backscatter ratio, more continuous cloud optical characteristics can be obtained. Sensitivity analysis shows that a 10% error of the lidar constant will lead to a retrieval error of up to 30%. The algorithm performed precise capture of the ideal cloud signal at a specific height and at full height and the maximum relative error of the backscatter coefficients at 1064 nm and 532 nm were 6% and 4%, respectively. With the application of the algorithm in the two observation cases on single or multiple cloud layers, the results indicate that the microphysical properties mostly agree with the empirical radar measurements but are slightly different when larger particles cause signal changes of different extents. Consequently, the synergetic algorithm is capable of computing the cloud droplet size distribution. It provides continuous profiles of cloud optical properties and captures cloud microphysical properties well for water cloud studies.

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Method to retrieve cloud condensation nuclei number concentrations using lidar measurements

Cited by 14 publications

References 54 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

Resolving Vertical Profile of Cloud Condensation Nuclei Concentrations from Spaceborne Lidar Measurements

Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data

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