A new high‐resolution central and western Saharan summertime dust source map from automated satellite dust plume tracking

Abstract

In this paper, we outline a new objective dust source detection method for the central and western Sahara (CWS), based on the automated tracking of individual dust plumes in data from the Spinning Enhanced Visible and Infrared Imager, available every 15 mins. at ~0.03° spatial resolution. The method is used to map the origin of summertime dust storms in the CWS for June – August 2004 – 2010. It reveals the sources of these events in unprecedented detail, allowing for the identification of specific, highly active source areas. The study of collocated surface features reveals that many of the dominant sources are likely associated with paleolakes and outwash plains, many in close proximity to the Saharan mountains. Extensive nonsource areas are associated with low albedo and elevated terrain, pointing to the mountainous regions of the Sahara. Additionally, sand seas are not identified as important source areas, but their margins sometimes are. The automated tracking method also facilitates analysis of the transport direction of dust plumes from key source regions and the inference of emission mechanisms. It is found that there are two broad domains within the CWS: one in southwest Algeria and northwest Mali, characterized primarily by transport toward the southwest and very likely dominated by low‐level jets embedded in the northeasterly Harmattan winds; and a second in southern Algeria, northwest Niger, and northeast Mali where there is no preferred transport direction and a strong potential association between dust events and deep convection, pointing toward cold pool outflows as the likely deflation mechanism.

How important are atmospheric depressions and mobile cyclones for emitting mineral dust aerosol in North Africa?

“…7). During spring and summer, the western coast may be influenced by moist air advected from the Atlantic, which can prevent dust detection in the satellite product (Brindley et al, 2012), although the visual identification by Schepanski et al (2012) might be less influenced by moisture than an automatic algorithm (Ashpole and Washington, 2013). Also the tendency to larger DSAF over the north compared to the south in the model simulation is not in agreement with the satellite DSAF.…”

Quantifying the effect of geomorphology on aeolian dust emission potential in northern China

“…Given the increasingly important role of human impact on soil erodibility, several studies have used satellite-based dust indicators allied with land cover maps to attribute dust emission to natural or anthropogenic sources (Ginoux et al, 2012;Lee et al, 2012;Parajuli et al, 2014). Baddock et al, 2009;Brindley et al, 2012;Ashpole and Washington, 2013;Parajuli and Zender, 2017). While satellite remote sensing is instrumental in identifying the spatial distribution of dust sources, there remain some uncertainties and inaccuracies related to the dust detection algorithms, overpass time, cloud effects and image/signal interpretation (e.g.…”

Identification and Characterization of Dust Source Regions Across North Africa and the Middle East Using MISR Satellite Observations

“…Aerosol loading-based dust source identification approaches derived from satellite AOD products such as the Deep Blue algorithm applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data from the polar-orbiting Terra and Aqua satellites (Sayer et al, 2013) or AI product from the Ozone Monitoring Instrument aboard the polar-orbiting Aura satellite (Torres et al, 2013) are currently widely used to constrain model dust emissions when combined with additional geomorphological factors (Ginoux et al, 2012;Prospero et al, 2002). BT-difference based dust source identification from 15-min resolution data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument aboard the geostationary Meteosat Second Generation satellite provides back-tracking of individual dust plumes through the Dust Flag method, either visually or automatically (Ashpole & Washington, 2013), and has been widely used in temporally and spatially resolved assessments of Saharan dust sources (Evan, Fiedler, et al, 2015;Schepanski et al, 2009Schepanski et al, , 2015Schepanski et al, , 2016.…”