GOES-16 (GOES-East) “Red” Visible (0.64 µm), Shortwave Infrared (3.9 µm) and “Clean” Infrared Window (10.3 µm) images (above) showed that Canadian wildfires burning along the Manitoba/Ontario border produced a pyroCumulonimbus (pyroCb) around 1930 UTC on 22 May 2018.

As the pyroCb moved southeastward over western Ontario, the coldest GOES-16 cloud-top infrared brightness temperatures were around -55ºC (orange enhancement), which corresponded to altitudes of about 10.3 to 10.8 km according the rawinsonde data from Pickle Lake, Ontario (below).

In a comparison of 1-km resolution NOAA-19 Visible (0.63 µm), Shortwave Infrared (3.7 µm) and Infrared Window (10.8 µm) images at 2210 UTC (below), the minimum cloud-top infrared brightness temperature was -58.1ºC (darker orange enhancement), which roughly corresponded to altitudes of 10.6 to 11.0 km (just below the tropopause) on the Pickle Lake soundings.

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An explosive eruption from the Halema’uma’u crater at the Kilauea summit on the Big Island of Hawai’i occurred around 1550 UTC on 19 May 2018. Using Himawari-8 data, multispectral retrievals of parameters such as Ash Cloud Height (above) and Ash Loading (below) from the NOAA/CIMSS Volcanic Cloud Monitoring site helped to characterize the volcanic ash plume.

Later in the day, a Suomi NPP VIIRS True-color Red-Green-Blue (RGB) image viewed using RealEarth (below) showed the hazy signature of volcanic smog or “vog” which had spread out to the south, southwest and west of the Big Island. Light amounts of ash fall were reported downwind of Kilauea.

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When cloud top temperatures are very cold, the 3.9 µm imagery will have characteristics that suggest a noisy signal.  The 45-minute animation above shows a cold cloud top east of Florida in 4 different infrared channels:  3.9 µm (Upper Left), 10.3 µm (Upper Right), 8.5 µm (Lower Left) and 12.3 µm (Lower Right).  That the 3.9 µm image shows noise is not a new problem, as it was present in legacy GOES imagery as explained here.  At very cold temperatures the relationship between radiance (detected by the satellite) and temperature is highly non-linear, because of the character of the Planck function for that wavelength, meaning a very small change in radiance — within the noise — causes a large change in temperature (Compare the first two figures at this link for legacy GOES, for example).

Examine the two figures for GOES-16 below. They show the Planck curves for Band 14 (11.2 µm) and Band 7 (3.9 µm). Two things are apparent. Band 7 (3.9 µm), by design, covers a larger range of temperatures. In addition, very small changes in detected radiance (“counts”) at cold temperatures cause very big changes in the 3.9 µm brightness temperature. The relationship between detected radiance and very cold temperatures is much smoother at 11.2 µm.  The 3.9 µm band lacks precision compared to the other window channels, such as the 11.2 µm, for very cold temperatures. 

A zoomed-in view for cold brightness temperatures between 190 and 230 K (-83.15º C to -43.15º C) is shown below. If a true temperature of 208 K is being sensed by the satellite at the two wavelengths, it will be well-resolved at 11.2 µm, but the 3.9 µm detection will jump between 205 K and 210 K: the nature of the relationship between radiance and brightness temperature is such that there is less precision at the colder end at 3.9 µm. In the 30 K range from 197-227 K, just 12 possible bits are available in the 3.9 µm band (12 out of 2^14 — 16,384; recall that Band 7 on ABI has the highest bit depth of all the channels).  A change of just one count is a large difference in 3.9 µm brightness temperature.

Users need smarter ways to enhance the coldest 3.9 µm to prevent the flashing pixels evident in common traditional color and black-and-white enhancements.  Consider creating a color enhancement that shows only one color at temperatures colder than, say, -40º C, because the detector does not precisely distinguish between the coldest temperatures.  In other words, don’t highlight the noise!  Conversely, don’t use the 3.9 µm imagery at night to discern cloud-top features.   During the day, solar radiation at 3.9 µm reflected off cloud tops causes an increase in apparent brightness temperature so this quantization noise does not occur.

As noted above, this is not a new problem. An image (produced using McIDAS-X) of an Mesoscale Complex over the Great Plains of the United States from GOES-16 is here at 10.3 µm and here at 3.9 µm; the same image from GOES-15 is shown here at 10.7 µm and here at 3.9 µm. In both shortwave images, speckling at very cold cloud top temperatures is apparent.

(Thanks to Mat Gunshor, CIMSS, and Tim Schmit, NOAA, for figures and comments on this entry)

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Severe thunderstorms developed along and ahead of a cold front that was moving across the Northeastern US on 15 May 2018. 1-minute Mesoscale Domain Sector GOES-16 “Red” Visible (0.64 µm) images (above) showed the progression of these storms — and SPC storm reports (plotted in red, and parallax-corrected to align with the corresponding cloud-top feature) included an EF2 tornado at 2029 UTC near Kent, New York and a macroburst producing winds of 100-110 mph at 2044 UTC near Brookfield, Connecticut.

The corresponding GOES-16 “Clean” Infrared Window (10.3 µm) images (below) showed the evolution of cold overshooting tops, as well as the development of a few “enhanced-v” signatures with a pronounced warm wake immediately downwind of the cold overshooting top.

A toggle between 1-km resolution POES (NOAA-19) AVHRR Near-Infrared “Vegetation” (0.86 µm) and “Dirty” Infrared Window (12.0 µm) images (below) provided a more detailed view of the storm at 2004 UTC. SPC storm reports within +/- 30 minutes of the image are plotted on the 12.0 µm image.The coldest cloud-top infrared brightness temperature was -82ºC, associated with an overshooting top in southeastern New York.

POES (NOAA-19) Near-Infrared “Vegetation” (0.86 µm) and “Dirty” Infrared Window (12.0 µm) images, with plots of SPC storm reports [click to enlarge]

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