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Proceedings Paper

Prediction of aerosol distributions parameters according to weather forecast: status report
Author(s): Itai Dror; Norman S. Kopeika
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Paper Abstract

Prediction of wave propagation parameters through an aerosol medium such as scattering and absorption coefficients and scattering phase function is possible if the particulate size distribution is known. In this paper an effort is made to relate the desert particulate size distribution parameters to simple meteorological parameters. All of the size distribution curves showed clear `knees' in their characteristic indicating that the source of the particles is more than one. The particulate size distributions were well fitted to a Trimodal log normal distribution. This good agreement suggests that the particulate size distribution is composed from three separate sources. The first and second sources are due to the local particles which exist in the location of the measurement, contributing to the smaller radii of the size distribution. The third source is large particles which are transferred from large deserts like the Sahara desert by dust carrying winds. These particles contribute to the larger radii end of the distribution. The size distribution parameters were related to meteorological parameters. The most dominant parameter was relative humidity. Using this model it should be possible to predict the particulate size distribution from meteorological data. Prediction of particulate size distribution allows the prediction of aerosol Modulation Transfer Function which is crucial in the prediction of image quality propagating through aerosols.

Paper Details

Date Published: 1 August 1992
PDF: 9 pages
Proc. SPIE 1688, Atmospheric Propagation and Remote Sensing, (1 August 1992); doi: 10.1117/12.137878
Show Author Affiliations
Itai Dror, Ben-Gurion Univ. of the Negev (Israel)
Norman S. Kopeika, Ben-Gurion Univ. of the Negev (Israel)

Published in SPIE Proceedings Vol. 1688:
Atmospheric Propagation and Remote Sensing
Anton Kohnle; Walter B. Miller, Editor(s)

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