An automated dust detection using SEVIRI: A multiyear climatology of summertime dustiness in the central and western Sahara

Abstract

Here we present an automated dust detection scheme using the Infrared (IR) channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI), carried on board Meteosat Second Generation (MSG) satellites, from which dust scheme images that are now widely used in Saharan dust studies are created. This provides an objective, readily reproducible and quick way to build up climatologies of dust presence which compares well with subjectively identified dust presence in the daytime hours. At nighttime the automated detection scheme is less reliable due to the strong diurnal cycle of surface temperatures. Our SEVIRI Dust Flag (SDF) is compared to Aerosol Optical Depth (AOD) from the surface and found to successfully and consistently identify moderate‐heavy dust outbreaks, although success rate is lower in the early morning and late evening. SDF corresponds to Absorbing Aerosol Index (AAI) from the Ozone Monitoring Instrument (OMI) that is also indicative of moderate‐heavy dust outbreaks across the central and western Sahara, but there are differences in the spatial patterns of climatologies created over a number of years that are likely to be due to the different sensitivities of the detection schemes. Despite these discrepancies, SDF and AAI both place dust hot spots in southern Algeria and across its southern borders with Mali and Niger, and SDF climatologies for June–August 2004–2010 reveal that there is a substantial degree of interannual variability in dust presence in the central and western Sahara in the boreal summer.

Optical properties of Saharan dust aerosol and contribution from the coarse mode as measured during the Fennec 2011 aircraft campaign

“…This may have a significant impact on satellite retrievals over the desert, such as the SEVIRI-RGB thermal infrared dust product , which has been used to infer dust source regions (Schepanski et al, 2007;Ashpole and Washington, 2012) and can be used as preferential dust source regions in models Heinold et al, 2011) and for dust model evaluation . The abundance of large particles may have implications for other satellite retrievals over desert surfaces which make assumptions on dust size and/or optical properties.…”

New developments in the representation of Saharan dust sources in the aerosol–climate model ECHAM6-HAM2

“…Afternoon to nighttime dust events mostly occur under clouds and thus cannot be detected, while dust emissions between morning and noon tend to occur under clear-sky conditions . Further uncertainties in the MSG observations exist due to the sensitivity to atmospheric water vapor, the altitude of the dust layer, and the low contrast in the infrared signal between desert surface and dust at night (Ashpole and Washington, 2012;Brindley et al, 2012). At least for summer, ground-based observations in the central Sahara Marsham et al, 2013) and convection-permitting simulations for West Africa show a much larger contribution (30-50 %) by convective cold pools in the late afternoon and evening.…”

An Enhanced Satellite-Based Algorithm for Detecting and Tracking Dust Outbreaks by Means of SEVIRI Data

“…Despite the advantages offered by these algorithms, as well as by other novel techniques (e.g., [51,52]) also combining information provided by different satellite sensors (e.g., [53]), an effective identification and mapping of airborne dust, regardless of background surfaces and of atmospheric/observational conditions, still represents a challenge for scientists. In particular, some critical scenarios remaining challenging for most of the algorithms analysing VIS/IR radiances developed so far include: airborne dust identification over bright surfaces (e.g., desert regions); the dependence of IR signals on dust plume features (e.g., plume height [54]); the sensitivity of BTD to variability in surface emissivity [48]; the impact of cirrus clouds on the BTD signal [48,55].…”

“…Recent dust detection methods have analysed SEVIRI imagery at two different time slots per day [47], employed a cloud screened BTD image [48], used dynamic reference brightness temperature differences [49], or integrated several fixed threshold tests on BTD and BTR (Brightness Temperature Ratio) signals [50]. Despite the advantages offered by these algorithms, as well as by other novel techniques (e.g., [51,52]) also combining information provided by different satellite sensors (e.g., [53]), an effective identification and mapping of airborne dust, regardless of background surfaces and of atmospheric/observational conditions, still represents a challenge for scientists.…”