The GOES 3.9 µm Channel Talking Points
1. Title
2. Outline
3. This session focuses on GOES channel 2, which is centered near 3.9 µm and has a
ground resolution of 4km. The actual pixel size in the midlatitudes is closer to 4 by 7
km. Channel 4 (10.7 µm) will also be discussed, in the context of a different product
with channel 2.
4. The plot on the left shows the Planck function of the sun (assumed to emit at 6000 K,
left) and the Earth (emitting temperature of 300 K, right). The 3.9 µm band is
indicated by the vertical lines. One can see that the Earth and solar curves overlap
near 3.9 µm. The plot on the right is a blown-up version near the intersection with
the 3.9 µm band. The solar curve has been divided into 3 curves, each representing a
different amount of reflectivity. There is an equal contribution of radiation at 3.9 µm
by the sun at 20% reflectance and the Earth emitting at 300 K.
The equation at the bottom shows that the 3.9 µm radiance measured from GOES has
2 terms: an emitted component (left) and a reflected solar component (right). The
emitted component depends on the emissivity at 3.9 µm, and the amount of
reflectance depends on the reflectivity at 3.9 µm. Both of these, in general, vary
according to surface type (ground, ice cloud, water cloud, etc).
5. At night, the signal-to-noise ratio in the 3.9 µm band is large over cold thunderstorm
tops; these 2 plots explain why. The plot on the left (3.9 µm) shows that the Planck
function becomes near horizontal at cold emitting temperatures, resulting in large
uncertainties/errors. On the right (10.7 µm), the curve continues to have a noticeable
slope at cold temperatures, so the signal-to-noise ratio in channel 4 is significantly
better.
6. This is an example of the effect of noise in the 3.9 µm channel. Note that this is at
night. The cold thunderstorm tops have a "dotted" appearance, a sign that the signalto-noise ratio is not good. Use the 10.7 µm channel to view cold thunderstorm tops at
night.
7. Channel saturation can occur over really hot scenes, such as wildfires and in the
desert southwest during the summer. Saturation means the radiance is maxed out and
can't detect temperatures any warmer. For these saturated pixels, GOES-12
sometimes "rolls over" to the opposite end of the color scale, indicating a pixel which
is supposedly very cold. GOES-11 doesn't appear to have this problem. Although
the cold pixel is false, it can actually help a very hot fire show up compared to the
surrounding pixels. See the example on the right.
8. At night, we can ignore the solar reflected component, so the measured radiance
consists of only the emitted component. This is a simpler situation. It turns out that
water clouds have a larger emissivity at 10.7 µm than at 3.9 µm, so taking the
difference between these 2 channels at night exposes these emissivity differences.
This can be used to differentiate water clouds (fog, low stratus, etc) from ice (cirrus
clouds, snow, etc).
9. In AWIPS, the 10.7 - 3.9 channel difference product can be used to identify these low
water clouds. If you sample the values over fog and stratus, they show negative
counts in AWIPS, even though the brightness temperature difference in positive. In
this example, fog fills the central valley of California, and the 10.7 - 3.9 difference
shows -10 counts there.
10. It's very useful to take advantage of AWIPS ability to modify default color tables. In
this example, we have noted that the fog and stratus have counts between -20 and -5
counts, so we've made all counts within that range a solid yellow color. This makes
the fog/stratus show up very well.
11. If you prefer greyscale color tables, you could alternatively stretch the table from
white to black over the region of observed counts, as shown here. This is a big
improvement over the default fog and the linear color enhancements.
12. A method was developed to modify AWIPS so that it outputs 10.7 - 3.9 values in
brightness temperature difference rather than in counts, making the result more
physical. The website below outlines the steps necessary to make this change.
13. This is a loop of the 10.7 µm channel over California. Note that the central valley
brightness temperatures do not appear to be significantly difference from the
surrounding areas. It would be very difficult to conclude that the valley was filled
with fog.
14. This loop shows is for the same time period as the last loop, but it's now the 10.7 - 3.9
difference product, with the 2nd enhancement discussed above applied. The
fog/stratus now shows up very well in the central valley. Note how the fog begins to
dissipate from north to south toward the end of the loop.
15. 10.7 µm loop at night. Fog/stratus is not clearly identifiable.
16. Same time period as last loop, but again the 10.7 - 3.9 difference. Now areas of
fog/stratus begin to stand out: southern South Dakota, western Arkansas, northern
Missouri, much of Alabama and Georgia, etc.
17. Same as previous loop, but with station plots overlaid. With this added piece of
information it becomes possible to differentiate between fog and low stratus. For
example, at 02:45 UTC (the final time), Kansas City probably has low stratus, while
Atlanta has fog.
18. Another application of the 10.7 - 3.9 difference is the identification of thunderstorm
outflow boundaries at night. They are evident only if they're associated with low
clouds.
19. A loop of the difference product showing an MCS moving northeastward over
Nebraska and leaving behind a few outflow boundaries. One is moving southward in
eastern Colorado. These boundaries stuck around until the following day, at which
time tornadic thunderstorms formed that afternoon. The boundaries likely played a
role in that day's convective evolution.
20. During the day, we must worry about the solar reflected component. In many cases,
the reflected component is larger than the emitted component, so the difference
product can actually switch signs when the sun rises. The solar reflectivity (r3.9)
varies by cloud and surface type; this can aid in the differentiation of snow v/s water
cloud v/s ice cloud, etc. AWIPS does not compute r3.9 directly, but the difference
product provides an adequate approximation for it, especially for cold emitting
objects (like cold cloud tops).
21. This graph shows the reflectivity at 3.9 µm (r3.9) as a function of particle size for both
liquid water and ice clouds. First, note that for a given size, water clouds reflect more
3.9 µm radiation than ice clouds. Also notice that reflectivity generally increases as
particle size decreases. Typically, water cloud particle sizes are much smaller than
ice clouds, so the difference in reflectivity between the two cloud types is significant.
22. This is a visible loop of the same California fog event we looked at earlier, except
now the sun has risen. During the day, the visible band is generally the best channel
to use in fog identification. However, when snow cover exists (like over the Sierras),
some ambiguity may exist between snow and fog.
23. This loop shows the difference product from before sunrise until late in the afternoon.
Notice how the fog switches from being cooler (darker) than the surrounding ground
before sunrise to warmer (brighter) after the sun begins reflecting from the clouds.
This is because the reflected component is large, resulting in a warmer 3.9 µm
brightness temperature than at 10.7 µm, so the difference is negative. Remember that
AWIPS switches the sign, so the counts are shown as strongly positive. Notice that
the snow over the Sierras now appears dark, and fog/stratus to the east of the
mountains becomes more evident. Using this difference product during the day with
a similar enhancement, fog/stratus will appear relatively bright and snow will be
relatively dark.
24. Here's another example over Wyoming and Colorado. This visible loop shows clouds
moving over some areas which have recently received snow. There is also some
fog/stratus, but it's difficult to differentiate between the snow cover and fog/stratus
using the visible channel alone.
25. Here is the corresponding difference product loop with the same enhancement as with
the California case 2 slides earlier. Now notice that the areas in northern Wyoming
and southern Montana appear dark, indicating snow cover (since they were bright in
the visible). But the areas in the Nebraska panhandle and northeastern Colorado
appear bright, suggesting that fog/stratus blankets that area. The mid-level clouds
moving to the northeast are also bright, an indication that they are primarily liquid
water clouds.
26. (skip this in the web presentation)
27. To create a similar enhancement for the difference product during the day, stretch the
counts from black to white just over the observed count values. In this case, we
stretched it from -5 to +85, but this may vary from case to case. It's important to take
advantage of this enhancement tool whenever possible.
28. Here's a MODIS example over the northeast. The first frame is the visible with
observed snow depth plotted. Almost the entire area has snow cover, even though the
visible reflectance varies greatly. This is because dark forest in upstate New York is
darker than the fewer-tree area further south. The 2nd frame is the MODIS 3.9 µm
band. In this color table, warmer brightness temps are given darker colors. The dark
strips oriented north/south are areas of fog/stratus which were not easily identifiable
in the visible image.
29. Another useful application of the 3.9 µm band is fire detection. These 2 graphs show
why. On the left, the x-axis is the percent of a single infrared pixel encompassed by a
500 K fire; we assume the background temperature is 300 K. The 2 curves show the
brightness temperatures in channels 2 and 4. Notice that the channel 2 curve is much
warmer than the channel 4 curve when about 1/3 of the pixel is burning. The right
graph is the difference between these 2 curves. Channel 2 is MUCH more sensitive
to subpixel heat, so the difference product will greatly assist in identifying fires.
30. Here is an example over Colorado back in 2002. The top loop (channel 2) shows a
number of saturated (and rolled-over) pixels, making the fires easy to spot. In the
bottom loop (channel 4), the fires are much more difficult to locate. This was during
the GOES-11 science test, and the rollover problem hadn't yet been corrected.
31. This image is a new experimental effective radius product developed at CIRA. It
uses the 3.9 µm reflectivity to retrieve thick ice cloud effective radius values. Notice
that the clouds over Texas and Oklahoma have significantly smaller ice crystals than
those over Kansas and Missouri. Research is ongoing to explain this difference. The
strength of the storms may be playing a role.
32. The first of the GOES-R series of satellites is scheduled to be launched in ~2012.
The imager aboard this satellite, called the Advanced Baseline Imager (ABI), is a
significant improvement over the current GOES imager. It has improved spectral
(from 5 bands to 16), spatial (4 to 2 km in the infrared, 500 m in the visible), and
temporal (routine 5-minute imagery, 30-sec during RSO) resolution. This chart
shows the proposed bands. There are several in the shortwave infrared portion of the
spectrum, so this should help us in cloud and surface identification.
33. This MODIS example over eastern Utah is a 3-color product composed of 3 MODIS
channels. Each band has been assigned a color, allowing for better feature
identification. The white areas are fog/stratus, while the purple areas show snow
cover. With more channels, GOES-R will have similar capabilities.
34. Summary