Regional and Mesoscale Meteorology Branch

The purpose of the Volcanic Ash Enhancement imagery product is 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 certain kinds of ash in the scene. While there may be other properties to volcanic ash plumes (such as high SO2 content, detectable via other spectral bands of GOES-R) the current enhancement keys in on ash plumes with a high silicate-based composition. These plumes pose a particular hazard to turbine jet engines as they can both damage and accrue material on the blades, compromising the aerodynamics of the engine system and leading to flame-outs. While the enhancement does not provide a quantitative approximation of how much ash is present, its height in the atmosphere, or its specific size properties (parameters of high importance for model predictions of how the ash plume will vary/transport), it does provide a rapid-assessment flag for the most hazardous (to aviation) variety of plume and a quick validation against other detection techniques.

The Volcanic Ash 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 Volcanic Ash Enhancement described here, we can only demonstrate now it via sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, which fly on polar-orbiting NASA Terra and Aqua satellites. The polar orbits limit the temporal frequency of coverage to about two passes (mid-morning and mid-afternoon) per day, recalling that this is a daytime-only product. The following provides a brief summary of the logic that goes into enhancing volcanic ash plumes from GOES-R “ABI-like” measurements.

In the most general sense, enhancing volcanic ash plumes in satellite imagery requires the isolation of the ash signal (in terms of measurable properties) in order to separate it in color-space from other components of the scene. The science literature presents many ways to do this. As the chemical properties of ash are very diverse, it is often the case that one approach will work better than another for a given volcano (and sometimes ash properties of a given volcano will vary over time, depending on the nature of the eruption). 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: similar to our discussion of mineral dust enhancements, to some extent we can distinguish volcanic ash from meteorological clouds with our own eyes through color differences (some ash clouds 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 volcanic ash 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 volcanic ash detection. This approach works well over dark surface backgrounds, like water. In cases where the background color leads to ambiguity in discerning the ash cloud/plume, we need to appeal to other discriminators, and these are described below.
2. Temperature: When hot ash is lofted into the atmosphere, its temperature rapidly adjusts to whatever the air temperature of the environment is. The ash layer will have its own radiative influence on the environmental temperature (via absorption of upwelling thermal radiation from below, absorption/reflection of incoming solar radiation from above, and the heat of the volcanic gasses themselves) but these effects are of secondary importance here, so we will ignore them. Since temperatures in the lower atmosphere generally decrease with height (especially during the daytime hours), the ash layer cools as it rises, and soon produces a “thermal contrast” against the warmer surface. A satellite sensor that is sensitive to heat (infrared radiation) can therefore assist us in detecting the lofted ash based on the temperature contrast it produces against the warmer background surface. If we combine this temperature contrast information with the color distinction information described above, now we have a more robust way of distinguishing between meteorological clouds (which are gray/white and cool) and ash plumes (which are earth-tones and cool) than either test can provide on its own.
3. Spectral Differences in Transparency: Similar again to mineral dust detection, the chemical and physical properties of some volcanic ash clouds (particularly those with silicate-based particles like microscopic obsidian glass) 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 volcanic ash plumes appearing “thinner” (more transparent) or “thicker” (more opaque). As a result, measurements of a mid-level volcanic ash plume in a part of the infrared spectrum that corresponds to the more absorbing behavior may appear cooler than measurements of that same ash plume in a part of the infrared spectrum where it appears more transparent. In the case of more absorbing behavior, we’re seeing more of the cool emissions of the ash 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 ash. Computing a difference between two such measurements, chosen carefully, provides an effective way to identify ash. The two channels used here are the 11 and 12 micrometer thermal infrared bands, which provide an ash detection signal that is opposite in sign to most meteorological clouds.

In many ways the current algorithm is similar to the Blowing Dust (Blue Light Absorption) product. An important distinction here is in the handling of high-altitude signals. In the Blowing Dust product, it is assumed that upper-tropospheric signals are the result of semi-transparent cirrus clouds which are in fact false-alarms. The filtering is accomplished by examining the 1.38 µm shortwave water-vapor band reflectance. In the current volcanic ash product, we must allow for the possibility that explosive eruptions may loft ash into the upper troposphere or even the lower stratosphere. As such, the 1.38 µm logic is modified to allow for high reflectance to first be tested against coloration prior to filtering it out. If the reflectance exceeds a set of thresholds then it is retained as an enhanced pixel, whereas the Blowing Dust product would suppress this pixel.

While any one of the above discriminators alone may not be sufficient to fully distinguish volcanic ash, we can often provide a reasonable isolation 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 ash. 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 ash signal) is communicated in the enhanced imagery in terms of brightness of the enhanced ash features…with brighter regions being higher confidence.