The Rise of the Paraguay Brings Down Paraguay

When was the last time you heard anything about Paraguay? Nope – they weren’t in the World Cup, that was Uruguay. (Paraguay actually finished last out of all South American teams when it came to World Cup qualifying. Sorry to remind you, Paraguayans.) A quick perusal of the web indicates that the country has a history of isolationism, so it may not come as a surprise that news out of Paraguay is few and far between.

For you non-Paraguayans in the audience: How many of you knew that Paraguay was the richest nation in South America in the mid-1800′s? Paraguay held that title right up to the point that they tried to keep Brazilian influence out of a civil war in Uruguay. That kick-started the War of the Triple Alliance, which ultimately killed more than half the population of Paraguay, strengthened Argentina as a nation, and is credited with bringing about the end of slavery in Brazil. Paraguay has never been the same since. It became the poorest country in the region – a title it has held, pretty much, through today. This has caused one reporter to say (in one of the links above) that, to Paraguayans, success is a prelude to danger.

When the national football team scores, “it makes us nervous and we panic.”

But, this isn’t a metaphor for the title of this post. The title refers to Paraguay: the River (Rio Paraguay), which has brought the worst flooding in decades to Paraguay: the Country, and displaced more than 200,000 Paraguayans. Flooding has also occurred on the Rio Paraná – the second longest river in South America – and has impacted hundreds of thousands of people in Brazil and Argentina. (You won’t get me to say that it has impacted a Brazilian people – because that is an awful, overused joke. Oh, wait. Ignore what I said I wasn’t going to say.)

Just look at what the flooding did to Iguazú Falls – one of the wonders of the world you never heard about – on the border between Argentina and Brazil:

There are more pictures of the flooding at the falls here. Iguazú Falls is located at the head of a narrow canyon called the Devil’s Throat, where water levels were reported to be 16 meters (52 feet) above normal! It is said that this is the worst flooding since 1982-1983. (That flood event killed 170 people.)

As shown before, VIIRS is capable of viewing widespread flooding. So, what does VIIRS tell us about this flood? As it turns out, both the “Natural Color” RGB composite and the “True Color” RGB composite provide unique information, so let’s take a closer look.

If you simply want to see where the water is, look no further than the “Natural Color” RGB composite. The “Natural Color” composite uses the high-resolution bands I-01 [0.64 µm; blue], I-02 [0.87 µm; green] and I-03 [1.61 µm; red]. At these wavelengths, water is not very reflective (it absorbs more than it reflects). So, with low reflectivity in all three channels, water appears nearly black. That allows one to identify water easily. Here’s a Natural Color image from a clear day before the worst of the flooding began (2 June 2014):

VIIRS "Natural Color" image, taken 17:28 UTC 2 June 2014

VIIRS "Natural Color" image, taken 17:28 UTC 2 June 2014

That’s Paraguay in the center of the image. Rio Paraguay is the north-south river that cuts Paraguay in half (OK, maybe 60-40). Rio Paraná is the big river that marks the eastern border between Paraguay and Argentina, and turns south after acquiring Rio Paraguay’s water. (Look for the big reservoir in the upper-right, and follow that river down to the bottom of the image, left of center.) Make sure you click on the image, then on the “3298 x 2345″ link below the banner to see the full resolution version. Compare that with a similar image from the only clear day at the end of the month (30 June 2014):

VIIRS "Natural Color" image, taken 17:03 UTC 30 June 2014

VIIRS "Natural Color" image, taken 17:03 UTC 30 June 2014

At first glance, the most obvious flooding occurred along the Paraná in Argentina. But flooding is noticeable along the Rio Paraguay if we zoom in for a closer look. Here’s a “before” (2 June) and “after” (30 June) overlay for the area around Paraguay’s capital city, Asunción:

Drag the vertical bar over the images from left to right to compare the two. (If this “before/after” trick doesn’t work for you, try refreshing the page. It may not work at all if you’re using Google Chrome.) The flooding you see here near Asunción was associated with only a 2 m (6 ft) water rise.

Something interesting happens when we focus in on the Paraná at the Itaipú Reservoir, just upstream from Rio Iguazú:

VIIRS "Natural Color" images of Itaipu Reservoir, June 2014

VIIRS "Natural Color" images of Itaipu Reservoir, June 2014. These images have been brightened to highlight difference in reservoir color.

After the flooding, the reservoir no longer appears black. This is because the flooding washed an awful lot of dirt into the water. And it really shows up in the “True Color” RGB composite:

VIIRS "True Color" images of Itaipu Reservoir, June 2014

VIIRS "True Color" images of Itaipu Reservoir, June 2014.

The water appears more turquoise before the flood, and brown after the flood. This is because the True Color composite represents the true color of the objects in the image. It is made from channels in the blue [0.48 µm; M-3], green [0.55 µm; M-4] and red [0.67 µm; M-5] portions of the visible spectrum. Take a look again at the Iguazú Falls video above and notice how brown the water is. The True Color images capture this. The reason the water appears blue and not black in the Natural Color composite is that there is enough sediment in the water to make it reflective at 0.64 µm (the blue component of the image). The longer wavelengths in the green and red components are not sensitive to the sediment, whereas the shorter wavelengths in the True Color components are very sensitive to sediment. (This is the basis for Ocean Color retrievals.)

If we focus in on the Rio Paraná near where it meets the Rio Paraguay, we can see clearly that the Natural Color highlights where the flood waters are, and the True Color highlights the sediment in that water:

VIIRS Natural Color and True Color images of the Rio Parana, June 2014

VIIRS Natural Color and True Color images of the Rio Parana, June 2014

Unfortunately, floods on the Paraguay and Paraná rivers are not uncommon, as a resident of Asunción explains:

BONUS: The NOAA/STAR JPSS group has put together a website on the flooding in Paraguay that features my Natural Color images along with a number of other VIIRS-based products that are being developed for flood detection. A lot of people from a number of different research groups played a part in this!

Sehr Schweres Unwetter in NRW

Not having full command of the German language, “sehr schweres Unwetter” seems like an understatement. It translates as “very bad thunderstorm,” which in this case is like calling the Titanic a “very big boat”. Of course, if you live in the Great Plains, you probably refer to a supercell thunderstorm as “a little bit of rain and wind” but the storms that hit Nordrhein-Westfalen (NRW) on 9-10 June 2014 rival anything the toughest Oklahoman has experienced (minus the tornadoes). Also, keep in mind that Germany and the Low Countries have nowhere near the wide-open spaces the U.S. Great Plains are known for. Take 5 times the population of Oklahoma and cram them into a land area the size of Maryland. (Or, if you’re from Maryland, multiply your state’s population by three to approximate the population density of the area we’re talking about. Then ponder how anyone in that part of Germany is able to spend less than 18 hours per day stuck in traffic like you would be if you were suddenly surrounded by three times as many people.)

Let me set the scene for you. (If you’ve ever lived in the Midwest, you know the drill.) The air is hot and unbelievably humid. The sky is overcast. There is no wind to speak of, but there is a certain “electricity” in the air that tells you that a violent end to the heatwave is coming. Off in the distance, clouds lower and darken. A gentle rumbling of thunder slowly builds as the storm approaches. Lightning appears and becomes ever more frequent. Right before the storm hits, the winds pick up out of nowhere and… Wait! I don’t need to describe it. I can show it to you:

EDIT: I did need to describe it, because the videos are no longer available. If you weren’t able to see the videos before they were removed, they showed scary looking clouds and nearly constant lightning approaching Bochum. In fact, there were an estimated 113,000 lightning strikes across Germany from the storm.

Germany is, apparently, a land of iPhones and GoPros and all sorts of video recording equipment, and there is no shortage of video of the storm. There are videos of the storm approaching from different perspectives (here, here and here), the strong winds and heavy rains that are more reminiscent of a tropical storm (here, here and here), footage of the lightning in slow-motion and, because this is the Internet, a 30 min. montage of storm footage set to salsa music (although one commenter says the first footage is from a storm in 2010).

The aftermath is pretty impressive also – trees and large branches down everywhere blocking roads, crushing cars and stopping the never-late German train system. In fact, 6 people were killed – mostly by falling trees. Winds were observed in the 140-150 km h-1 range (approximately 85-90 miles per hour), which puts it just below a Category 2 hurricane according to the Saffir-Simpson scale. There were even reports of baseball sized hail, something that’s not unusual in Oklahoma, but is very rare in Europe. (Here is some pretty big hail in the town of Zülpich from earlier in the day.)

Now that you’ve used up the last 90 minutes looking at YouTube videos, let’s get down to business. What do satellites tell us about this storm?

EUMETSAT put together this animation of images from the geostationary satellite Meteosat-10:

Watch that video again, preferably in fullscreen mode. First, the white boxes highlight the supercell thunderstorms over Europe between 01:00 UTC 9 June 2014 and 08:15 UTC 10 June 2014. Right before sunset on 9 June, you can see a storm moving north out of France into Belgium that seems to explode as it heads towards the Netherlands and western Germany. This is our “schweres Unwetter”. The second thing to notice is where that storm is at 02:00 UTC on the 10th. That was the time that VIIRS passed overhead.

So, without any more bloviating, here’s the high-resolution infrared (I-5) image from VIIRS:

VIIRS I-5 image from 02:07 UTC 10 June 2014

VIIRS I-5 image of severe thunderstorms over Europe from 02:07 UTC 10 June 2014

The storm that caused all the damage over Nordrhein-Westfalen has weakened and is now over northeastern Germany on its way to Poland. But, a second impressive supercell complex is pounding Belgium and the Netherlands, and taking aim at western Germany once again.

The coldest pixels are 196.5 K (-76.7 °C or -106 °F) in the storm over Benelux and 198.7 K (-74.5 °C or -102.1 °F) in the storm over northeast Germany. Another impressive thing about these storms is their size relative to the size of these countries. That Benelux storm looks like it’s at least five times the size of Luxembourg and as big as Belgium! (And I’m not counting the area of the anvil, which is even larger. I’m only counting the area containing overshooting tops.)

Since it’s nighttime, what did the Day/Night Band see? Well, the answer depends on how you display the data. You see, we’re approaching the Summer Solstice in the Northern Hemisphere, where the days are long and twilight encroaches the nighttime overpasses at these latitudes. If you try to scale the radiances from lowest = black to highest = white, you get something like this:

VIIRS Day/Night Band image, taken 02:07 UTC 10 June 2014

VIIRS Day/Night Band image, taken 02:07 UTC 10 June 2014. Radiance values are displayed and scaled according to text above.

That’s not very helpful because the radiance values vary by 6 orders of magnitude across the scene and we only have 256 colors to work with to relay that information. But, we can take advantage of the fact that the Day/Night Band radiance values are, to the first order, a function of the solar and lunar zenith angles, and use this as the basis for a “dynamic scaling” that compares the observed radiance with an expected maximum and minimum radiance value that is a function of those angles. (In case you’re interested, the dynamic scaling algorithm used here is based around the error function.) This allows you to produce something like this:

VIIRS Day/Night Band image, taken 02:07 UTC 10 June 2014

VIIRS Day/Night Band image, taken 02:07 UTC 10 June 2014. This image uses dynamic scaling as described in the text.

Here, we’ve lost some quantitative information (colors no longer represent specific radiance values) but we’ve gained valuable qualitative information.  Now we can see where the storms are! Notice the shadows in the overshooting tops of our Benelux storm – right where the coldest pixels are in the infrared image. We can see some of the city lights, but not others, because the twilight encroaching from the northeast is brighter than the cities in that part of the image. (It is easy to pick out London and Paris, though.) If you read the previous post, you might be wondering why there are no mesospheric waves with these storms. That’s because there is too much twilight (and moonlight) to see the airglow. (There’s also the possibility that the stratosphere and mesosphere weren’t conducive for vertically propagating waves, but you wouldn’t be able to tell that under these lighting conditions.)

Some people like to combine the infrared with the Day/Night Band into a single image. This is done by changing the opacity of one of the images and overlaying it on the other. Here’s an example of what that looks like using the dynamically scaled Day/Night Band image:

VIIRS combined IR/DNB image from 02:07 UTC 10 June 2014

VIIRS combined IR/DNB image from 02:07 UTC 10 June 2014

The light/shadow effect of the visible information adds a sort-of 3-D effect to the infrared images and, since this is the Day/Night Band, it can show where the storms are in relation to the urban areas. Here, it seems to work better for the Benelux storm than it does for the other one. (Of course, it would be better without the twilight. And, it works best with a full moon, which occurred three days later.)

Of course, if you have access to the Near Constant Contrast imagery, you don’t have to worry about scaling. The imagery is useful as-is:

VIIRS NCC image, taken at 02:07 UTC 10 June 2014

VIIRS NCC image, taken at 02:07 UTC 10 June 2014

And the combined IR/NCC image looks like this:

Combined IR/NCC image from 02:07 UTC 10 June 2014

Combined IR/NCC image from 02:07 UTC 10 June 2014

In case you’re interested, there are additional videos, animations and images of these storms from the Meteosat High Resolution Visible (HRV) channel at the EUMETSAT Image Library.


Severe Weather in the Mesosphere

So far (*knock on wood*), it’s been a pretty quiet year for severe weather. If you only count tornadoes, there have been 81 tornado reports from 1 January to 4 April this year. (11 of those have come just this week.) This is a lot fewer than the previous three year average of 192 tornadoes by the end of March. For that, you can thank the dreaded, terrifying “Polar Vortex” you’ve heard so much about over the winter. Tornadoes don’t like to come out when it’s cold everywhere. (Although, there was a notable exception on 31 March 2014, when a tornado hit a farm in Minnesota when the area was under a blizzard warning.)

I just said that there have been 11 tornado reports this week. Eight of those came in the past 24 hours. At the southern end of the line that brought the tornadoes to Illinois, Missouri and Texas, the severe weather included golf ball-size hail and this:


That report came from the National Weather Service in Corpus Christi, TX and it was caused by non-tornadic straight-line winds in Orange Grove. Winds capable of ripping a shed out of the ground, combined with golf ball-sized hail – that’s one recipe for broken windows. And it’s not a pleasant way to be awakened at 4:30 in the morning.

A couple of hours earlier, VIIRS caught this severe storm as it was rapidly growing. Here’s what the storm looked like in the high-resolution infrared channel (I-5, 11.45 µm):

VIIRS high-resolution IR image (channel I-5), taken at 08:13 UTC 4 April 2013.

VIIRS high-resolution IR image (channel I-5), taken at 08:13 UTC 4 April 2013.

Make sure you click on the image, then on the “2999×2985″ link below the banner to see the full resolution image, which, for some reason, is the only version where the colors display correctly.

The storm that hit Orange Grove is the southern-most storm, with what looks like a letter “C” imprinted on the top. (That kind of feature typically looks more like a “V” and makes this an “Enhanced-V” storm, which you can learn more about here. Enhanced-V storms are noted for their tendency to produce severe weather.) For those of you keeping score at home, the coldest pixel in this storm is 184.7 K (-88.5 °C).

Compare the image above with the Day/Night Band image below (from the same time):

VIIRS Day/Night Band image, taken at 18:13 UTC 4 April 2014

VIIRS Day/Night Band image, taken at 08:13 UTC 4 April 2014

There are a few interesting features in this image. For one, there’s a lot of lightning over Louisiana, Arkansas and Mississippi. (Look for the rectangular streaks.) There’s even some lighting visible where our “Enhanced-V” is. Two, it takes a lot of cloudiness to actually obscure city lights: only the thickest storm clouds appear to be capable of blocking out light from the surface. Three: there are a lot of boats out in the Gulf of Mexico at 3 o’clock in the morning (and a few oil rigs as well). And four: notice what appear to be concentric rings circling the location where our severe storm is with its enhanced-V.

In this image, there is no moonlight (we’re before first quarter, so the moon isn’t up when VIIRS passes over at night). The light we’re seeing in those ripples is caused by “airglow”, which we’ve seen before. And the ripples themselves may be similar to what is called a “mesospheric bore.” If you don’t want to get too technical, a mesospheric bore is when this happens in the mesosphere. They are related to – but not exactly analogous to – undular bores, which you can read more about here.

Unlike the situation described for the undular bore in that last link, the waves here are caused by our severe storm. To put it simply, we have convection that has formed in unstable air in the troposphere. This convection rises until it hits the tropopause, above which the air is stable. This puts a halt to the rising motion of the convection but, some of the air has enough momentum to make it in to the stratosphere. This is called the “overshooting top“, and is where our -88°C pixels are located. (Look for the pinkish pixels in the middle of the “C” in the full-resolution infrared image.) The force of this overshooting top creates waves in the stable layer of air above (the stratosphere) that propagate all the way up into the mesosphere. The mesosphere is where airglow takes place, and these waves impact the optical path length through the layer where light is emitted. This of course, impacts the amount of light we see. The end result: a group of concentric rings of airglow light surrounding our storm.

You could make the argument that the waves we see in the Day/Night Band image are not an example of a bore. Bores tend to be more linear and propagate in one direction. These waves are circular and appear to propagate in all directions out from a central point. It may be better to describe them as “internal buoyancy waves“, which are similar to what happens when you drop a pebble into a pond. Only, in this case the pebble is a parcel of air traveling upwards, and the surface of the water is a stable layer of air. Compare the pebble drop scenario with this video of a bore traveling upstream in a river to see the difference.

In fact, if you look closer at the Day/Night Band image, in the lower-right corner (over the Gulf of Mexico) there is another group of more linear waves and ripples in the airglow that may actually be from a bore. It’s hard to say for sure, though, without additional information such as temperature, local air density, pressure and wind speeds way up in that part of the mesosphere.

By the way, you can see mesospheric bores and other waves in the airglow if you have sensitive-enough camera, like the one that took this image:

Photograph of a mesospheric bore. Image courtesy T. Ashcraft and W. Lyons (WeatherVideoHD.TV)

Photograph of a mesospheric bore. Image courtesy T. Ashcraft and W. Lyons (WeatherVideoHD.TV)

And, if you’re interested, the Arecibo Observatory has a radar and optical equipment set up to look at these upper-atmosphere waves (scroll down to Panel 2 on this page). The effect of these waves on atmospheric energy transport is a hot topic of research.

Golf ball-sized hail at the Earth’s surface is related to energy transport 100 km up in the atmosphere!


NOTE: This post has been updated since it was first written to clarify that the circular waves are likely not evidence of a bore, as was originally implied. They are more likely internal buoyancy waves, which are also known as gravity waves. For more information, consult your local library.

Hell Froze Over (and the Great Lakes, too)

This has been some kind of winter. The media has focused a lot of attention on the super-scary “Polar Vortex” even though it isn’t that scary or that rare. (I wonder if Hollywood will make it the subject of the next big horror movie in time for Halloween.) Many parts of Alaska have been warmer than Georgia, with Lake Clark National Park tying the all-time Alaskan record high temperature for January (62 °F) on 27 January 2014. (Atlanta’s high on that date was only 58 °F.) Sacramento, California broke their all-time January record high temperature, reaching 79 °F three days earlier. In fact, many parts of California had record warmth in January, while everyone on the East Coast was much colder than average. Reading this article made me think of an old joke about statisticians: a statistician is someone who would say: if your feet are stuck in a freezer and your head is stuck in the oven, you are, on average, quite comfortable.

One consequence of the cold air in the eastern United States is that Hell froze over. No, not the Gates of Hell in Turkmenistan. This time I’m talking about Hell, Michigan. Hell is a nice, little town whose residents never get tired of people telling that joke.

It has been so cold in the region around Hell that the Great Lakes are approaching a record for highest percentage of surface area covered by ice. This article mentions some of the benefits of having ice-covered Lakes, including: less lake-effect snow, more sunshine and less evaporation from the Lakes, which would keep lake levels from dropping. Although, that is at the cost of getting ships stuck in the ice, and reducing the temperature-moderating effects of the Lakes, which allows for colder temperatures on their leeward side.

This article (and many other articles I found) uses MODIS “True Color” images to highlight the extent of the ice. Why don’t they show any VIIRS images? Well, I’m here to rectify that.

First off, I can copy all those MODIS images and show the “True Color” RGB composite from VIIRS:

VIIRS "True Color" RGB composite of channels M-3, M-4 and M-5, taken 17:27 UTC 11 February 2014

VIIRS "True Color" RGB composite of channels M-3, M-4 and M-5, taken 17:27 UTC 11 February 2014

While it was a rare, sunny winter day for most of the Great Lakes region on 11 February 2014, it’s hard to tell that from the True Color imagery. I mean, look at this True Color MODIS image shown on NPR’s website. Can you tell what is ice and what is clouds?

There are ways of distinguishing ice from clouds, which I have talked about before but, it doesn’t hurt to look at these methods again and see how well they do here. First, let’s look at my modification of the EUMETSAT “Snow” RGB composite:

VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 17:27 UTC 11 February 2014

VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 17:27 UTC 11 February 2014

This “Snow” RGB composite differs by using reflectances at 2.25 µm in the place of the 3.9 µm channel that EUMETSAT uses. (Their satellite doesn’t have a 2.25 µm channel.) It’s easy to see where the clouds are now. Of course, now the snow and ice appear hot pink, which you may not find aesthetically pleasing. And it certainly isn’t reminiscent of snow and ice.

If you don’t like the “Snow” RGB, you may like the “Natural Color” RGB composite:

VIIRS "Natural Color" RGB composite of channels I-01, I-02 and I-03, taken 17:27 UTC 11 February 2014

VIIRS "Natural Color" RGB composite of channels I-01, I-02 and I-03, taken 17:27 UTC 11 February 2014

This has the benefit of making snow appear a cool cyan color, and has the added benefit that you can use the high-resolution imagery bands (I-01, I-02 and I-03) to create it. There is twice the resolution in this image than in the Snow and True Color RGB images. Here’s another benefit you may not have noticed right away: the clouds, while still white, appear to be slightly more transparent in the Natural Color RGB. This makes it a bit easier to see the edge of the ice on the east side of Lake Michigan and the center of Lake Huron, for example.

If you’re curious as to how much ice is covering the lakes, here are the numbers put out by the Great Lakes Environmental Research Laboratory (which is about a 25 minute drive from Hell) from an article dated 13 February 2014:

Lake Erie: 96%; Lake Huron: 95%; Lake Michigan: 80%; Lake Ontario: 32% and Lake Superior: 95%. This gives an overall average of 88%, up from 80% the week before. The record is 95% set in 1979, although it should be said satellite measurements of ice on the Great Lakes only date back to 1973.

Why does Lake Ontario have such a low percentage? That last article states, “Lake Ontario has a smaller surface area compared to its depth, so it loses heat more slowly. It’s like putting coffee in a tall, narrow mug instead of a short, wide one. The taller cup keeps the coffee warmer.”  Doesn’t heat escape from the sides of a mug as well as the top? And isn’t Lake Superior deeper than Lake Ontario? Another theory is that “Lake Ontario’s depth and the churning caused by Niagara Falls means that it needs long stretches of exceptionally cold weather to freeze.”  Does Niagara Falls really have that much of an impact on the whole lake?

So, what is the correct explanation? I’m sorry, VIIRS can’t answer that. It can only answer “How Much?” It can’t answer “Why?”


BONUS UPDATE (17 February 2014):

It has come to my attention that the very next orbit provided better images of the Great Lakes, since they were no longer right at the edge of the swath. Here, then, are the True Color, Snow and Natural Color RGB composite images from 19:07 UTC, 11 February 2014:

VIIRS "True Color" composite of channels M-3, M-4 and M-5, taken 19:07 UTC 11 February 2014

VIIRS "True Color" composite of channels M-3, M-4 and M-5, taken 19:07 UTC 11 February 2014


VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 19:07 UTC 11 February 2014

VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 19:07 UTC 11 February 2014


VIIRS "Natural Color" composite of channels I-01, I-02, and I-03, taken 19:07 UTC 11 February 2014

VIIRS "Natural Color" composite of channels I-01, I-02, and I-03, taken 19:07 UTC 11 February 2014


UPDATE #2 (18 March 2014): The Great Lakes ice cover peaked at 92.2% on 6 March 2014, just short of the all-time record in the satellite era. March 6th also happened to be a clear day over the Great Lakes, and VIIRS captured these images:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 18:35 UTC 6 March 2014

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 18:35 UTC 6 March 2014


VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 18:35 UTC 6 March 2014

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 18:35 UTC 6 March 2014

B-31 and the Pine Island Glacier

Nope. This post is not about a warplane, an alcoholic beverage or a “New Wave” band from the 1970s. (Those are all B-52s.) And I’m not talking about a county road in Michigan or a New York City bus line. B-31 is the rather bland name given to the massive iceberg that just broke off from the Pine Island Glacier in Antarctica. (Of course, if you tried to name every chunk of ice floating around Antarctica, how long would it take you to run out of names and just switch to random letters and numbers?)

This particular chunk of ice is special, however, as it has been described as the size of a city. Now, as a scientist, I have to say that the size of a city is a terrible unit of measurement. How big a city are we talking about? I suspect people who live in one of the ten largest cities in the world would laugh at what the people of Wyoming call a “city”. And are we talking the size of the greater metropolitan area or just what is within the city limits?

The article that describes B-31 as the size of city mentioned that it was roughly the size of Singapore, or twice the size of Atlanta. Those seem like odd choices for comparison. How many of you have a good idea of what the land area is of Singapore? And twice the size of Atlanta? They could have used New York City, which has just over twice the land area of Atlanta and people are probably more familiar with New York City. In any case, all of these size estimates have errors.

The original estimate came from this NASA MODIS image and associated caption, which put the size of B-31 as 35 km x 20 km. Now, that’s 700 km2 assuming the iceberg is a perfect rectangle, which you can see in the image that it isn’t. Singapore has a land area of 714 km2, while New York City is 768 km2 and Atlanta is 341 km2 (these are “within the city limits” numbers, not the size of the greater metropolitan area). Since the iceberg is actually smaller than the 35 km x 20 km rectangle based on the widest and longest dimensions of the iceberg, maybe “twice the size of Atlanta” is the most accurate estimate.

Anyway, MODIS is not the only satellite instrument out there capable of viewing B-31. Landsat-8 saw it in much higher resolution in another post from NASA. And, of course this entire blog is about what VIIRS can see. Now, VIIRS doesn’t have the resolution of Landsat or the highest-resolution channels on MODIS, but VIIRS has the Day/Night Band, allowing us to see the iceberg both day and night (at visible wavelengths).

To show why that is important, take a look at the infrared image (M-15, 10.7 µm) below. Images in the “infrared window” (the N-band window, according to this site) used to be the only way to detect surface features and clouds at night. At these wavelengths, the amount of radiation detected by the satellite is a function of the temperature of the objects the instrument is looking at. As always, to see the high resolution version of the image, click on it, then on the “1660×1706″ link below the banner.

VIIRS IR image (M-15) taken 23:34 UTC 7 November 2013

VIIRS IR image (M-15) taken 23:34 UTC 7 November 2013

See that slightly darker gray area near the center of the image? That’s open water in Pine Island Bay, which is only slightly warmer than the ice and low clouds surrounding it. Otherwise, there isn’t much detail in this picture. What really stands out are the cold, high clouds that are highlighted by the color scale. Contrast this with a visible wavelength image from the same time (M-5, 0.67 µm):

VIIRS visible (M-5) image, taken 23:34 UTC 7 November 2013

VIIRS visible (M-5) image, taken 23:34 UTC 7 November 2013

The open water in Pine Island Bay shows up clear as day because, well, it is daytime and the ice and snow reflect a lot more sunlight back to the satellite than the open water does. Icebergs can easily be distinguished from the low clouds now. You can even see through some of the low clouds to identify individual icebergs that are not visible in the infrared image. The difference in reflectivity between the ice and water at visible wavelengths is a lot greater than the difference in brightness temperature in the 10-12 µm infrared wavelengths, and that contrast is what makes things more easily visible.

Now, it is summer down there and at these latitudes, the sun is up for most of the day (actually, all day for everywhere in this scene on the Summer Solstice, which occurred on 21 December 2013), so you could say that using the VIIRS Day/Night Band to look at this stuff is unnecessary. But, since VIIRS is on a polar-orbiting satellite, it views the poles a lot more frequently than where you or I live: every 101 minutes on average, instead of every 12 hours in the low and mid-latitudes. That means it may occasionally capture a nighttime image here or there during the short nights and will frequently capture images where the day/night terminator crosses through the scene and we still want to be able to see what’s going on then. And you need the Day/Night Band to do that.

For the first time on this blog, however, we’re not going to show the Day/Night Band data exactly. We’re going to show the Near Constant Contrast imagery product, which is produced from the Day/Night Band. You can read up more on the Near Constant Contrast product and how it’s related to the Day/Night Band here. At this point, we’ll refer to NCC and DNB rather than having to type out Near Constant Contrast and Day/Night Band all the time.

Here’s a NCC image from 7 November 2013 at 20:15 UTC where the Pine Island Glacier has been identified. B-31 is still attached to the glacier – it’s sticking out into the bay and, if you look at the high resolution version of the image, you may be able to see the crack where it has started to calve.

VIIRS Near Constant Contrast image from 20:15 UTC 7 November 2013

VIIRS Near Constant Contrast image from 20:15 UTC 7 November 2013. The Pine Island Glacier is identified.

Keep your eye on that spot as you watch this zoomed-in animation of NCC images starting from the above image to 03:06 UTC 18 November:

Animation of VIIRS NCC images of the Pine Island Glacier from 7-18 November 2013

Animation of VIIRS NCC images of the Pine Island Glacier from 7-18 November 2013

I should say that the above animation does not include images from every orbit. I’ve subjectively removed images that were too cloudy to see anything as well as images where the VIIRS swath didn’t cover enough of the scene. This left 25 images over the 11 day period. Even so, VIIRS captured the moment of B-31 breaking free quite well.

Imagine the sound that this 600+ km2 chunk of ice made as it broke free. I bet it sounded something like this glacier calving event in Greenland:


One of the articles linked to above mentioned the importance of tracking such a large iceberg, because it could impact ships in the area. (Just this week a ship got stranded in ice off the coast of Antarctica.) So, I decided to see if VIIRS could track it. The results are in the MP4 video clip linked to below. You may need an appropriate browser plug-in or add-on (or whatever your browser calls it) to be able to view the video.

Animation of VIIRS NCC images from 7 November – 26 December 2013 (.mp4 file)

That’s 50 days of relatively cloud-free VIIRS NCC images (7 November – 26 December 2013), compressed down to 29 seconds. Go ahead, watch the video more than once. Each viewing uncovers additional details. Notice how B-31 doesn’t move much after 10 December. Notice how ice blocks the entrance to Pine Island Bay at the beginning of the loop, then clears out by the end of the loop. Notice all the icebergs near the shore that are pushed or pulled or blown out to sea from about 20 December through the end of the loop. Notice that B-31 isn’t even the biggest chunk of ice out there. Notice the large ice sheet on the west side of Pine Island Bay that breaks up right at the end of the loop. In fact, here’s another zoomed-in animated GIF to make sure you notice it:

Animation of VIIRS NCC images from 20-26 December 2013

Animation of VIIRS NCC images from 20-26 December 2013

That area of ice is much larger than B-31! (Dare I say, as large as the state of Rhode Island? Probably not, because then you’ll just think of how Rhode Island is the smallest US state, so it can’t be very impressive. It’s also not very accurate since that estimate is based on eye-balling it and thinking it looks like it could be four times the size of B-31.)

Of course, we are heading towards the middle of summer in the Antarctic when the ice typically reaches its minimum extent. So the ice breaking up isn’t unusual. Plus, large calving events occur on the Pine Island Glacier every few years. But, the B-31 event is noteworthy because Pine Island Glacier holds about 5% of the total freshwater contained on Antarctica.  It’s also the site of an ongoing field experiment where researchers are investigating glacier-ocean interactions. You can read up on what it’s like to install instruments on a glacier while living in a tent on the coldest continent 1000 miles from any other human settlement in this article. (That article doesn’t say if any instruments are still stuck in B-31 and floating out to sea, though.) And, if you’re curious, Pine Island Glacier has its own Twitter account. So far, the conclusions are that Pine Island Glacier is thinning, receding and speeding up. Large calving events are just one piece of the puzzle, but an important piece to understand since they contribute to sea level rise.

The calving process of B-31 was first noticed by NASA researchers noticing a crack forming in Pine Island Glacier while flying over the area in October 2011 – before VIIRS was even launched. But, VIIRS was there to capture the end result of that crack two years later!


UPDATE (22 April 2014): B-31 has continued to drift towards the open ocean. Researchers at NASA have been monitoring the movement of the massive iceberg since it first calved, and have put together their own video here, which tracks B-31 from the time of my video above into mid-March 2014.