A new method for calculating number concentrations of cloud condensation nuclei based on measurements of a three-wavelength humidified nephelometer system

Tao, Jiangchuan; Zhao, Chunsheng; Kuang, Ye; Zhao, Gang; Shen, Chuanyang; Yu, Yingli; Bian, Yuxuan; Xu, Wanyun

doi:10.5194/amt-11-895-2018

Cited by 8 publications

(7 citation statements)

References 54 publications

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“…CCN number concentrations (N CCN ) have been measured at different locations worldwide (e.g., Twomey, 1959;Hudson, 1993;Kulmala et al, 1993;Hämeri et al, 2001;Sihto et al, 2011;Pöhlker et al, 2016;Ma et al, 2014). However, the accessible data, especially for long-term measurements, are still limited in the past and today due to the relatively high cost of instrumentation and the complexity of long-term operating.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies have also involved intensive aerosol optical properties, such as the scattering Ångström exponent (SAE), hemispheric backscattering fraction (BSF), and single-scattering albedo (SSA) to build up a bridge between the N CCN and AOPs. Jefferson (2010) used BSF and SSA to parameterize the coefficients C and k in the relation N CCN (SS) = C× (SS) k , where SS is the supersaturation percent (Twomey, 1959) and the exponent k is a function of SSA, which means it depends on both the scattering and absorption coefficients. Liu and Li (2014) discussed how different aerosol properties affect the ratio of N CCN to σ sp , i.e., R CCN /σ sp based on in situ and remote-sensing data.…”

Section: Introductionmentioning

confidence: 99%

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Estimating cloud condensation nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization

et al. 2019

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Abstract. The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol–cloud interactions within warm clouds. Long-term CCN number concentration (NCCN) data are scarce; there are a lot more data on aerosol optical properties (AOPs). It is therefore valuable to derive parameterizations for estimating NCCN from AOP measurements. Such parameterizations have already been made, and in the present work a new parameterization is presented. The relationships between NCCN, AOPs, and size distributions were investigated based on in situ measurement data from six stations in very different environments around the world. The relationships were used for deriving a parameterization that depends on the scattering Ångström exponent (SAE), backscatter fraction (BSF), and total scattering coefficient (σsp) of PM10 particles. The analysis first showed that the dependence of NCCN on supersaturation (SS) can be described by a logarithmic fit in the range SS <1.1 %, without any theoretical reasoning. The relationship between NCCN and AOPs was parameterized as NCCN≈((286±46)SAE ln(SS/(0.093±0.006))(BSF − BSFmin) + (5.2±3.3))σsp, where BSFmin is the minimum BSF, in practice the 1st percentile of BSF data at a site to be analyzed. At the lowest supersaturations of each site (SS ≈0.1 %), the average bias, defined as the ratio of the AOP-derived and measured NCCN, varied from ∼0.7 to ∼1.9 at most sites except at a Himalayan site where the bias was >4. At SS >0.4 % the average bias ranged from ∼0.7 to ∼1.3 at most sites. For the marine-aerosol-dominated site Ascension Island the bias was higher, ∼1.4–1.9. In other words, at SS >0.4 % NCCN was estimated with an average uncertainty of approximately 30 % by using nephelometer data. The biases were mainly due to the biases in the parameterization related to the scattering Ångström exponent (SAE). The squared correlation coefficients between the AOP-derived and measured NCCN varied from ∼0.5 to ∼0.8. To study the physical explanation of the relationships between NCCN and AOPs, lognormal unimodal particle size distributions were generated and NCCN and AOPs were calculated. The simulation showed that the relationships of NCCN and AOPs are affected by the geometric mean diameter and width of the size distribution and the activation diameter. The relationships of NCCN and AOPs were similar to those of the observed ones.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating cloud condensation nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization

et al. 2019

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“…Feingold and Morley (2003) demonstrate that the extent of backscatter and extinction enhancements hints the ability of 5 particles to serve as CCN. Tao et al (2018) use in situ measured light scattering enhancement factors to predict NCCN at 0.07% supersaturation, and the result shows strong consistency with CCN counter.…”

mentioning

confidence: 81%

Method to retrieve cloud condensation nuclei number concentrations using lidar measurements

Tan¹,

Zhao²,

Yu³

et al. 2019

Preprint

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Abstract. Determination of cloud condensation nuclei (CCN) number concentrations at cloud base is important to constrain aerosol-cloud interactions. A new method to retrieve CCN number concentrations using backscatter and extinction profiles from multiwavelength Raman lidars is proposed. The method implements hygroscopic enhancements of backscatter/extinction with relative humidity to derive dry backscatter/extinction and humidogram parameters. Humidogram parameters, Ångström exponents, and lidar extinction-to-backscatter ratios are then linked to the ratio of CCN number concentration to dry backscatter/extinction coefficient (ARξ). This linkage is established based on the datasets simulated by Mie theory and κ-Köhler theory with in situ measured particle size distributions and chemical compositions. CCN number concentration can thus be calculated with ARξ and dry backscatter/extinction. An independent theoretical simulated datasets is used to validate this new method and results show that the retrieved CCN number concentrations at supersaturations of 0.07 %, 0.10 %, and 0.20 % are in good agreement with theoretical calculated values. Sensitivity tests indicate that retrieval error in CCN arise mostly from uncertainties in extinction coefficients and RH profiles. The proposed method improves CCN retrieval from lidar measurements and has great potential in deriving scarce long-term CCN data at cloud base which benefits aerosol-cloud interaction studies.

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“…Many equations to parameterize enhancement factors have been proposed by previous studies (Titos et al, 2016). Two one-parameter equations are selected to test their performance on estimating ξ dry and representing particle hygroscopic growth characteristics.…”

Section: Derivation Of Humidogram Parameters Drymentioning

confidence: 99%

Method to retrieve cloud condensation nuclei number concentrations using lidar measurements

Tan

Zhao

et al. 2019

Atmos. Meas. Tech.

Self Cite

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Abstract. Determination of cloud condensation nuclei (CCN) number concentrations at cloud base is important to constrain aerosol–cloud interactions. A new method to retrieve CCN number concentrations using backscatter and extinction profiles from multiwavelength Raman lidars is proposed. The method implements hygroscopic enhancements of backscatter and extinction with relative humidity to derive dry backscatter and extinction and humidogram parameters. Humidogram parameters, Ångström exponents, and lidar extinction-to-backscatter ratios are then linked to the ratio of CCN number concentration to dry backscatter and extinction coefficient (ARξ). This linkage is established based on the datasets simulated by Mie theory and κ-Köhler theory with in-situ-measured particle size distributions and chemical compositions. CCN number concentration can thus be calculated with ARξ and dry backscatter and extinction. An independent theoretical simulated dataset is used to validate this new method and results show that the retrieved CCN number concentrations at supersaturations of 0.07 %, 0.10 %, and 0.20 % are in good agreement with theoretical calculated values. Sensitivity tests indicate that retrieval error in CCN arises mostly from uncertainties in extinction coefficients and RH profiles. The proposed method improves CCN retrieval from lidar measurements and has great potential in deriving scarce long-term CCN data at cloud base, which benefits aerosol–cloud interaction studies.

show abstract

A new method for calculating number concentrations of cloud condensation nuclei based on measurements of a three-wavelength humidified nephelometer system

Cited by 8 publications

References 54 publications

Estimating cloud condensation nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization

Estimating cloud condensation nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization

Method to retrieve cloud condensation nuclei number concentrations using lidar measurements

Method to retrieve cloud condensation nuclei number concentrations using lidar measurements

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