Cloud-top features over MSC seen through Dust RGB product

Filter by


EUMETSAT Users Twitter

RSS Feed

RSS Icon Image Library

New features on top of the convective clouds visible with GOES-16 ABI Dust RGB product.

Date & Time
10 May 2018 20:20 UTC–11 May 07:57 UTC
Dust RGB, Visible Channel, Infrared Channel

By Ivan Smiljanic (SCISYS)

Although the Dust RGB product falls in the standard suite of most-used MSG SEVIRI RGB products, it is only now, applied to ABI instrument data, that this product is showing the power to resolve some of the cloud-top features associated with the most severe storms, overshooting tops, plumes etc. (Figure 1).

Figure 1: GOES-16 Dust RGB animation, 10 May 20:20 UTC–11 May 07:57 UTC. Download animation (MP4, 3MB)

The reason for the product's 'advanced behaviour' can be found in the positive superposition of different factors: advanced spatial resolution (ABI vs SEVIRI instrument), shape of so-called Spectral Response Function (SRF), i.e. the characteristics of used infrared channels (basically central wavelength and the channel width), dynamic range and channel noise level, product receipt, etc. The severity of the convection probably also contributes to the chance of seeing such pronounced cloud-top features.

Using SEVIRI infrared channels it is not possible to observe such cloud-top features; detection of these was traditionally reserved for visible channels, especially ones with higher resolution. Hence, it is perhaps the first time these features are observable during night hours, i.e. 24 hours per day, from geostationary orbit.

For example, SEVIRI Night Microphysics RGB can be used for particle size differentiation, but only of warm clouds. The crucial channel for microphysical information, namely 3.9 µm channel, has too much noise for cold clouds (at night) so cannot be used for particle size detection.

In this case a mesoscale convective system (MCS), passing mainly over the state of Nebraska in the south-westerly flow, formed multiple updrafts. A few of those were persistent updrafts, producing more pronounced cloud-top features and a ‘point source’ for small ice particles (namely updrafts A and C in the imagery).

Figure 2: GOES-16 Dust RGB animation, 11 May 00:02–01:32 UTC. Download animation (MP4, 522 KB)

Only by looking at the animated Dust RGB product (Figure 1 and the zoomed-in animation in Figure 2) can the following cloud-top characteristics be inferred:

Figure 3
Figure 3: Jumping cirrus seen on GOES-16 Visible image, 11 May 01:22 UTC
  • Updraft locations (with associated overshooting tops) — depicted with ‘bubbling’ yellow points inside the thick, ice cloud anvil in red shades.
  • Areas of newly-formed, small ice particles — wider green to yellow shades within anvil cloud, where newly-grown updrafts tend to have more a green-coloured anvil towards the upstream side.
  • AACPs (Above Anvil Cirrus Plumes) — areas downstream of the persistent updrafts (updraft wake area), perhaps seen only with animated imagery in a specific shape of yellow shades (updrafts A and C).
  • Gravity waves of higher amplitudes — happening in the wake of the updrafts, normally V-shaped, consequence of upper air flow going over and around updrafts, associated to possible occurrence of jumping cirrus (Figure 3).

Confirmation on cloud-top features comes with advanced resolution of visible channels, namely 0.64 µm channel (Figure 4).

Figure 4
Figure 4: GOES-16 VIS 0.64 µm, 11 May 00:42 UTC

At the channel resolution of 500 m a complex picture of the MSC topography is apparent, especially during dusk when the Sun zenith angel is very high. Even small gravity waves are visible and the topography of updraft domes (e.g. updraft marked by D on the imagery).

The infrared window imagery, 10.4 µm channel (Figure 5, right), reveals the temperature distribution on the top of the anvil, namely cloud rings or U-shapes (normally associated with strong updrafts).

The positions of the coldest singularities (signs of updrafts) are fairly well-centred with the actual positions of the updraft domes seen on the visible channel (Figure 5, left).

Phase separation is best performed with the green component, where the 8.4 µm channel provides the crucial information on the liquid versus ice water particles. It is not clear how well this channel differentiate between different particle sizes.

Image comparison
GOES-16 VIS 0.64 µm, 11 May 00:42 UTC GOES-16 IR 10.4 µm, 11 May 00:47 UTC
Figure 5: Comparison of GOES-16 visible and infrared imagery, 11 May 00:47 UTC

Figure 6 compares individual components of the used Dust RGB recipe. Looking at different components is appears that the strongest influence on the shades of the anvil have BTD (Brightness Temperature Difference) components. These two consistently show areas of updrafts in white shades, and thin cirrus in dark shades (edges of MCS anvil).

Figure 6
Figure 6: 4-panel view of the Dust RGB and individual components of this product, 11 May, 00:42 UTC

It appears from the Cloud Phase RGB (Figure 7, left) that this product does not give enough information on the cloud-top microphysics during the sunset period, under relatively low reflection conditions. Therefore, it does not bring the confirmation of the potential particle size indications given by the BTD 11.2–8.4 µm (Figure 7, right).

Image comparison
Cloud Phase RGB BTD 11.2–8.4 µm
Figure 7: Comparison of GOES-16 Cloud Phase and Dust RGB images, 11 May 00:42 UTC.

In conclusion, here is a statement from Michael Pavolonis (NOAA Federal) on the properties of the ABI 8.4 µm channel and its ability to differentiate between different (ice) particle sizes:

"The 8.4 µm channel is certainly sensitive to particle size. However, it is also sensitive to many other factors such as cloud phase, cloud optical depth, cloud height/temperature, ice particle habit, and many background/measurement variables (e.g. water vapor, atmospheric temperature, surface temperature, surface emissivity, viewing angle, etc.). While there may be instances where RGB’s qualitatively reveal interesting cloud particle size effects to those that are very well trained in image interpretation, a radiative transfer model is needed to map the multi-spectral measurements into geophysical parameters such as cloud particle size."

By continuing to use this website, you are giving consent to analytic cookies being used. For more information on how EUMETSAT uses data and how you can disable this, please view this web page: Analytics Cookie Policy