Oh, Yakutsk! It has been a long time – 2012, to be exact – since we last spoke about you. It was a different time back then, with me still referring to the Natural Color RGB as “pseudo-true color”. (Now, most National Weather Service forecasters know it as the “Day Land Cloud RGB”). VIIRS was a only a baby with less than one year on the job. Back then, the area surrounding the “Coldest City on Earth” was on fire. This time, we return to talk about ice.
You see, rivers near the Coldest City on Earth freeze during the winter, as do most rivers at high latitudes. Places like the Northwest Territories, the Yukon, Alaska and Siberia use this to their advantage. Rivers that are frozen solid can make good roads, a fact that has often been overly dramatized for TV. Transporting heavy equipment may be better done on solid ice in the winter than on squishy, swampy tundra in the summer. But, that comes with a cost: ice roads only work during the winter.
In remote places like these, with few roads, rivers are the lifeblood of transportation – acting as roads during the winter and waterways for boats during the summer. But, what about the transition period that happens each spring and fall? Every year there is a period of time where it is too icy for boats and not icy enough for trucks. Monitoring for the autumn ice-up is an important task. And, perhaps it is more important to monitor for the spring break-up of the ice, since the break up period is often associated with ice jams and flooding.
We’ve covered the autumn ice up before (on our sister blog), but VIIRS recently captured a great view of the spring break up near Yakutsk, that will be our focus today.
We will start with the astonishing video captured by VIIRS’ geostationary cousin, the Advanced Himawari Imager (AHI) on Himawari-8 from 18 May 2018:
The big river flowing south to north in the center of the frame is the Lena River. (Yakutsk is on that river just south of the easternmost bend.) The second big river along the right side of the frame is the Aldan River, which turns to the west and flows into the Lena in the center of the frame.
Now that you are oriented, take a look at that video again in full screen mode. If you look closely, you will see a snake-like section of ice flowing from the Aldan into the Lena. This is exactly the kind of thing river forecasters are supposed to be watching for during the spring!
Of course, this is a geostationary satellite, which provides good temporal resolution, but not as good spatial resolution. The video is made from 1-km resolution imagery, but we are looking at high latitudes on an oblique angle, so the resolution is more like 3-4 km here. So, how does this look from the vantage point of VIIRS, which provides similar imagery at 375 m resolution? See for yourself:
(You will have to click on the image to get the animation to play.)
This animation includes both Suomi NPP and NOAA-20 VIIRS. That gives us ~50 min. temporal resolution to go with the sub-kilometer spatial resolution. Eagle-eyed viewers can see how the resolution changes over the course of the animation, as the rivers start out near the left edge of the VIIRS swath (~750 m resolution), then on subsequent orbits, the rivers are near nadir (~375 m resolution) and then on the right edge of the swath (~750 m resolution again). In any case, this is better spatial resolution than AHI can provide at this latitude.
One thing you can do with this animation is calculate how fast the ice was moving. I estimated the leading edge of the big “ice snake” moved about 59 pixels (22.3 km at 375 m resolution) during the 3 hour, 21 minute duration of the animation. That works out to an average speed of 6.7 km/hr (3.6 knots), which doesn’t seem unreasonable. Counting up pixels also indicates our big “ice snake” is at least 65 km long, and the Aldan River is nearly 3 km wide in its lower reaches when it meets the Lena River. That is in the neighborhood of 200 km2 of ice!
That much ice moving at 3 knots can do a lot of damage. Just look at what the ice on this much smaller river did to this bridge:
(Make sure you watch it all the way to the end!)
Roughly one month ago, social media (and, later, more conventional media) outlets were inundated with numerous reports of orange snow in eastern Europe and western Asia – reports like this one, this one and this one. Of course, it wouldn’t really be a hit with the media unless someone could claim it was “apocalyptic”. And of course, the apocalypse didn’t happen. It was simply Saharan dust picked up by high winds from an intense mid-latitude cyclone and deposited far away. We’ve seen this before with VIIRS.
These reports focused on Sochi, Russia, home of the 2014 Winter Olympics. Unfortunately, every time I looked for it in VIIRS imagery, it was cloudy in Sochi. But, the plume of Saharan dust that caused this event was clearly visible over the Mediterranean:
This image came from our new NOAA-20 VIIRS, which, at this point, is not operational and undergoing additional testing. If you look closer, you might also notice smoke or smog over Poland in the image above (upper left corner). If you really zoom in (click on the image to get to the full resolution version), you may notice a brownish tint to the snow along the north shore of the Black Sea – where the BBC report I linked to listed additional sightings of orange snow. But, the dust-covered snow shows up more clearly in this “before and after” image courtesy of S-NPP VIIRS and the @NOAASatellites twitter account:
(As an aside: differences in technique used to produce these true color images are likely larger than the differences between S-NPP VIIRS and NOAA-20 VIIRS, so don’t read too much into the fact that the dust-on-snow appears more clearly in the @NOAASatellites image than in my own.)
But, dust-on-snow is not limited to areas within a few thousand kilometers of the Sahara Desert. (It is limited to areas within 40,000 km of the Sahara [in the horizontal dimension, at least], since that is roughly the circumference of the Earth – and assuming you ignore dust storms on Mars.) Dust on snow can happen anywhere you have snow within striking distance of a source of dust. Another example was captured by a new Landsat-like micro-satellite, Venµs, and its non-microsat predecessor, Sentinel-2B, Landsat’s European cousin. A more dramatic example happened last week right here in Colorado. Here is a VIIRS true color image of Colorado from S-NPP VIIRS, taken on 14 April 2018:
Here are similar images from NOAA-20 and S-NPP from 18 April 2018:
The trick is to compare these two images with the image from 14 April. The other trick is to know where you’re supposed to be looking. (Hint: we’re looking at the Sangre de Cristo mountains in southern Colorado.) Here’s a “before” and “after” image overlay trick I’ve used before. (You may have to refresh the page before it will work.) Both of these images are the S-NPP VIIRS ones, for simplicity:
If you slide the bar left to right, you should notice the snow is more brown in the mountains just right of center in the 18 April image. There are other areas where the snow melted between the two images, plus a couple of small clouds that add to the differences. Of course, this is only 750 m resolution. We get a better view with the 375m-resolution visible channel, I-1:
We lose the color information, of course, since we are looking at a single channel, but it is obvious the snow became less reflective in the 18 April image. And, we can prove that this was a result of dust. Here are the visible, true color, Dust RGB, “Blue Light Dust” and DEBRA Dust images from S-NPP on 17 April 2018, courtesy Steve M.:
If you are unfamiliar with them, we’ve looked at the Dust RGB, Blue Light Dust and DEBRA before, here and here. As seen in the above images, this was not a difficult to detect dust case. Even Landsat-8 captured this event, which is surprising given the narrow swath and 16-day orbit repeat cycle. (Sure, it’s higher resolution than VIIRS, but will it be overhead when you need it?)
So now we get to why dust-on-snow is important. There is a growing body of research (e.g. this paper) that shows dust-on-snow has a big impact on water resources in places like the Rocky Mountains. You see, dirty snow is less reflective than clean snow. That means it absorbs more solar radiation. This, in turn, means it heats up and melts faster, leading to earlier spring run-off. The end result is less water later in the season, which opens the door to wildfires and more severe droughts. This article that, coincidentally, was published as I was writing this, sums things up nicely. It is so important, the Center for Snow and Avalanche Studies has formed CODOS: the Colorado Dust on Snow Program, whose purpose is to monitor dust on snow and provide weekly updates.
As for why you shouldn’t eat orange snow, that should be obvious. You shouldn’t eat any snow that isn’t pure white (and even that might be risky). But, feel free to eat colorful ice, as long as you know where it came from.
Today, we’re going to take a look at another less-covered VIIRS channel on this blog: M-9, also known as the “cirrus band”. (Disambiguation: if you’re looking for the electronic musical group “Cirrus (band)“, you’re in the wrong place.) We don’t use M-9 on this blog much because it doesn’t often provide amazing images. But, it is used for a lot of practical applications, so it is worth knowing about. We are also going to say “Hello!” to Suomi-NPP’s baby brother, NOAA-20, and welcome a new VIIRS instrument in space!
Unlike M-8, the “cirrus band” (PDF) is on nearly all of the new geostationary satellites (except Himawari). It’s also on MODIS, Landsat, and several other polar-orbiting satellite imagers. The “cirrus band” is unique in that it is highly sensitive to water vapor, but is located in the near-IR (1.38 µm) where emission from the Earth is minimal. (So, contrary to popular belief, VIIRS does have a water vapor channel. It just doesn’t behave like the typical mid-wave IR water vapor channels most people are used to.)
Electromagnetic radiation at 1.38 µm is absorbed by water vapor. But, the Earth and its atmosphere are too cold to emit much at this wavelength. (Thankfully, or we would have all melted by now.) Of course, the sun is hot enough. This means the 1.38 µm radiation coming from the sun is absorbed by water vapor in our atmosphere, and the only* radiation making its way back to VIIRS is what is reflected off of clouds above the water vapor. This makes channels centered at 1.38 µm particularly useful at identifying thin cirrus that would otherwise blend in with the background on other channels. Hence, the name “cirrus band”. (* Of course, reflection off of high clouds is not the only source, as we shall see. That’s the reason for this blog post.)
So, high clouds are white and the background is black – this is the assumption when looking at VIIRS’s cirrus band (unless you’re using a funky color table). But, take a look at this image that S-NPP VIIRS took on 17 January 2018:
On my monitor, viewing angle makes a big difference as to how bright the features appear. If you are viewing this on a laptop or tablet, your screen is much easier to adjust that than my Jumbotron if it’s hard to see. You can also move your head around and see if anyone else looks at you funny. (This is also a good way to test out a TV in the store before you buy it. Will people sitting off to the side get the same view as someone directly in front of the TV? You might want to know that if hosting a party for the big game this weekend.)
Let’s zoom in on the area in question:
And, give the image maximum contrast:
There is a feature in that image that looks awfully like the coastline of the Gulf of Mexico stretching from Louisiana to the Florida panhandle. It sure looks like you can see the Mississippi River, the Tennessee River, and all the “lakes” in eastern Texas. But, I thought water vapor was supposed to absorb all the radiation before it made it to the surface! And, this is Louisiana we’re talking about. The entire coastal region of the state is a big swamp – I mean a collection of bayous. So, there should be plenty of water vapor around.
One would expect to see all the way to the surface in high-altitude arid areas, like the Bolivian Altiplano and the upper elevations of the Atacama Desert. And, you do:
But, one does not expect to see the surface of Louisiana at 1.38 µm, since it is so close to sea level and it is one of the most humid parts of the United States. Maybe something is wrong with S-NPP VIIRS? Let’s look at our new baby, NOAA-20 VIIRS:
And, once again, with maximum contrast:
Note that NOAA-20 was launched back in November 2017, and is still undergoing post-launch testing and checkout, so it has not been declared operational just yet. But, this is a good test for the new VIIRS. It can see the same surface features S-NPP did 50 minutes earlier. And, it means that both instruments are working. So, why can we see all the way to the surface of Louisiana in the “cirrus band”? Because, the atmosphere was incredibly dry.
Here’s the sounding from Slidell, LA (on the other side of Lake Pontchartrain from New Orleans) on 12 UTC 17 January 2018. Notice the precipitable water value (“PWAT”) is 2.47, which is reported on soundings in mm. That’s just less than 0.1 inches. The nearby soundings taken at Shreveport and Lake Charles reported PWATs of 2.45 mm and 2.77 mm, respectively. Normal for this time of year is about 7 times greater! (Note that he corrected his typo.)
To put this into perspective, this was drier than the Sahara Desert was a few days later:
Notice you can’t see the surface of the Sahara, indicating there was more water vapor in the air over the desert than there was over Louisiana. The only thing you can see are the cirrus clouds and other clouds that made it to the upper atmosphere. This is more typical of the “cirrus band”.
Now, back to Louisiana: the dry, Arctic airmass resulted in a number of record low temperatures. Plus, this was accompanied by snow, as seen by both S-NPP and NOAA-20:
Snow was reported all the way to Gulf Coast, and you can see evidence of it in the images around Houston, TX, which is pretty rare. But, wait! Why didn’t we see snow in the M-9 “cirrus band” images? Because snow is not very reflective at 1.38 µm, and it blends in with the background. To show what an interesting winter it has been, here’s a map put out by the Weather Prediction Center from 18 January 2018, showing estimated total snowfall accumulations for this winter (so far). Note that an area of Mississippi and Louisiana has had approximately the same amount of snow as most of Iowa and southern Wisconsin (and even here in Northern Colorado!). All 48 contiguous United States have received measurable snowfall!
Fun fact: you can open one of the True Color images in a new browser tab, and the other image in this tab and toggle back and forth between them. This allows you to see the clouds move, and the edges of the snowfield melt. If you have eagle eyes, you can also see that the S-NPP image is sharper on the east side of the image (close to its nadir), while the NOAA-20 image is sharper on the west side (close to its nadir). The satellites are both in the same orbit, but on opposite sides of the Earth. Since the Earth is constantly rotating underneath them, and the VIIRS swath is designed to fill all the gaps at the Equator (unlike MODIS), their ground tracks at low and mid-latitudes are separated by half the width of a VIIRS swath. Nadir for one VIIRS is near the edge of the swath of the other VIIRS. (But, not at high latitudes.) The distance from Tallahassee, Florida to Houston, Texas is a pretty good rule of thumb for the spatial distance between the two satellites when they’re over the United States. Fifty minutes is a good rule of thumb for the temporal distance between them (and this is true all over the globe).
So, for once, Louisiana was colder than the Arctic (Ocean, at least) and drier than the Sahara Desert!
I wrote the first post on this blog more than 5.5 years ago. Since then, I have covered a multitude of instances where VIIRS imagery has helped us learn about the world we live on. But, during that time there has been one channel on VIIRS that has never been mentioned. Not once. And, what may be even more surprising is that this channel is not featured on any of the next generation geostationary satellites. It’s not on the GOES-R Program’s ABI, not on Himawari’s AHI, not on the upcoming Meteosat Third Generation FCI. Those with photographic memories will know exactly which channel I’m talking about. The rest of you will just have to guess, or go back through the archives and use the process of elimination to figure it out.
So, is this channel useless? Why is it on VIIRS, but not ABI? Which one is it? The suspense is killing me! I can’t answer that second question, but I can definitely answer the third and give some insights to #1. (The short answer to #1 is “No” – otherwise we wouldn’t be here.) But, to do this, we have to remember why Lake Mille Lacs disappeared earlier this year. It might also be good to remember our earlier posts on Greenland, because that is the location of our most recent mystery.
We begin with the view of Greenland from GOES-16 back at the end of July 2017:
This video covers the period of time from 0700 UTC 27 July to 2345 UTC 28 July. If you follow this blog, you already know that this the “Natural Color” RGB composite, which in GOES-16 ABI terms is made of bands 2 (0.64 µm), 3 (0.86 µm) and 5 (1.61 µm). Notice the whitish coloration over the central portion of Greenland. This is the feature of interest.
We know from experience (and earlier blog posts) that snow and ice are not very reflective at 1.6 µm, which is why it takes on that cyan appearance in Natural Color imagery. Whitish colors are indicative of liquid clouds. But, the feature of interest doesn’t appear to move over this two day period. (If you look closely, it does appear to shrink a little, though.) It’s hard to believe a cloud could be that stationary over a two day period.
Let’s isolate the 1.6 µm band by itself to see if we can tell what’s going on:
Shortly after the first sunrise, you can see a patch of liquid clouds over the ice that quickly dissipate, leaving our feature of interest exposed. Clouds appear again near the first sunset, and late in the second day (28 July). The feature of interest isn’t as bright as those clouds, but is brighter than the rest of the ice and snow on Greenland.
At shorter wavelengths, nearly all of Greenland is bright, so our feature of interest isn’t as noticeable. Here’s the 0.86 µm band from ABI:
But, it shows up at the two longer shortwave IR bands. Here’s the 2.25 µm band:
The same is true for 3.9 µm, but I won’t waste time showing it.
So, what is going on? What is our feature of interest?
Well, the problem is, Greenland is way off on the limb from the perspective of GOES-16’s current location. Perhaps we need a better view from something that passes directly overhead of Greenland. Hmmm. What could that be?
This is a VIIRS blog after all, so I think you know the answer to my rhetorical question. Let’s start with our good old friend, Natural Color, which we should all be familiar with:
You can tell by the shadows cast where the clouds are, even if they are a similar color to the background of snow and ice on Greenland. But, the feature of interest isn’t very obvious. There appears to be an area of lighter cyan over the central portions of the ice sheet, but it definitely doesn’t look like a cloud. Let’s break it up into single channels, like we did with ABI, starting with M-7 (0.86 µm):
Again, it’s all bright. How about M-10 (1.61 µm)?
Now, Greenland appears all dark. For completeness, let’s look at M-11 (2.25 µm):
It’s subtle, but you can see a hint of brightening over the south-central portion of the ice sheet. (In case you’re wondering why it looks so much darker in VIIRS than ABI, it’s because our visible and near-IR GOES-16 imagery uses “square root scaling” by default. In image processing terms, it’s the same as a gamma correction of 2. The VIIRS images don’t have that.) Now, for the ace up my sleeve – the one channel that has never appeared before on this blog:
This is M-8, centered at 1.24 µm. Its primary use is listed in the JPSS Program literature as “cloud particle size.” Based on reading the documentation for the cloud products, it appears M-8 is used operationally only as a backup for M-5 (0.67 µm) in the cloud optical thickness and effective particle size retrievals under certain conditions, or when M-5 fails to converge on solution. One of those conditions is the retrieval of cloud properties over snow and ice. As we shall see, however, M-8 is very good at determining the properties of the snow and ice itself.
M-8 shows quite clearly the bright central portion of Greenland (our feature of interest) surrounded by dark at the edges of the ice sheet (even without any gamma correction). Snow-free areas appear brighter than the edge of the ice sheet because, much like M-7/0.86 µm, vegetation is also highly reflective at 1.24 µm.
This example shows what we’ve long known. Snow and ice are highly reflective in the visible (and very near IR) portions of the electromagnetic spectrum. In the short- and mid-wave IR, snow and ice are absorbing and cold. This means they don’t emit or reflect much radiation at these wavelengths. That’s why they appear dark at 1.61 and 2.25 µm. M-8 straddles the boundary of these regions as exemplified by this graph:
The information in this graph comes from the ASTER Spectral Library created by NASA. Note that the reflectance of snow in M-8 is highly variable and a function of the snow grain size. This may explain why the central portion of Greenland’s ice sheet appears so bright, while the edges are so dark in M-8. Another explanation is that, much like in Minnesota, snow melt causes a drop in reflectance. Slush just isn’t as reflective as fresh snow, and M-8 is highly sensitive to this.
The last week in July was a very warm one for Greenland. The capitol, Nuuk, recorded highs in the 60s (°F), or upper-teens (°C), peaking at 71°F (22°C) on 29 July 2017. Normal for that time of year is 52°F (11°C).
Since Greenland is pretty far north, we can take advantage of the multiple VIIRS overpasses per day and really capture this snowmelt:
This animation, which you may have to click on to get it to play, covers the three day period 27-29 July 2017. Here’s it is obvious what impact the heat wave is having on Greenland’s ice and snow. Our “feature of interest” really shrinks over this period of time.
In early August, the snow and ice start to recover and become more reflective again. Here’s an extended animation that includes the relatively clear days of 17 July, 20 July and the entire period from 30 July to 3 August 2017:
Our “feature of interest” is unmelted snow/ice on Greenland’s ice sheet.
Now, this is the VIIRS Imagery Team Blog. We can do a better job of highlighting this snowmelt by combining it with other channels in an RGB composite. One way is to replace M-7 with M-8 in the Natural Color RGB:
Fresh, fine snow has the cyan color we’re all familiar with, but now coarse snow and melting snow are a deeper, more vivid blue color.
Another option takes a page out of the EUMETSAT Snow playbook. Here’s one with M-8 as the blue component, M-7 as the green component and M-5 as the red component:
Now the fresh, fine snow is pale yellow, while the coarse snow and snowmelt are a darker yellow-orange. The question is: which one do you like better?
So, I have now talked about every band on VIIRS. And, I learned that the last time I looked at melting on Greenland, I should have been looking at M-8 from the very beginning.