| 光谱学与光谱分析 |
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| Study on the Measurement of the Atmospheric Extinction of Fog and Rain by Forward-Scattering Near Infrared Spectroscopy |
| WANG Mian,LIU Wen-qing,LU Yi-huai,ZHAO Xue-song,SONG Bing-chao,ZHANG Yu-jun,WANG Ya-ping,LIAN Cui-hua,CHEN Jun,CHENG Yin,LIU Jian-guo,WEI Qing-nong |
| Key Laboratory of Environmental Optics & Technology，Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China |
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Abstract In the visible and near IR，absorption is negligible so that the atmospheric extinction can be derived by atmospheric scattering which is mainly contributed by fog droplet, rain droplet, another types of droplet and small articles.The forward-scattering visibility meter (FVM) works by illuminating with near IR light a small sample volume of about 100 mL of air and measuring the intensity scattered in the angular range of 30° to 36° degrees.The scattered intensity is proportional to the extinction coefficient regardless of the article size distribution and after wavelength calibration.The ratio of scattered signal to extinction coefficient of fog and haze can be achieved by comparative test of FVM outputs and manual observations.Nevertheless, as a result of the application of the measurement during rain with the ratio of fog and haze, an unacceptable error is raised.To obtain an accuracy extinction measurement during rain, an appropriated ratio of scattered signal to extinction coefficient of rain would be found.The calculation for different size distributions of fog and rain with Mie theory has been made in this paper.And a comparison of extinction measurements made with two FVMs and manual observations during fog and rain has been made.The result shows that during rain the FVM extinction coefficient is from 20% to 60% greater than that of manual observations.This result can be used to define correction factors so that the FVM using forward-scattering near IR spectroscopy not only can be used to estimate extinction during fog and haze as well as during rain.
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Received: 2007-05-09
Accepted: 2007-08-26
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Corresponding Authors:
WANG Mian
E-mail: mwang@aiofm.ac.cn
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[1] Vogt H.Journal of the Atmospheric Sciences, 1968, 25(5)：912.
[2] David C Burnham, Fifth Symposium on Meteorological Observations and Instrumentations, Ioronto, Ont., Canada, April 11-15, 1983. 102.
[3] HONG Guang-lie, ZHANG Yin-chao, ZHOU Meng-ran, et al(洪光烈，张寅超，周孟然，等).Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(7)：1249.
[4] Winstanley J V，Adams M J.Applied Optics, 1975, 14(9)：2151.
[5] Deirmendjian D.The Scattering of Light and Other Electromagnetic Radiation.M Kerker Ed., New York：Academic Press, 1969.171.
[6] ZHOU Xiu-ji, TAO Shan-chang, YAO Ke-ya(周秀骥，陶善昌，姚克亚).Advanced Atmospheric Physics(高等大气物理学).Beijing：Meteorological Press(北京：气象出版社), 1991.906.
[7] MeCartney Earl J.Optics of the Atmosphere Scattering by Molecules and Particles.New York：John Wiley & Sons, Inc.1976.29, 275.
[8] Vermote E，et al.6S User Guide Version 2, NASA-Goddard Space Flight Cent., Greenbelt, MD., 1997.
[9] YIN Hong(尹 宏).Atmospheric Radiation Foundation(大气辐射学基础).Beijing：Meteorological Press(北京：气象出版社), 1993.58.
[10] ZHANG Cheng-chang，ZHOU Wen-xian(章澄昌，周文贤).Atmospheric Aerosol Tutorials(大气气溶胶教程).Beijing：Meteorological Press(北京：气象出版社), 1995.21.
[11] Yura H T, Barthel K G, Büchtemann W.J.Opt.Soc.Am., 1983, 73(11)：1574.
[12] Josef Podzimek.Studia Geophysica et Geodaetica, 1997, 41(3)：277.
[13] Marshall J S, Palmer W McK.J.Meteorology, 1948, 5：165.
[14] Ranmaswamy V，Chylek P.Shape of Raindrops, Light Scattering by Irregularly Shaped Particles, Schuerman D W Ed., New York：Plenum Press, 1979. |
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