Regional and Mesoscale Meteorology Branch

The purpose of the Blowing Dust Enhancement imagery product to provide a visually intuitive depiction that is useful to experts and non-experts alike. Instead of toggling between visible imagery, infrared imagery, and various spectral differences, the current product attempts to put several of these elements together and depict the consensus of where the tests have high confidence in the presence of dust in the scene.

The Blowing Dust Enhancement product demonstrates the kind of imagery that will be possible in the GOES-R era. GOES-R will feature the Advanced Baseline Imager (ABI) sensor whish will be able to produce versions of the imagery shown here at much higher time resolution.

Since we do not have all the required channels on present-day GOES satellites to do the Blowing Dust Enhancement, we can only demonstrate it now via the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments which fly on the polar-orbiting NASA Terra and Aqua satellites. This limits the temporal frequency of coverage to about two passes (mid-morning and mid-afternoon) per day.

Depicting lofted dust in enhanced satellite imagery requires that we isolate its signal (in terms of measurable properties) in order to separate it in color-space from other components of the scene. The science literature describes many ways to do this. As the chemical properties of lofted dust are very diverse, it is often the case that one approach will work better than another for a given region. The current approach combines several of the more reliable discriminators which take advantage of the spectral bands that will be available to GOES-R. The principal considerations for the enhancement are:

1. Color: to some extent we can distinguish dust from meteorological clouds with our own eyes through color differences (mineral dust clouds often have earth tones, owing to their strong absorption of blue light). Since most meteorological clouds appear gray/white, the color information provides a useful discriminator. This is why true color imagery can also be useful for dust detection. A satellite sensor that has the ability to distinguish color (e.g., via multiple narrow-band channels in the visible part of the spectrum) can combine this information in an analogous way to color vision for dust detection. This approach works well over dark surface backgrounds, like water, but becomes problematic over bright surfaces like deserts (and particularly, when the dust is over a surface background that has similar coloration to the dust itself). In this case, we need to appeal to other discriminators, and these are described below.
2. Temperature: When dust is lofted into the atmosphere, its temperature rapidly adjusts to whatever the air temperature of the environment is. The dust layer will have a radiative influence on its environmental temperature (via absorption of upwelling thermal radiation from below, and reflection of incoming solar radiation from above) but these effects occur on longer time scales and are of secondary importance here, so we ignore them. Since temperatures in the lower atmosphere generally decrease with height (especially during the daytime hours), the dust layer cools as it rises, and soon produces a nice “thermal contrast” against the warmer surface. A satellite sensor that is sensitive to heat (infrared radiation) can therefore assist us in detecting an elevated dust plume based on the temperature contrast it produces against the warm background surface. If we combine this temperature contrast information with the color distinction information, now we have a more robust way of distinguishing between meteorological clouds (which are gray/white and cool) and dust plumes (which are earth-tones and cool) over both water and land surfaces than either test can provide on its own.
3. Spectral Differences in Transparency: The chemical and physical properties of mineral dust layers give rise to different optical properties (scattering and absorption behavior) depending on what part of the infrared spectrum we view them in. In the same way that these properties result in preferred blue-light absorption in the visible part of the spectrum, differences occur in the middle and thermal infrared parts of the spectrum which gives rise to dust plumes appearing “thinner” (more transparent) or “thicker” (more opaque). As a result, measurements of a mid-level dust plume in a part of the infrared spectrum that corresponds to the more absorbing behavior may appear cooler than measurements of that same dust plume in a part of the infrared spectrum where dust appears more transparent. In the case of more absorbing behavior, we’re seeing more of the cool emissions of the dust layer’s environmental temperature, while in the case of more transparent behavior we’re seeing warmer contributions from the underlying surface that are able to transmit through the dust. Computing a difference between two such measurements, chosen carefully, provides an effective way to identify dust. The two channels used here are the 11 and 12 micrometer thermal infrared bands, which provide a dust detection signal that is opposite in sign to most meteorological clouds.

While any one of the above discriminators alone may not be sufficient to fully distinguish elevated dust, we can often provide a reasonable isolation of dust from other components of the scene through combining the various tests. When all tests are satisfied simultaneously (i.e., an intersection) we can say with greater confidence that the pixel being examined contains dust. It is important to point out that in the current algorithm these tests are not strictly “yes/no” but are cast as confidence factors that range between values of 0 (low confidence) to 1 (high confidence). The extent to which the tests are satisfied (strength of the dust signal) is communicated in the enhanced imagery in terms of brightness of the enhanced dust features…with brighter regions being higher confidence.