On the Disappearance of Lake Mille Lacs

Two weeks ago, one of Minnesota’s 10,000 lakes disappeared, leaving them with only 9,999. And, it wasn’t a small one, either. It was the state’s second largest inland lake. But, this is not like Goose Lake, which actually did dry up. The lake in question simply became temporarily invisible. So, no need to panic, fishing and boating enthusiasts. But, as you’ll see, the term “invisible” can be just as ambiguous as the term “lake”.

Let’s start with the fact that Minnesota doesn’t have 10,000 lakes. Their slogan is a lie! Depending on how you define a lake, Minnesota has 21,871, or 15,291, or 11,842. But, Wisconsin might have more (or less) and likes to argue with Minnesota about that fact. Michigan might have way more (62,798) or way less (6,537). And, they all pale in comparison to the number of lakes in Alaska. Here is an article that explains the situation nicely.

With that out of the way, today’s story comes from “current GOES” and what one colleague noticed during a cursory examination of GOES Imager images. Here’s the GOES-13 visible image from 19:30 UTC 27 January 2017:

GOES-13 visible image, taken 19:30 UTC 27 January 2017

GOES-13 visible image, taken 19:30 UTC 27 January 2017

Compare that with the visible image from 19:15 UTC 2 February 2017:

GOES-13 visible image, taken 19:15 UTC 2 February 2017

GOES-13 visible image, taken 19:15 UTC 2 February 2017

Notice anything different between the two images over Minnesota? No? Then let’s flip back-and-forth between the two, with a giant, red arrow pointing to the area in question:

Animation of the above images

Animation of the above images. The red arrow points to Lake Mille Lacs.

The red arrow is pointing to the location of Lake Mille Lacs. You might know it as Mille Lacs Lake. (Either way, it’s name is redundant; “Mille Lacs” is French for “Thousand Lakes,” making it Thousand Lakes Lake.) As the above images show, on 27 January 2017 Lake Mille Lacs was not visible in the GOES image. On 2 February 2017, it was. They both look like clear days, so what happened? Why did Lake Mille Lacs disappear?

As I said before, the lake didn’t dry up. It simply became temporarily invisible. But, this requires a discussion about what it means to be “visible”. Lake Mille Lacs shows up in the image from 2 February 2017 because it appears brighter than the surrounding land. That’s because the lake is covered with snow. Aren’t the surrounding land areas also covered with snow? Yes. However, the surrounding lands also have trees which obscure the snow and shade the background surface, which is why forested areas appear darker even when there is snow.

That leads to this question: why does the lake appear darker on 27 January? Because it rained the week before. Want proof? Look at the almanac for Brainerd (NW of Lake Mille Lacs) for the period of 18-22 January 2017. Every day made it above freezing along with several days of rain. Much of the snow melted (including the snow on the lake). Want more proof? Here’s a video taken on the lake from 20 January 2017. See how Minnesotans drive around on frozen lakes – even in the rain? And, see how wet and slushy the surface of the ice is? This makes it appear darker than when there is fresh snow on top. If you’ve ever seen a pile of slush, you know it’s not bright white, but a dull gray color. The less reflective slush on the lake reduced the apparent brightness down to the level of the surrounding woodlands. That’s why the lake appeared to disappear.

Now, this is “current GOES” imagery. We can do better with VIIRS, since we have more channels to play with. And, as we all know, GOES-R successfully launched back in November 2016 and is now in orbit as GOES-16. This satellite has the first Advanced Baseline Imager (ABI) in space. The ABI has many of the same channels as VIIRS, so the following discussion applies to both instruments. “New” GOES will have up to 500 m resolution in the visible, which is much closer to VIIRS (375 m) than “current” GOES (1 km). That’s another thing to think about when we talk about what is visible and what isn’t.

Here are the VIIRS high-resolution visible (I-1) images that correspond to the GOES images above:

VIIRS high-resolution visible (I-1) image, taken 19:35 UTC 27 January 2017

VIIRS high-resolution visible (I-1) image, taken 19:35 UTC 27 January 2017

VIIRS high-resolution visible (I-1) image, taken 19:22 UTC 2 February 2017

VIIRS high-resolution visible (I-1) image, taken 19:22 UTC 2 February 2017

Although, we should probably focus on Minnesota. Here are the cropped images side-by-side:

Comparison between VIIRS high-resolution visible (I-1) images

Comparison between VIIRS high-resolution visible (I-1) images

Remember: you can click on any image to bring up the full resolution version.

Although Lake Mille Lacs is just barely visible in the image from 27 January, it’s much easier to see on 2 February. So, we get the same story from VIIRS that we got with GOES, which is good. That means we don’t have a major fault of a multi-million dollar satellite. It’s a “fault” of the radiative properties of slush, combined with the low resolution of the GOES images above.

Keep your eyes also on the largest inland lake in Minnesota: Red Lake. The Siamese twins of Upper and Lower Red Lake didn’t get as much rain as Lake Mille Lacs and its snow never fully melted, so its appearance doesn’t change much between the two images.

The GOES Imager also has a longwave infrared (IR) channel, and a mid-wave IR channel similar to VIIRS. Since the goal of this is not to compare GOES to VIIRS, but to show how these lakes appear at different wavelengths, we’ll stick to the VIIRS images. Here are the high-resolution VIIRS longwave IR images from the same times:

Comparison of VIIRS high-resolution longwave IR (I-5) images

Comparison of VIIRS high-resolution longwave IR (I-5) images

In both images, the lakes are nearly invisible! This is because the longwave IR is primarily sensitive to temperature changes, and the slush is nearly the same temperature as the background land surface. With no temperature contrast to key on, the lake looks just like the surrounding land. Although, if you zoom in and squint, you might say that Lake Mille Lacs is actually more visible in the image from 27 January. 27 January was a warmer day (click back on that Brainerd almanac), and the surrounding land warmed up more than the slushy ice on the lake. 2 February was much colder on the lake and the land. But, let this be a lesson that, just because the lake doesn’t show up, it doesn’t mean the lake doesn’t exist!

Something interesting happens when you look at the mid-wave IR. All the lakes are visible, and take on a similar brightness, no matter how slushy they are:

Comparison of VIIRS high-resolution mid-wave IR (I-4) images

Comparison of VIIRS high-resolution mid-wave IR (I-4) images

In this wavelength range, both reflection of solar energy and thermal emission are important. Snow, ice and slush are not reflective and they are cold, making the lakes appear darker than the surrounding land. The fact that the land surrounding Lake Mille Lacs and Red Lake is darker on 2 February than it is on 27 January is further proof that it was a colder day with more snow on the ground.

Here’s where we get to the advantage of VIIRS (and, soon, GOES-16): it has more channels in the shortwave and near-IR. The 1.6 µm “snow and ice” band has a lot of uses, and I expect it will be a popular channel on the ABI. Here’s what the high-resolution channel looks like from VIIRS:

Comparison of VIIRS high-resolution near-IR (I-3) images

Comparison of VIIRS high-resolution near-IR (I-3) images

Compare these with the visible images above. Now, the reverse is true: Lake Mille Lacs is easier to see in the first image than the second! You can’t call it invisible at all on 27 January! The presence of liquid water makes the slush very absorbing – more than even ice and snow – so it appears nearly black. In fact, it’s hard to tell the difference between the slushy ice-covered Lake Mille Lacs, and the open waters of Lake Superior, which has no ice or slush on it. On 2 February, we see the fresh layer of snow on Lake Mille Lacs has increased the lake’s reflectivity, but it’s still slightly darker than the surrounding snow covered land. This is for two reasons: snow and ice are absorbing at 1.6 µm and the surrounding woodlands are more reflective.

Here’s a better comparison between the “visible” and the “snow and ice” bands:

Comparison of VIIRS I-1 and I-3 images (animation)

Comparison of VIIRS I-1 and I-3 images (animation)

You’ll have to click on the image to see it animate between the two.

Here’s an animation showing all five high-resolution bands on VIIRS for the two days:

Comparison of VIIRS high-resolution imagery channels (animation)

Comparison of VIIRS high-resolution imagery channels (animation)

Again, you have to click on it to see it animate.

Now, we can combine channels into RGB composites that highlight the snow and ice. We’ve discussed several RGB composites for snow detection before. And, we have been looking at the Natural Color RGB for a long time. This composite combines the high-resolution bands I-1 (0.64 µm), I-2 (0.86 µm) and I-3 (1.6 µm) as the blue, green and red components of the image, respectively. Here’s what it looks like for these two days:

Comparison of VIIRS Natural Color RGB composites

Comparison of VIIRS Natural Color RGB composites using high-resolution imagery bands

Lake Mille Lacs is visible on both days – first because it’s darker than the surroundings, then because it’s brighter. This composite demonstrates how vegetation can obscure the surface snow – it appears more brown in deciduous forests (and bare fields with no snow) and green in coniferous areas. But, the important point is that the wetter the snow and slush, the darker it appears. The fresher the snow, the brighter cyan color it has.

This is exaggerated in the “Snow RGB” that combines moderate resolution bands M-11 (2.25 µm), M-10 (1.6 µm) and M-7 (0.86 µm):

Comparison of VIIRS "Snow RGB" composites of channels M-11, M-10 and M-7

Comparison of VIIRS “Snow RGB” composites of channels M-11, M-10 and M-7

M-11 (2.25 µm) is sold as a “cloud particle size” band, but it also helps with snow and ice detection (and fires). The presence of water in melting snow enhances the darkening at 2.25 µm. In this RGB, that means melting snow appears more red, while fresh snow appears more pink. The slush on Lake Mille Lacs appears very dark – almost as dark as Lake Superior – so a Minnesotan might be forgiven if they see the image from 27 January and decide not to drive out on the lake to go ice fishing because they think the ice isn’t there.

Of course, VIIRS also gives us the True Color RGB – the most intuitive RGB composite – that combines the blue-, green- and red-wavelength visible bands: M-3 (0.48 µm), M-4 (0.55 µm) and M-5 (0.67 µm). If you’re curious, here’s what that looks like:

Comparison of VIIRS True Color RGB composite images

Comparison of VIIRS True Color RGB composite images

The slush on Lake Mille Lacs looks just like dirty slush and the fresh snow looks just like snow. (As it should!)

So, the second biggest lake in Minnesota never disappeared – it just changed its surface properties. And, it will always be “visible” to VIIRS in one channel or another – unless it’s cloudy (or it completely dries up).

December Fluff

By now, you probably know the drill: a little bit of discussion about a particular subject, throw in a few pop culture references, maybe a video or two, then get to the good stuff – high quality VIIRS imagery. Then, maybe add some follow-up discussion to emphasize how VIIRS can be used to detect, monitor, or improve our understanding of the subject in question. Not today.

You see, VIIRS is constantly taking high quality images of the Earth (except during orbital maneuvers or rare glitches). There isn’t enough time in a day to show them all, or go into a detailed discussion as to their relevance. And, nobody likes to read that much anyway. So, as we busily prepare for the upcoming holidays, we’re going to skip the in-depth discussion and get right to the good stuff.

Here then is a sample of interesting images taken by VIIRS over the years that weren’t featured on their own dedicated blog posts. Keep in mind that they represent the variety of topics that VIIRS can shed some light on. Many of these images represent topics that have already been discussed in great detail in previous posts on this blog. Others haven’t. It is important to keep in mind… See, I’m starting to write too much, which I said I wasn’t going to do. I’ll shut up now.

Without further ado, here’s a VIIRS Natural Color image showing a lake-effect snow event that produced a significant amount of the fluffy, white stuff back in November 2014:

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (18:20 UTC 18 November 2014)

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (18:20 UTC 18 November 2014)

As always, click on the image to bring up the full resolution version. Did you notice all the cloud streets? How about the fact that the most vigorous cloud streets have a cyan color, indicating that they are topped with ice crystals? The whitish clouds are topped with liquid water and… Oops. I’m starting to discuss things in too much detail, which I wasn’t going to do today. Let’s move on.

Here’s another Natural Color RGB image using the high-resolution imagery bands showing a variety of cloud streets and wave clouds over the North Island of New Zealand:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (02:55 UTC 3 September 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (02:55 UTC 3 September 2016)

Here’s a Natural Color RGB image showing a total solar eclipse over Scandinavia in 2015:

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (10:06 UTC 20 March 2015)

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (10:06 UTC 20 March 2015)

Here’s a VIIRS True Color image and split-window difference (M-15 – M-16) image showing volcanic ash from the eruption of the volcano Sangeang Api in Indonesia in May 2014:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (06:20 UTC 31 May 2014)

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (06:20 UTC 31 May 2014)

VIIRS split-window difference (M-15 - M-16) image (06:20 UTC 31 May 2014)

VIIRS split-window difference (M-15 – M-16) image (06:20 UTC 31 May 2014)

Here’s a VIIRS True Color image showing algae and blowing dust over the northern end of the Caspian Sea (plus an almost-bone-dry Aral Sea):

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (09:00 UTC 18 May 2014)

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (09:00 UTC 18 May 2014)

Here is a high-resolution infrared (I-5) image showing a very strong temperature gradient in the Pacific Ocean, off the coast of Hokkaido (Japan):

VIIRS I-5 (11.45 um) image (03:45 UTC 12 December 2016)

VIIRS I-5 (11.45 um) image (03:45 UTC 12 December 2016)

The green-to-red transition just southeast of Hokkaido represents a sea surface temperature change of about 10 K (18 °F) over a distance of 3-5 pixels (1-2 km). This is in a location that the high-resolution Natural Color RGB shows to be ice- and cloud-free:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (03:45 UTC 12 December 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (03:45 UTC 12 December 2016)

Here’s a high-resolution infrared (I-5) image showing hurricanes Madeline and Lester headed toward Hawaii from earlier this year:

VIIRS I-5 (11.45 um) image (22:55 UTC 30 August 2016)

VIIRS I-5 (11.45 um) image (22:55 UTC 30 August 2016)

Here are the Fire Temperature RGB (daytime) and Day/Night Band (nighttime) images of a massive collection of wildfires over central Siberia in September 2016:

VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12 (05:20 UTC 18 September 2016)

VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12 (05:20 UTC 18 September 2016)

VIIRS Day/Night Band image (19:11 UTC 18 September 2016)

VIIRS Day/Night Band image (19:11 UTC 18 September 2016)

Here is a 5-orbit composite of VIIRS Day/Night Band images showing the aurora borealis over Canada (August 2016):

Day/Night Band image composite of 5 consecutive VIIRS orbits (30 August 2016)

Day/Night Band image composite of 5 consecutive VIIRS orbits (30 August 2016)

Here is a view of central Europe at night from the Day/Night Band:

VIIRS Day/Night Band image (01:20 UTC 21 September 2016)

VIIRS Day/Night Band image (01:20 UTC 21 September 2016)

And, finally, for no reason at all, here’s is a picture of Spain wearing a Santa hat (or sleeping cap) made out of clouds:

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (13:05 UTC 18 March 2014)

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (13:05 UTC 18 March 2014)

There you have it. A baker’s ten examples showing a small sample of what VIIRS can do. No doubt it will be taking more interesting images over the next two weeks, since it doesn’t stop working over the holidays – even if you and I do.

Single-Purpose Flour

Think of a snowflake. What happens when that snowflake hits the ground? Now, picture other snowflakes – millions of them – all hitting the ground and piling up on top of each other, crushing our first poor snowflake. Skiers love to talk (and dream) about “fresh powder.” But, what happens when the “powder” isn’t so fresh?

Those delicate, little snow crystals we imagine (or look at directly, if we click on links included in the text) undergo a transformation as soon as they hit the ground. Compression from the weight of the snow above, plus the occasional partial thaw and re-freeze cycle (when temperatures are in the right range), breaks up the snow flakes and converts the 6-pointed crystals into more circular grains of snow. As more and more snow accumulates on top, the air in between the individual snowflakes/grains (which is what helps make it a good insulator) gets squeezed out, making the snow more dense. If enough time passes and enough snow accumulates, individual snow grains can fuse together. These bonded snow grains are called “névé.” If this extra-dense snow can survive a whole summer without melting, then a second winter of this compaction and compression will squeeze out more air and fuse more snow grains, creating the more dense “firn.” After 20 or 30 years of this, what once was a collection of fragile snowflakes becomes a nearly solid mass of ice that we call a “glacier.” Glaciers can be made up of grains that are several inches in length.

But, you don’t need to hear me say it (or read me write it), you can watch a short video where a guy in a thick Scottish accent explains it. (Did you notice his first sentence was a lie? Snow is made of frozen water, so glaciers are made of frozen water, since they are made of snow. I think what he means is that glaciers aren’t formed the same way as a hockey rink, but the way he said it is technically incorrect.) At the end of the video, the narrator hints at why we are looking at glaciers today: glaciers have the power to grind down solid rock.

When a glacier forms on a non-level surface, gravity acts on it, pulling it down the slope. This mass of ice and friction from the motion acts like sandpaper on the underlying rock, converting the rock into a fine powder known as “glacial flour” or, simply, “rock flour.” In the spring and summer months, the meltwater from the glacier collects this glacial flour and transports it downstream, where it may be deposited on the river’s banks. During dry periods, it doesn’t take much wind to loft these fine particles of rock into the air, creating a unique type of dust storm that is not uncommon in Alaska. One that can be seen by satellites.

And, wouldn’t you know it, a significant event occurred at the end of October. Take a look at this VIIRS True Color image from 23 October 2016:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (21:24 UTC 23 October 2016)

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (21:24 UTC 23 October 2016)

See the big plume of dust over the Gulf of Alaska? Here’s a zoomed in version:

Zoomed in version of above image.

Zoomed in version of above image.

That plume of dust is coming from the Copper River delta. The Copper River is fed by a number of glaciers in Wrangell-St. Elias National Park, plus a few in the Chugach Mountains so it is full of glacial sediment and rock flour (as evidenced by this photo). And, it’s amazingly full of salmon. (How do they see where they’re going when they head back to spawn? And, that water can’t be easy for them to breathe.)

Notice also that we have the perfect set-up for a glacial flour dust event on the Copper River. You can see a low-pressure circulation over the Gulf of Alaska in the above picture, plus we have a cold, Arctic high over the Interior shown in this analysis from the Weather Prediction Center. For those of you familiar with Alaska, note that temperatures were some 30 °F warmer during the last week in October in Cordova (on the coast) than they were in Glennallen (along the river ~150 miles inland). That cold, dense, high-pressure air over the interior of Alaska is going to seek out the warmer, less dense, low-pressure air over the ocean – on the other side of the mountains – and the easiest route to take is the Copper River valley. The air being funneled into that single valley creates high winds, which loft the glacial flour from the river banks into the atmosphere.

Now, depending on your preferences, you might think that the dust shows up better in the Natural Color RGB composite:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (21:24 UTC 23 October 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (21:24 UTC 23 October 2016).

But, everyone should agree that the dust is even easier to see the following day:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (21:01 UTC 24 October 2016)

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (21:01 UTC 24 October 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (21:01 UTC 24 October 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (21:01 UTC 24 October 2016)

You can also see a few more plumes start to show up to the southeast, closer to Yakutat.

Since Alaska is far enough north, we get more than one daytime overpass every day. Here’s the same scene on the very next orbit, about a 100 minutes later:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (22:42 UTC 24 October 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (22:42 UTC 24 October 2016)

Notice that the dust plume appears darker. What is going on? This is a consequence of the fact that glacial flour, like many aerosol particles, has a tendency to preferentially scatter sunlight in the “forward” direction. At the time of the first orbit (21:01 UTC), both the sun and the dust plume are on the left side of the satellite. The sunlight scatters off the dust in the same (2-dimensional) direction it was traveling and hits the VIIRS detectors. In the second orbit (22:42 UTC), the dust plume is now to the right of the satellite, but the sun is to the left. In this case, forward scattering takes the sunlight off to the east, away from the VIIRS detectors. With less backward scattering, the plume appears darker. This has a bigger impact on the Natural Color imagery, because the Natural Color RGB uses longer wavelength channels where forward scattering is more prevalent. Here’s the True Color image from the second orbit:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (22:42 UTC 24 October 2016)

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (22:42 UTC 24 October 2016)

The plume is a little darker than the first orbit, but not by as much as in the Natural Color imagery. Here are animations to show that:

Animation of VIIRS True Color images (24 October 2016)

Animation of VIIRS True Color images (24 October 2016)

Animation of VIIRS Natural Color images (24 October 2016)

Animation of VIIRS Natural Color images (24 October 2016)

There are many other interesting details you can see in these animations. For one, you can see turbid waters along the coast in the True Color images that move with the tides and currents. These features are absent in the Natural Color because the ocean is not as reflective at these longer wavelengths. You can also see the shadows cast by the mountains that move with the sun. Some of the mountains seem to change their appearance because VIIRS is viewing them from a different side.

The dust plumes were even more impressive on 25 October 2016, making this a three-day event. The same discussion applies:

VIIRS True Color composite of channels M-3, M-4 and M-5 (20:43 UTC 25 October 2016)

VIIRS True Color composite of channels M-3, M-4 and M-5 (20:43 UTC 25 October 2016)

VIIRS True Color composite of channels M-3, M-4 and M-5 (22:26 UTC 25 October 2016)

VIIRS True Color composite of channels M-3, M-4 and M-5 (22:26 UTC 25 October 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (20:43 UTC 25 October 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (20:43 UTC 25 October 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (22:26 UTC 25 October 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (22:26 UTC 25 October 2016)

Full disclosure, yours truly drove through a glacial flour dust storm along the Delta River on the north side of the Alaska Range back in 2015. Even though it was only about a mile wide, visibility was reduced to only a few hundred yards beyond the hood of my car. It felt as dangerous as driving through any fog. The dust event shown here was not a hazard to drivers, since it was out over the ocean, but it was a hazard to fisherman. Being in a boat near one of these river deltas means dealing with high winds and high waves. To forecasters, these dust plumes provide information about the wind on clear days (when cloud-track wind algorithms are no help), which is useful in a state with very few surface observing sites to take advantage of.

The last remaining issue for the day is one of terminology. You see, “glacial flour dust storm” is a mouthful, and acronyms aren’t always the best solution. (GFDS, anyone?) “Haboob” covers desert dust. “SAL” or “bruma seca” covers Saharan dust specifically. So, what should we call these dust events? Something along the lines of “rock flour”, only more proactive! And, Dusty McDustface is right out!

Watch for Falling Rock

Q: When a tree falls in the forest and nobody is around to hear it, does it make a sound?

A: Yes.

That’s an easy question to answer. It’s not a 3000-year-old philosophical conundrum with no answer. Sound is simply a pressure wave moving through some medium (e.g. air, or the ground). A tree falling in the forest will create a pressure wave whether or not there is someone there to listen to it. It pushes against the air, for one. And it smacks into the ground (or other trees), for two. These will happen no matter who is around. As long as that tree doesn’t fall over in the vacuum of space (where there is nothing to transmit the sound waves and nothing to crash into), that tree will make “a sound”. (There are also sounds that humans cannot hear. Think of a dog whistle. Does that sound not exist because a human can’t hear it?)

What if it’s not a tree? What if it’s 120 million metric tons of rock falling onto a glacier? Does that make a sound? To quote a former governor, “You betcha!” It even causes a 2.9 magnitude earthquake!

That’s right! On 28 June 2016, a massive landslide occurred in southeast Alaska. It was picked up on seismometers all over Alaska. And, a pilot who regularly flies over Glacier Bay National Park saw the aftermath:

If you didn’t read the articles from the previous links, here’s one with more (and updated) information. And, according to this last article, rocks were still falling and still making sounds (“like fast flowing streams but ‘crunchier'”) four days later. That pile of fallen rocks is roughly 6.5 miles long and 1 mile wide. And, some of the rock was pushed at least 300 ft (~100 m) uphill on some of the neighboring mountain slopes.

Of course, who needs pilots with video cameras? All we need is a satellite instrument known as VIIRS to see it. (That, and a couple of cloud-free days.) First, lets take a look at an ultra-high-resolution Landsat image (that I stole from the National Park Service website and annotated):

Glacier Bay National Park as viewed by Landsat (courtesy US National Park Service)

Glacier Bay National Park as viewed by Landsat (courtesy US National Park Service)

Of course, you’ll want to click on that image to see it at full resolution. The names I’ve added to the image are the names of the major (and a few minor) glaciers in the park. The one to take note of is Lamplugh. Study it’s location, then see if you can find it in this VIIRS True Color image from 9 June 2016:

VIIRS True Color RGB composite image of channels M-3, M-4 and M-5 (20:31 UTC 9 June 2016), zoomed in at 200%.

VIIRS True Color RGB composite image of channels M-3, M-4 and M-5 (20:31 UTC 9 June 2016), zoomed in at 200%.

Anything? No? Well, how about in this image from 7 July 2016:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (21:42 UTC 7 July 2016)

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (21:42 UTC 7 July 2016), zoomed in at 200%

I see it! If you don’t, try this “Before/After” image overlay, by dragging your mouse from side to side:

afterbefore

That dark gray area in the image from 7 July 2016 that the arrow is pointing to is the Lamplugh Glacier landslide! If the “Before/After” overlay doesn’t work, try refreshing the page, or look at this animated GIF:

Animation of VIIRS True Color images highlighting the Lamplugh Glacier landslide

Animation of VIIRS True Color images highlighting the Lamplugh Glacier landslide

Of course, with True Color images, it can be hard to tell what is cloud and what is snow (or glacier) and with VIIRS you’re limited to 750 m resolution. We can take care of those issues with the high-resolution (375 m) Natural Color images:

Animation of VIIRS Natural Color images of the Lamplugh Glacier landslide

Animation of VIIRS Natural Color images of the Lamplugh Glacier landslide

Make sure you click on it to see the full resolution. If you want to really zoom in, here is the high-resolution visible channel (I-1) imagery of the event:

Animation of VIIRS high-resolution visible images of the Lamplugh Glacier landslide

Animation of VIIRS high-resolution visible images of the Lamplugh Glacier landslide

You don’t even need an arrow to point it out. Plus, if you look closely, I think you can even see some of the dust coming from the slide.

That’s what 120 million metric tons of rock falling off the side of a mountain looks like, according to VIIRS!

The Great Flood of 2015

As we begin 2016, struggling to get back into the swing of things at work and vowing not to overeat or over-drink ever again, it’s appropriate to bid farewell to 2015 – not just for all the weird weather events that we covered on this blog over the year, but also for the weird, wacky weather that ruined many people’s holidays. I’m not sure of the exact number, but this article mentions 43 weather-related fatalities in the U.S. in the second half of December. Let’s see, between 23-30 December 2015, there were:

–    77 tornadoes (including 38 on the 23rd and 18 on the 27th);

–    Parts of New Mexico and west Texas got over 2 ft (60 cm) of snow from a blizzard that created drifts upwards of 10 ft (3 m) on the 27th;

–    Record warmth was observed in the Northeast before and during Christmas and the site of Snowvember went until 18 December before the first measurable snow of the season;

–    Chicago received almost 2″ of sleet (48 mm) on the 29th when any accumulation of sleet is quite rare;

–    And – what will be our focus here – St. Louis received over 3-months-worth of precipitation in three days (26-28 December), from a storm that flooded a large area of Missouri, Illinois and Arkansas. In fact, the St. Louis area had the wettest December on record, right after having the 7th wettest November on record, which put it over the top for wettest calendar year on record. Current estimates place 31 fatalities at the hands of this flooding, which caused the Mississippi River to reach its highest crest since the Great Flood of 1993.

What kind of satellite imager would VIIRS be if it couldn’t detect massive flooding on the largest river in North America? (Hint: not a very useful one. Or, a less useful one, if you’re not into hyperbole.) Hey, if it works in Paraguay, it works here – or it isn’t science!

I shouldn’t have to prove that the Natural Color RGB is useful for detecting flooding (since I have done it many, many, many, many, many, many times before), so we can go right to the imagery. Here’s what the Midwest looked like on 13 November 2015 – before the flooding began:

VIIRS Natural Color RGB composite of channels I-1, I-2, and I-3 (19:02 UTC 13 November 2015)

VIIRS Natural Color RGB composite of channels I-1, I-2, and I-3 (19:02 UTC 13 November 2015).

And, here’s what the same area looked like on New Year’s Day:

VIIRS Natural Color RGB composite of channels I-1, I-2, and I-3 (18:45 UTC 1 January 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2, and I-3 (18:45 UTC 1 January 2016).

Notice anything different? This is actually the reverse of the last time we played “Spot the Differences” – we’re looking for where water is now that wasn’t there before, instead of searching for bare ground that used to have water on it.

Of course, the first thing to notice is the large area of snow covering Iowa, Nebraska and northwest Missouri that wasn’t there back in November. Next, we have more clouds over the southern and northern parts of the scene. Those are the easy differences to spot. Now look for the Missouri River in eastern Missouri, the Arkansas River in Arkansas, the Illinois River in Illinois, the Indiana River in Indiana… Wait! There is no Indiana River. I fooled you! (Although, there are rivers in Indiana that are flooded.)

The most significant areas of flooding are in northeast Arkansas and the “Bootheel” of Missouri (which I think looks more like a toe or a claw than a heel), and the Mississippi River along the border of Tennessee shows signs of significant flooding as well. (If only it were the Tennessee River!) Here’s a before and after comparison, zoomed in on that part of the region:

13 November 20151 January 2016

You may have to refresh the page to get this to work right.

There’s a lot more water in the image from 1 January 2016 than there was back in November 2015! Since we are looking at the high-resolution Imagery bands, our quick-and-dirty estimate of water volumes still applies like it did for California’s drought: multiply the number of water-filled pixels by the depth (in feet) of the flooding, and by 100 acres to get the floodwater volume in acre-feet. Then multiply that by 325,852 gallons per acre-foot to get the volume in gallons. Even though this estimate is not exact, you can see how the gallons of floodwater add up. And, if you live in California, you can dream of seeing that much water! If you live in Missouri and can think of an economical way to transport this water to California, you’d be rich.

Now, see how many other areas of flooding you can find when you compare the two images in animation form:

Animation of VIIRS Natural Color RGB images from 13 November 2015 and 1 January 2016

Click to view an animation of VIIRS Natural Color RGB images from 13 November 2015 and 1 January 2016.

You will have to click on the image to see the animation. You can click on the image again to see it in full resolution (with most web browsers).

One thing you might notice is that some of the floodwaters appear more blue than black. Take a look at the Arkansas River in particular. As we discussed with the Rio Paraná and Rio Paraguay, this is due to the increased sediment that increases the albedo of the water at visible wavelengths. In other places the floodwaters are shallow enough that VIIRS can see the ground underneath – again making the water appear more blue in this RGB composite.

Wouldn’t it be nice to identify areas of flooding without having to play a “Spot the Differences” game? Maybe something that would automatically detect flooded areas? Well, you’re in luck:

VIIRS-based Flood Map (18:48 UTC 1 January 2016)

VIIRS-based Flood Map (18:48 UTC 1 January 2016). Image courtesy S. Li (GMU).

This image is an example of the VIIRS-based flood detection product being developed by the JPSS Program’s River Ice and Flooding Initiative. This initiative is a collaboration between university-based researchers and NOAA forecasters who use products like these to help save lives. Thanks to S. Li for developing the product for and providing the image!

If you want to know what the flooding looks like from the ground, here is a nice video. Or, you can look at some pictures here.

As a final note, the American Meteorological Society is holding its Annual Meeting in New Orleans next week. This event will be held at the Convention Center – right on the bank of the Mississippi River – right at the time the river is forecast to crest from these floodwaters. The world’s largest gathering of weather enthusiasts might be directly impacted by this flood. Let’s hope no one has to swim their way to any poster sessions or keynote speeches! (I don’t think local residents want to deal with any flooding, either.)