Satellite detection of dust using the IR imagery of Meteosat: 1. Infrared difference dust index
Article first published online: 21 SEP 2012
Copyright 2001 by the American Geophysical Union.
Journal of Geophysical Research: Atmospheres (1984–2012)
Volume 106, Issue D16, pages 18251–18274, 27 August 2001
How to Cite
2001), Satellite detection of dust using the IR imagery of Meteosat: 1. Infrared difference dust index, J. Geophys. Res., 106(D16), 18251–18274, doi:10.1029/2000JD900749., , and (
- Issue published online: 21 SEP 2012
- Article first published online: 21 SEP 2012
- Manuscript Accepted: 14 NOV 2000
- Manuscript Received: 27 JAN 2000
The Infrared Difference Dust Index (IDDI) is a satellite dust product designed for climatological applications, designed specifically for dust remote sensing in arid regions such as the Sahel and Sahara. It is based on the atmospheric response to dust, extracted from midday Meteosat-IR imagery, and takes advantage of the impact of dust aerosols on the thermal infra-red radiance outgoing to space. Simulations show a quasi-linear relationship between satellite response to dust and shortwave optical depth, with a sensitivity depending on particle size distribution and radiative surface properties. Comparison of measured satellite response with photometric optical depth agrees with the simulations. Water vapor significantly affects the satellite signal for cases of large columnar amounts and oceanic air masses advected inland. Hence apart from possible coastal effects, the water vapor effect can be neglected in the Sahelian-Saharan zone north of the Intertropical Convergence Zone, coinciding with the major regions of African dust emission and transport. The construction of the IDDI involves the processing of reference images, theoretically representing the outgoing radiance obtaining under clear-sky conditions. Errors may arise from (1) dust remaining in the reference images and (2) seasonal shifts of the reference level; however, the latter error will be offset by averaging used in climatological processing. An error budget is presented for the station of Gao. A statistical comparison of IDDI data with visibility measured at synoptic stations results in (1) a validation of the product, and (2) a climatologically relevant visibility-IDDI relation, valid for the arid regions of northern Africa. The latter relation is consistent with both simulations and photometric measurements. IDDI maps over Africa compare successfully with optical depth over adjacent ocean regions derived from Meteosat-VIS imagery. The observed continuity of dust plumes across the African coast demonstrates the consistency between both products.