Oh, How the Seasons Change!

November 11, 2014Uncategorizedice and snow, river, vegetationCurtis_Seaman

The transition between winter and summer happens twice a year. Unless you live in the tropics. Then you don’t really have winter. If there are seasons there, they are “dry” and “wet”. But, at high latitudes, the transition from summer to winter is often abrupt and cannot be mistaken for anything else. It’s hard not to notice when 22 hours of sunlight turns into 2 hours of sunlight and back again the following year. For places like the interior of Alaska, it’s also hard not to notice the temperatures in the 70s F giving way to temperatures below 0 °F. (Of course, here in Colorado, our temperatures went from 70 °F to 10 °F in a period of about 36 hours hours this week. Not to brag or anything.)

Summers are short at high latitudes but, autumns are shorter. So, what can VIIRS tell us about the changing seasons?

We’re going to focus on the “Natural Color” RGB composite. In this composite, the red component is the reflectance at 1.6 µm, the green component is the reflectance at 0.87 µm and the blue component is the reflectance at 0.64 µm (a red visible wavelength). The Natural Color RGB is useful for detecting snow and ice, determining cloud top phase, monitoring vegetation and detecting flooding. So, it’s good to get familiar with it. Plus, it’s one of the best RGB composites you can make with the high-resolution (375 m) channels on VIIRS.

Here is a Natural Color RGB image of Alaska from 6 September 2014 – at the end of summer:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3, taken 22:56 UTC 6 September 2014.

It’s easy to pick out the ice clouds from the liquid clouds, just as it’s easy to see the snow on the Brooks Range. They appear cyan instead of white (as they would in the True Color RGB composite). If you want to know why snow and ice appear cyan in this composite, click here. But, I want to draw your attention to the third and nineteenth longest rivers in the US. Lower-48ers that didn’t click on the link are probably wondering which rivers I’m referring to. But, of course, Alaskans know which rivers I’m talking about, right?

OK, fine. Just to make sure we’re all on the same page, I’m talking about the Yukon and the Kuskokwim. These rivers are wide enough to be seen by VIIRS. Did you find them in the above image? Click on the image to see it in full resolution and make sure you see them.

Notice how the rivers are almost black. That’s because water is poorly reflective at these three wavelengths. This will come in handy later on. Now, let’s look again at these rivers a month later (7 October 2014):

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3, taken 23:19 UTC 7 October 2014.

Why are the rivers surrounded by brown when a month earlier the river valleys were green? Deciduous trees like to hang out in the river valleys of southwestern Alaska, and these trees have already changed color and lost all their leaves over the course of this month, leaving behind only the bare branches and trunks. This is one sign of the changing seasons. (Note, however, that it is just a coincidence that these areas appear brown here. This is a false-color composite. The brown color is due to the reduced reflectivity of the deciduous forests at 0.87 µm caused by the lack of leaves, not because the tree trunks are brown. Read this if you want to learn more.)

Another sign of the changing seasons is the additional snow present. Everywhere north of the Brooks Range is snow covered. Plus, you can see pockets of snow in the Kilbuck and Kuskokwim Mountains, the Aleutian Range and in the hills and mountains surrounding Norton Sound.

Fast forward another month to 4 November 2014:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3, taken 22:51 UTC 4 November 2014.

Now, almost the whole state is covered by snow. But, look at the rivers! They are no longer black – they are cyan, which means they have frozen over. Although, if you look closely, you can see a few pixels suggesting open water on the lower sections of the Yukon, generally between Russian Mission and Mountain Village. Also, look closely where the Kuskokwim River flows into Kuskokwim Bay, downstream from Bethel – there is ice along the shores, but open water in the middle. Ice is also forming in Norton Sound and has covered Baird Inlet.

Two weeks earlier (21 October 2014), there is more of a mix of ice and open water on the Yukon and Kuskokwim:

VIIRS Natural Color RGB composite of channels I-1, I-2, and I-3, taken 23:59 UTC 21 October 2014.

Identifying ice and open water on the rivers is very important. When the two coexist, ice jams can occur. When an ice jam forms, it blocks the flow of the river, which can flood areas upstream of the jam. When the jam breaks, it can cause a flash flood downstream.

Ice jams, even on small rivers, can show their power:

Imagine what they can do on the third and nineteenth largest rivers in the country!

Sometimes, you don’t have much time to get out of the way. Note: you might not want to watch this one if you are prone to motion sickness:

Who remembers what happened to Crooked Creek on the banks of the Kuskokwim in 2011? Or on the Yukon River at Eagle in 2013?

Of course, once the rivers are completely frozen over, there is no threat of an ice jam or flooding. But, now that you know how to spot ice forming on these rivers in the fall, you’ll hopefully be able to spot the return of open water in the spring, when the threat returns.

To really capture the changing of the seasons, here’s an animation of the relatively cloud-free images from 3 September 2014 to 6 November 2014:

Animation of VIIRS Natural Color RGB composites from 3 September 2014 to 6 November 2014.

Click on the image to view the animation. It’s 17 MB, so it may take a while to load. See if you can pick out when the first ice forms on either river.

Revisiting Scaling on the Solstice

July 18, 2014Uncategorizedday/night, twilightCurtis_Seaman

OK, so by this time, it’s about a month after the Summer Solstice in the Northern Hemisphere. If the title bothers you, just replace “solstice” with “summer”. Then replace “on the” with “now that it’s” to make the sentence grammatically correct.

If you read the very first post on this blog (you may want to go back and read it again, even if you already did) you would know that it’s difficult to display VIIRS Day/Night Band (DNB) imagery when the day/night terminator is present. The data varies by 6-8 orders of magnitude between day and night (depending on the moon and other factors), which is tough to represent when you only have 256 colors available to make an image. That’s why the Near Constant Contrast EDR exists.

But, what if you don’t have access to the Near Constant Contrast (NCC) data? Is there anything one can do to get useful information on both sides of the terminator in the same image?

The short answer is: yes. The long answer is: yesss. But, that’s not to say the results are always going to be perfect.

Over the years, I have acquired examples of things that various people or groups have tried that didn’t work. Like this one:

VIIRS DNB example from 11:57 UTC 1 May 2013. This image is scaled for “day”, “night” and “twilight” regions. Image courtesy GINA.

Or this one that you should have seen before (if you re-read the blog post I told you to read):

DNB example using solar zenith angle-dependent scaling.

VIIRS DNB example from 12:48 UTC 13 August 2013. This image uses solar zenith angle-dependent scaling. Image courtesy of GINA.

This is not to single anyone out (especially because I don’t know the names of the people who produced these images) – I’ve tried a number of things that didn’t work out either. Knowing why they don’t work is the key to finding something that does work.

The first example tried to divide up the image into a “day” side, “night” side and “twilight” area in-between, then scale each region independently of the others. Of course, that leads to discontinuities at the boundaries of each region. (The brightest “twilight” pixels border the darkest “day” pixels, etc.)

The second example broke up the image into many different zones based on solar zenith angle, and then (I assume) applied some kind of smoothing to prevent discontinuities. But, you still end up with a wavy pattern of anomalously brighter and darker areas within each solar zenith angle zones. That’s distracting.

When I was developing software to produce Day/Night Band imagery from across the globe, I thought I had something when I scaled the imagery based on the median radiance values in the image. (If you want to know how it worked, it was along these lines: scale the image linearly between max and min values where max = [median*8 < maximum data value] and min = max/256) You didn’t need to know if it was day or night. Unlike before, this scaling didn’t highlight the day side or the night side when the terminator was in the scene. It highlighted the twilight zone (Ahhh! Run away!), like this:

VIIRS DNB image from 13:32 UTC 10 May 2014. This image uses “median-based” linear scaling, as described in the text.

Not perfect. But, in my defense, this scaling does work for most of the globe. The tropical group at CIRA uses it for their DNB images on “TC Realtime“. Also, it doesn’t work too bad near the solstice, when most of Alaska is on the “day” side of the terminator, even in the primary “nighttime” overpass:

VIIRS DNB image from 13:48 UTC 21 June 2014. This image uses “median-based” linear scaling, as described in the text.

It has the added benefit that you can associate each level of brightness with a specific radiance value.

Now, I was going to leave my DNB scaling as is because, “Hey, we can just use the Near Constant Contrast (NCC) in these situations. Why break my back trying to re-invent the wheel?” That is, after all, the primary point of the NCC – to make it easy to display DNB across the terminator. Then CIRA temporarily lost access to NCC data. My hand was forced. I had to think of a solution.

How about this idea: instead of finding the median value of the whole domain, break up the domain into small zones according to solar zenith angle, and then apply the same “median-based” linear scaling? Here’s what you get if you break up the image into solar zenith angle bins of 0.01 degrees:

VIIRS DNB image from 13:48 UTC 21 June 2014. This image uses “median-based” linear scaling over zones grouped by solar zenith angle.

Not bad. You get a lot of contrast throughout the image (particularly on the “night” side), but it still has stripes in it. This is due to the fact that the presence or absence of clouds (or city lights or whatever) is constantly changing the distribution of radiances within each solar zenith angle bin. The stripes get larger if you use larger bins. Smaller bins means a smaller sample size and, therefore, “less stable” median values. What we need is more of an absolute scale rather than a relative scale.

At this point, it occurred to me that Steve Miller at CIRA (my boss, but that doesn’t mean I’m brown-nosing) already came up with a solution. He developed a “dynamic scaling” method where max and min are based solely on the solar and lunar zenith angles.

VIIRS DNB images from 1 June 2014 and 14 June 2014 spanning the terminator. These images use “dynamic scaling” as defined in the text.

As you can see, the dynamic scaling produces good contrast on both sides of the terminator, which is what users are typically looking for. It’s important to be able to identify clouds and surface features (like icebergs, for example) throughout the entire image – not just on one side or the other or just in the middle.

In my bungled attempt to apply his dynamic scaling within my software, I had another epiphany. If you plot the radiance values (on a log-scale) as a function of solar zenith angle across the terminator, you get something that looks like this:

Scatterplot of observed DNB radiance values as a function of solar zenith angle for the 13:53 UTC 12 July 2014 overpass. Gray curves represent the max and min bounds used for the scaling.

Doesn’t that look a lot like the error function turned sideways? This became the basis for a new form of “dynamic scaling”: find the error function that fits the maximum and minimum expected values of the data as a function of solar and lunar zenith angles. In fact, those are the curves plotted on the graph. Steve Miller’s dynamic scaling is simply a piecewise linear approximation to my error function curves. (Or, more correctly, you could say my error function curves are a continuous approximation of his piecewise functions.)

A similar error-function-like distribution of radiance values occurs across the moon/no-moon terminator, except the range is only 2-3 orders of magnitude instead of 6-7. We simply multiply the lunar zenith angle-fitted error function and the solar zenith angle-fitted error function on the “night” side to account for the variation in radiance from full moon to new moon. When you do that (and apply a square-root correction), you get this image from a night with a full moon:

VIIRS DNB image from 13:53 UTC 12 July 2014. This image uses “erf-dynamic scaling” as described in the text.

And this image from a few nights after last quarter moon:

VIIRS DNB image from 13:48 UTC 21 June 2014. This image uses “erf-dynamic scaling” as described in the text.

These compare pretty well with the Near Constant Contrast imagery from the same times:

VIIRS NCC image from 13:53 UTC 12 July 2014.

VIIRS NCC image from 13:48 UTC 21 June 2014.

Here’s what the piecewise linear dynamic scaling gives you:

VIIRS DNB image from 13:43 UTC 21 June 2014. This image uses “dynamic scaling” as described in the text.

VIIRS DNB image from 13:31 UTC 13 July 2014. This image uses “dynamic scaling” as described in the text.

And, if you’re curious, the “erf-dynamic scaling” works just as well during the day as it does at the terminator. Here is an example of the DNB with this scaling, followed by the associated NCC image:

VIIRS DNB image from 22:01 UTC 21 June 2014. This image uses “erf-dynamic scaling” as described in the text.

VIIRS NCC image from 21:58 UTC 21 June 2014.

Of course, the exact form of the error functions could be tweaked here or there to provide a better fit to the natural variability of the observed radiances. But, after one lunar cycle of testing, the results look promising. It is possible to scale the Day/Night Band across the terminator to provide useful information for “day” and “night” (and even across the scary twilight zone)!

UPDATE (27 January 2015): The “erf-dynamic scaling” algorithm has been implemented in CSPP. So users of that software product should be on the look-out for the latest version. (It was added in October or November 2014. I don’t know the actual date.) Thanks to the folks at CIMSS who develop CSPP for quickly adding this! We look forward to collaborating more with the CSPP developers on future products. Also, you can read about a correction to the “erf-dynamic scaling” algorithm here.

Funny River Isn’t Laughing

May 23, 2014Uncategorizedfire, Fire Temperature RGB, I-4, Natural Color RGB, RGB composite, True Color RGB, twilightCurtis_Seaman

Imagine you’re getting ready for bed. You take one last look out of your bedroom window and you see this:

Photo of the Funny River Fire, taken near 1:00 AM local time (0900 UTC), 21 May 2014. Courtesy Bill Roth/Alaska Dispatch.

Good luck sleeping!

That is the light and smoke from the Funny River Fire, which started on 20 May 2014 and rapidly grew to over 44,000 acres in under 48 hours. Rapidly expanding fires like this one burn through a lot of fuel and can create a lot of smoke. Enough smoke to be seen by radar:

And certainly enough smoke to be seen from space:

VIIRS "True Color" RGB Composite, taken 21:58 UTC 20 May 2014

VIIRS “True Color” RGB Composite of channels M-3, M-4 and M-5, taken 21:58 UTC 20 May 2014

Look for the grayish plume arcing from the Kenai Peninsula out over the Gulf of Alaska. That is one impressive smoke plume!

The image above is what we like to call a “True Color” image. It is a combination of the red, green and blue visible-wavelength channels of VIIRS (M-5, 0.67 µm; M-4, 0.55 µm; M-3, 0.48 µm, respectively), so named because it represents the “true” color of objects as the human eye would see them. It is the most commonly used “RGB composite”, which is why a number of people simply refer to it as the “RGB”. To add more confusion, other people call it “Natural Color” because it is only an approximation of the “true” color and has to be corrected for atmospheric effects (i.e. Rayleigh scattering) to look right. However, we want to distinguish this from the EUMETSAT definition of “Natural Color”, which looks like this:

VIIRS "Natural Color" composite, taken 21:58 UTC 20 May 2014

VIIRS “Natural Color” composite of channels I-1, I-2 and I-3, taken 21:58 UTC 20 May 2014

Notice that the smoke plume isn’t as easy to see in the Natural Color image. This is because we are looking at longer wavelengths {1.61 µm (I-3, red component), 0.87 µm (I-2, green component) and 0.64 µm (I-1, blue component)} and smoke scatters less solar radiation back to the satellite as the wavelength increases. This is also why the smoke appears blue – the only channel of the three really able to see the smoke plume is I-1 (the blue component). The fact that we are able to see the smoke at all in the Natural Color image is a testament to just how much smoke there is!

Here’s another Natural Color image from a few orbits before, which happened to be right after sunrise:

VIIRS “Natural Color” composite, taken 13:48 UTC 20 May 2014

The smoke plume is as optically thick as a cloud, and is even casting shadows!

Now, the Funny River Fire is the perfect opportunity to introduce another RGB composite being developed at CIRA for use with VIIRS, which we call the “Fire Temperature RGB”. This RGB composite uses the near-IR and shortwave-IR channels to highlight fires. The blue component is M-10 (1.61 µm), the green component is M-11 (2.25 µm) and the red component is M-12 (3.70 µm). Here’s what the Fire Temperature RGB looks like for the 21:58 UTC overpass:

VIIRS "Fire Temperature" composite, taken 21:58 UTC 20 May 2014

VIIRS “Fire Temperature” composite of channels M-10, M-11 and M-12, taken 21:58 UTC 20 May 2014

This is yet another example of just how large a fire this is!

The Fire Temperature RGB provides information on how hot (or how “active”) the fire is. This is due to the fact that fires generally show up best around 4 µm. At shorter wavelengths, the amount of background solar radiation increases, so fires need to be hotter to be visible. This means that relatively cool or small fires will only show up in M-12 and appear red. Hotter fires will show up in M-11 and M-12 and appear yellow. The hottest, most active fires will be detected in all three channels and show up white. Also, there’s very little sensitivity to smoke, as you can see, so the imagery provides useful information even with such a thick smoke plume. Due to radiative differences between liquid droplets and ice particles, ice clouds tend to appear dark green, while liquid clouds appear more blue.

At night, M-11 doesn’t produce valid data (although there is a push from several user groups to change that), but M-10 and M-12 still provide valuable information. In fact, the only thing you can see at night in M-10 are fires and gas flares. Even without M-11 at night, we can use the Fire Temperature RGB to monitor the fire around the clock (whenever VIIRS is overhead) and here’s an animation to prove it:

Animation of VIIRS Fire Temperature RGB images of the Funny River Fire (2014).

In the first frame, the fire is obscured by clouds (we still can’t see through those) but, after that, you can see the fire was pushed to the shores of Tustumena Lake. With nowhere else to go, the fire expanded east and west. The fire slowly loses intensity until the last two frames, when activity picks up on the north side. (Although, clouds block the view of the east flank of the fire at that time, so we can’t say how active it is there.) Also notice on the second and third nights (11:00 to 13:00 UTC) that the fire appears less intense. This is probably due to the combination of reduced fire activity at night and the presence of clouds that are not visible at night in this RGB composite.

Here’s what the Funny River Fire looked like in the high-resolution fire detection channel (I-4, 3.74 µm) for the same times:

VIIRS channel I-4 images of the Funny River Fire (2014)

For this image, cooler pixels appear light, while warmer pixels appear dark. Pixels with a brightness temperature above 340 K have been colored. This channel by itself shows the clouds over the fire at night, but it can be ambiguous during the day because liquid clouds are highly reflective at this wavelength, so they also look warm.

It has already been demonstrated that the Day/Night Band is capable of detecting fires at night (click here and here for examples). So, why not just use it here? I tell you why: the day/night terminator is already encroaching on the nighttime overpasses. This makes it difficult to see fires, since the light from the fire is competing with light from the sun. This was a particularly intense fire on the first night though, so the Day/Night Band was able to see it (as evidenced by this Near Constant Contrast [NCC] image):

VIIRS NCC image, taken 12:09 UTC 20 May 2014

As you can see, the NCC product shows both the fire and the smoke plume. Notice also that you can’t see any city lights, even though it’s still nighttime over the fire because there is enough twilight to drown out the signal. That makes fire detection with the Day/Night Band tricky when the terminator is so close. It’s only because the Funny River Fire was so intense that we are able to see it. Of course, since it was so intense, we are able to see the smoke easily even if we can’t see the light from the fire.

Glow-in-the-dark Water

March 13, 2014Uncategorizedaurora, day/night, moonglint, sunglintCurtis_Seaman

Have you ever started looking for something, only to find something else that was more interesting than what you were originally looking for?

Back on 10 January 2014, there were widespread rumors of a significant aurora event that would be visible much further south than usual. It got a lot of people excited, even in our backyard here in Colorado. But did it happen?

If you’re curious, here is an explanation as to why the aurora forecasts were a bust. But, that’s not to say the aurora didn’t exist anywhere on the globe. The VIIRS Day/Night Band image below shows there was an aurora that made it as far south as Iceland.

VIIRS Day/Night Band image, taken 02:31 UTC 10 January 2014

What about on the next orbit? Was the aurora still there?

VIIRS Day/Night Band image, taken at 04:13 UTC 10 January 2014

If you squint, you can maybe see it over south-central Greenland. But, hold on a minute! What’s that in the upper-left corner? Why is the water so bright off the west coast of Greenland?

This is a nighttime scene, as evidenced by the city lights over Iceland, Ireland and the UK, although you might not think that by looking at only the left side of the image. And, let me assure you, the day/night terminator never appears at this angle at this time of day in January.

CIRA researchers have recently begun producing VIIRS imagery centered on Alaska on a quasi-operational basis. About a month ago, I noticed this image that also shows “glow-in-the-dark” water, and the mystery deepened:

VIIRS Day/Night Band image, taken 11:37 UTC 9 February 2014

And again, a few days ago, the Day/Night Band captured this image:

VIIRS Day/Night Band image, taken 12:35 UTC 10 March 2014

This time, there is a pretty vivid aurora but, you can also see bright water off the southern coast of Russia. So, what’s with water that appears to be glowing in the dark?

Is it some kind of bio-luminescent phenomenon, like milky seas? Is it some kind of radioactivity that makes everything glow, like in The Simpsons? Or an alien-UFO conspiracy to control the world’s population?

Sorry to get your hopes up, “truthers,” but it’s a pretty mundane explanation. (Either that, or I’m a member of the Illuminati. MWAH HA HA!) Have you ever looked at a body of water and saw glare from the sun? Or seen glare off of snow and ice? We call that sunglint. It is related to the Bi-directional Reflectance Distribution Function (BRDF), the mathematical way we describe that incoming light on a surface reflects more at certain angles than others. But, it’s not only sunlight that causes glint. Moonlight does it, too. (What is moonlight, if not reflected sunlight?)

Notice that the images with the glowing water were taken roughly a month apart. That’s not just a coincidence. According to this website, each of those images was taken 2-3 days after the moon reached first quarter, when the moon was 75-80% full. Why is this important? Because the phase of the moon is related to when the moon rises and sets, and this determines where the moon is in the sky when VIIRS passes overhead.

From a day or two after last quarter to new moon to a day or two after first quarter, the moon is below the horizon when VIIRS passes overhead during the nighttime overpass. (It’s above the horizon on the daytime overpass, but you can’t tell because the sun is so bright.) From just after first quarter to full moon to just after last quarter, the opposite is true – the moon is up at night and down during the day. When you get to 2-3 days after first quarter, that’s when the moon is close to the western horizon when VIIRS passes over at night. That’s why the left sides of the above images are brighter than the right sides. And, that’s also when this form of moon glint occurs, just like in this clip.

It’s not aliens or UFOs or mysterious radioactivity. It’s the geometry between the satellite, the Earth and the moon and the preferential reflection of light off of a body of water. It’s repeatable and predictable. It’s science.

UPDATE (3/14/2014): “Glow-in-the-dark” water is not confined to high latitudes like Greenland and Alaska. It happens anywhere the angle between the satellite, the Earth’s surface and the moon is in the glint range. Steve Miller (CIRA) forwarded information about a case he looked at off the coast of Louisiana. Here’s one of his images with everything labelled:

VIIRS Day/Night Band image, taken 07:41 UTC 12 January 2014

VIIRS Day/Night Band image, taken 07:08 UTC 12 January 2014. Interesting features have been identified and labelled.

This case occurred when the moon was 90% full. The brightest water occurs where the surface is calm and the “glint angle” is less than 10°. When the surface is not calm, waves scatter the light in different directions and only a portion of the light is reflected to the satellite. This makes the water appear not as bright. For glint angles between 0° and 30°, waves will scatter some of the light back to the satellite, and the water won’t appear dark. Calm water outside the 10° glint zone will appear dark, though, because the angle of the water surface isn’t right to reflect the moonlight back to the satellite. This is what you see along the coast of Texas. Outside of the 30° zone, waves aren’t at the proper angle to reflect light back to the satellite.

To demonstrate this, here’s a comparison with the same area on the next orbit along with the glint angles:

Comparison between DNB images and lunar glint angle for consecutive VIIRS overpasses on 12 January 2014.

On the next overpass, about 100 minutes later, all the water is outside the glint zone (the glint angles are all higher than 100°) and the water is dark everywhere, as expected.

Camouflage Clouds

February 10, 2014Uncategorizedclouds, ice and snow, IR, longwave, RGB composite, shortwaveCurtis_Seaman

The natural world is full of examples of animals that have evolved camouflage. Check out this list and see how many of the animals you can find. Another example that I find particularly interesting is the Potoo bird. Some animals, like the Potoo, use camouflage to hide from predators, while others, like the Polar Bear, are predators who use camouflage to hide from their prey and make it easier to sneak up on them. Clouds also use camouflage (or at least it seems that way) to hide from weather satellites. Are they predators trying to hunt down and destroy innocent weather forecasts? Are they hiding because they fear some atmospheric phenomenon will find them and glaciate them? It’s tough to tell what goes on in the mind of a cloud, since it isn’t alive and has no brain.

Did you click on the first link above and take the test? If so, you are now aware of the skills you’ll need to detect clouds in the Arctic.

Let’s start with an infrared (IR) image of Alaska taken by VIIRS at 23:29 UTC on 3 February 2014:

VIIRS IR image (I-5), taken at 23:29 UTC 3 February 2014

The question is: where are all the clouds?

Colors correspond to the color table in the lower right corner of the image. IR images typically use color tables like this one to highlight the structures of cold cloud tops. And given the long Arctic winter nights, IR images like this are typically all that are available. The problem that arises is that low clouds, like fog and stratus, have a brightness temperature similar to the background surface, making them hard to spot. Sometimes temperature inversions exist and the low clouds in the image are warmer than the background surface. Cloud-free valleys and the ice in the Arctic Ocean may be colder than the clouds you’re trying to see, so you can’t always use temperature or temperature differences to detect clouds.

Now, we’ll get some help for this case, since 23:29 UTC is 2:30 in the afternoon (for most of Alaska), so there is some sunlight. This allows us to compare the IR image above with visible-wavelength images. Of course, that doesn’t always help, since clouds can be camouflaged at many different wavelengths. Here’s the VIIRS “True Color” RGB composite (a composite of M-3, M-4 and M-5, which are at blue, green and red portions of the visible spectrum, respectively):

VIIRS “True Color” RGB composite of M-3, M-4 and M-5, taken 23:29 UTC 3 February 2014

Many of the clouds here are still camouflaged because the clouds and snow and ice all appear white. The clouds that are easy to spot in the IR image (e.g. over the Gulf of Alaska) are similarly easy to spot in the True Color image. But, what about the clouds that are still hiding?

For now, we’re going to focus on three interesting regions that contain camouflage clouds: the Tanana River valley near Tok, the Arctic Ocean north of Russia, and the northern tip of the Yukon Territory. Keen-eyed observers may already be able to spot the clouds I’m referring to by noticing cloud shadows or by remembering where forests or mountains are located that are now obscured. (Although, the clouds in the northwest Yukon Territory are really difficult to see because of saturation issues at the terminator.) The three areas I’m referring to are highlighted below:

VIIRS IR image (I-5), taken 23:29 UTC 3 February 2014. The areas of interest discussed in the text are highlighted.

Clouds are not so easy to see in these three areas, are they? (Remember, you can click on any image to see the high-resolution version.)

The Day/Night Band (and its Near Constant Contrast counterpart) show the clouds in these areas a bit better than the True Color image (and certainly better than the IR image). Here we show the Near Constant Contrast (NCC) image, so we’re not impacted by the presence of the day/night terminator:

VIIRS NCC image, taken 23:29 UTC 3 February 2014.

The clouds over Tok (lower right oval) are bit difficult to see, but you should be able to see the shadow they cast.

The clouds over northern Yukon Territory (upper right oval) are interesting for a couple of reasons: they obscure the terrain (the easiest way to tell those are clouds); they hug the surface so they aren’t casting any shadows; and the cloud on the northwest side of the oval is much warmer than the cloud on the southeast side of the oval even though they look similar at visible wavelengths (compare the visible images with the IR images).

The left oval over the Arctic Ocean shows the big difference in opacity between looking at a cloud in the IR versus the visible wavelengths. The IR image shows an opaque, slightly darker (i.e. warmer) shape barely discernible from the background ocean and ice. The NCC image shows a semi-transparent cloud (also slightly darker [i.e. less reflective] than the background ice) with a lot of structure due to gravity waves. Underneath the cloud feature, you can clearly see where the icebergs and open water are located. Try doing that with the IR image.

The shortwave (or what the JPSS program office calls “midwave”) IR image (I-4) is not the most intuitive to interpret, but it also shows these camouflage clouds (some better than others):

VIIRS shortwave IR image (I-4), taken 23:29 UTC 3 February 2014

The I-4 band is centered at 3.74 µm, a wavelength where reflection of solar radiation and the Earth’s emission both play an important role in what you are seeing. In the color table used here (best at highlighting wildfires and volcanic eruptions), highly reflective objects and warm objects show up darker. Ice clouds, snow and sea ice are all poorly reflective and cold, so they appear brighter. Liquid clouds are highly reflective, which makes the clouds over Tok easily visible.

The Yukon clouds are still pretty camouflaged because, even though they are liquid, they are colder, and don’t have as big a contrast with the background surface. As mentioned before, the clouds to the northwest in the oval are darker (warmer) than the clouds in the southeast part of the oval.

The Arctic Ocean clouds are interesting here. The reflective component reveals the gravity waves, but the emissive component obscures the ice and open water below. These clouds are not only camouflaged in certain wavelengths, they also act as camouflage for the ice below!

This example shows that not all clouds are easy to see with individual channels – even when looking at two or three different wavelengths. But, it does show that the visible-wavelength information provided by the NCC image is quite a bit different from the IR information that is typically used. And, even though this was a daytime scene, all the stuff I wrote still applies at night (except you lose the reflective component to the shortwave IR imagery).

Finally, let’s look at another RGB composite, what EUMETSAT calls “Natural Color”:

VIIRS “Natural Color” composite of I-1, I-2 and I-3, taken 23:29 UTC 3 February 2014. This image has been cropped relative to the other images to reduce the file size.

This composite uses bands I-1 (0.67 µm), I-2 (0.86 µm) and I-3 (1.61 µm) as the blue, green and red components, respectively. Snow, ice and ice clouds appear the bluish color known as cyan because they are highly reflective in I-1 and I-2, but poorly reflective in I-3. Liquid clouds appear white, to dirty white, to a grayish, pale cyan color depending on particle size and reflectivity. Vegetation is very green. Unlike the True Color RGB composite, the low liquid clouds in all three ovals are easier to see here because now they are a significantly different color than their respective backgrounds. Plus, the Arctic Ocean clouds are still transparent enough to show the ice and open water below.

The Natural Color composite may be the best way to detect low liquid clouds in this region, but it’s only available when the sun is above the horizon. The Day/Night Band (or NCC) is a useful stand-in, when it’s not available.

It just goes to show: the clouds may try to hide, but VIIRS can always find them!

Seeing the Light

VIIRS in the Arctic

Oh, How the Seasons Change!

Revisiting Scaling on the Solstice

Funny River Isn’t Laughing

Glow-in-the-dark Water

Camouflage Clouds