A Study of Supersaturation Spectra of Deposition Ice Nuclei

K. Hussain; A. Shaukat

doi:10.1007/BF02658020

Impact Factor: 5.8

Volume 6 Issue 2

Apr. 1989

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 1989 > 6(2): 247-253

A Study of Supersaturation Spectra of Deposition Ice Nuclei

1.
Centre for Solid State Physics, Punjab University New Campus, Lahore-20 Pakistan,Physics Department, Punjab University New Campus, Lahore-20 Pakistan

doi: 10.1007/BF02658020

Abstract
References
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Abstract:
Ice nuclei-supersaturation spectra in the form of a Power Law ( Ni=RSir; B and y are empirical constants) have been expressed since 1973 when the curve was first introduced independentally by Gagin and Huffman. Experiments performed with a thermal gradient diffusion chamber in order to investigate the validity of the power curve. The results show that a linear curve fit to the data is as good as the power curve. The linear curve has the coefficients of correlation between 0.75 and 0.93 whereas the power curve fit to the same data has the coefficients between 0.82 and 0.93. The data reported by other workers, Zamurs and Jiusto and Zamurs et al., exhibit the same trend.
References

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Manuscript History

Manuscript received: 10 April 1989

Manuscript revised: 10 April 1989

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

A Study of Supersaturation Spectra of Deposition Ice Nuclei

1. Centre for Solid State Physics, Punjab University New Campus, Lahore-20 Pakistan,Physics Department, Punjab University New Campus, Lahore-20 Pakistan

Manuscript received: 1989-04-10
Manuscript revised: 1989-04-10

Abstract: Ice nuclei-supersaturation spectra in the form of a Power Law ( Ni=RSir; B and y are empirical constants) have been expressed since 1973 when the curve was first introduced independentally by Gagin and Huffman. Experiments performed with a thermal gradient diffusion chamber in order to investigate the validity of the power curve. The results show that a linear curve fit to the data is as good as the power curve. The linear curve has the coefficients of correlation between 0.75 and 0.93 whereas the power curve fit to the same data has the coefficients between 0.82 and 0.93. The data reported by other workers, Zamurs and Jiusto and Zamurs et al., exhibit the same trend.