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).

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!

Remote Islands V: St. Helena and Ascension

You may have missed it in the news, but history was made last week:

A plane landed! Wow!

But, that’s not any old plane – that’s the first commercial airliner to land on St. Helena Island, which just completed the construction of their very first airport. That means there may be no more commercial sailing to this tiny island.

People prone to seasickness may be cheering the news. People afraid of flying might not. Did you notice it took three attempts to land that plane in the video above? The first pass was getting everything all lined up with no intention of landing. The landing gear wasn’t even down. The second – which looked like a roller coaster – was waived off due to the heavy crosswinds. The third time was the charm. However, it was such a shaky first landing, they’ve postponed the official opening of the airport.

So, where is St. Helena (pronounced Ha-LEEN-a), anyway? And why should I care?

Well, to answer the first question, it’s somewhere in this image:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (12:45 UTC 26 April 2016)

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (12:45 UTC 26 April 2016).

Did you find it? To help you with your bearings, Africa is just outside this VIIRS swath on the right side of the image. Two hints: click on the image to bring up the full resolution version. St. Helena is just northwest of the center of the image. It’s the only island in the image not covered by clouds. Fun fact: every island within this VIIRS swath is part of the British Overseas Territory of St. Helena, Ascension and Tristan da Cunha. We already looked more closely at Tristan da Cunha, so let’s take a look at the other two.

We can get a higher resolution look if we use the I-band Natural Color RGB composite:

VIIRS Natural Color RGB composite of channels I-01, I-02 and I-03 (12:45 UTC 26 April 2016)

VIIRS Natural Color RGB composite of channels I-01, I-02 and I-03 (12:45 UTC 26 April 2016).

Notice the island appears green in the center, surrounded by a ring of brown – just the way it looks on a really high resolution satellite image. VIIRS has the resolution to pick this out!

As for why you should care, I don’t know if I can answer that. If your first thought is to ask that question, you probably don’t care. But, there are a few interesting things to note about St. Helena (besides its new airport):

– It was once an important stopping point for ships sailing from Europe to India in search of spices. At least, until the Suez Canal opened.

– It later became a prison, housing those who fought against the British government and lost, including Napoleon Bonaparte, Dinuzulu, King of the Zulu Nation, and POWs from the Boer War.

– Along with Ascension Island, St. Helena helped inspire the modern environmental movement. And it was here that the first large scale experiments in weather modification took place. (Not counting rain dances, of course.)

After witnessing the effect of deforestation on the island in the late-1700s and early-1800s, it was believed that re-foresting would help keep moisture on the island, which would lead to more clouds and more rainfall. Ascension Island, which was essentially a barren wasteland when first discovered, was also planted with trees, creating it’s Green Mountain, which is clearly visible on very high resolution satellites.

Speaking of Ascension Island – where is that located? In the first image above, showing most of the Southern Atlantic, Ascension is near the upper left corner. It’s hard to see because it is covered by clouds. Just follow the 8 °S latitude line in from the left edge of the image.

Here it is at high resolution during a clear day:

VIIRS Natural Color RGB composite of channels I-01, I-02, and I-03 (14:03 UTC 20 April 2016)

VIIRS Natural Color RGB composite of channels I-01, I-02, and I-03 (14:03 UTC 20 April 2016).

If you look closely, you’ll see that there is a small cloud or two right over Green Mountain, so maybe the efforts of the early environmentalists paid off!

For completeness, Tristan da Cunha is in the lower left of the True Color image I posted at the top. While it is covered by clouds, you can tell it’s there because it is creating its own waves. Here it is on the next orbit, where it is closer to satellite nadir:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (15:24 UTC 26 April 2016)

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (15:24 UTC 26 April 2016).

If I’ve inspired you to visit these islands, ask the government to give me a commission. But, seriously, don’t forget to say “Hi!” to Jonathan. Or see the many other plants and animals that are found nowhere else on Earth.

UPDATE (16 October 2017): Reuters has reported that the airport is now officially open to commercial flights (only a year and half after I wrote the original blog post)!

UHF/VHF

Take a second to think about what would happen if Florida was hit by four hurricanes in one month.

Would the news media get talking heads from both sides to argue whether or not global warming is real by yelling at each other until they have to cut to a commercial? Would Jim Cantore lose his mind and say “I don’t need to keep standing out here in this stuff- I quit!”? Would we all lose our minds? Would our economy collapse? (1: yes. 2: every man has his breaking point. 3: maybe not “all”. 4: everybody panic! AHHH!)

It doesn’t have to just be Florida. It could be four tropical cyclones making landfall anywhere in the CONUS (and, maybe, Hawaii) in a 1-month period. The impact would be massive. But, what about Alaska?

Of course, Alaska doesn’t get “tropical cyclones” – it’s too far from the tropics. But, Alaska does get monster storms that are just as strong that may be the remnants of tropical cyclones that undergo “extratropical transition“. Or, they may be mid-latitude cyclones or “Polar lows” that undergo rapid cyclogenesis. When they are as strong as a hurricane, forecasters call them “hurricance force” (HF) lows. And guess what? Alaska has been hit by four HF lows in a 1-month period (12 December 2015 – 6 January 2016).

With very-many HF lows, some of which were ultra-strong, we might call them VHF or UHF lows. (Although, we must be careful not to confuse them with the old VHF and UHF TV channels, or the Weird Al movie.) In that case, let’s just refer to them as HF, shall we?

The first of these HF storms was a doozy – tying the record for lowest pressure ever in the North Pacific along with the remnants of Typhoon Nuri. Peak winds with system reached 122 mph (106 kt; 196 k hr-1; 54 m s-1) in Adak, which is equivalent to a Category 2 hurricane!

Since Alaska is far enough north, polar orbiting satellites like Suomi-NPP provide more than 2 overpasses per day. Here’s an animation from the VIIRS Day/Night Band, one of the instruments on Suomi-NPP:

Animation of VIIRS Day/Night Band images of the Aleutian Islands (12-14 December 2015)

Animation of VIIRS Day/Night Band images of the Aleutian Islands (12-14 December 2015).

It’s almost like a geostationary satellite! (Not quite, as I’ll show later.) This is the view you get with just 4 images per day. (The further north you go, the more passes you get. The Interior of Alaska gets 6-8 passes, while the North Pole itself gets all 15.) Seeing the system wrap up into a symmetric circulation would be a thing of beauty, if it weren’t so destructive. Keep in mind that places like Adak are remote enough as it is. When a storm like this comes along, they are completely isolated from the rest of Alaska!

Here’s the same animation for the high-resolution longwave infrared (IR) band (I-5, 11.5 µm):

Animation of VIIRS I-5 images of the Aleutian Islands (12-14 December 2015)

Animation of VIIRS I-5 images of the Aleutian Islands (12-14 December 2015).

I’ve mentioned Himawari before on this blog. Well, Himawari’s field of view includes the Aleutian Islands. Would you like to see how this storm evolved with 10 minute temporal resolution? Of course you would.

Here is CIRA’s Himawari Geocolor product for this storm:

Here is a loop of the full disk RGB Airmass product applied to Himawari. Look for the storm moving northeast from Japan and then rapidly wrapping up near the edge of the Earth. This is an example of something you can’t do with VIIRS, because VIIRS does not have any detectors sensitive to the 6-7 µm water vapor absorption band, which is one of the components of the RGB Airmass product. The RGB Airmass and Geocolor products are very popular with forecasters, but they’re too complicated to go into here. You can read up on the RGB Airmass product here, or visit my collegue D. Bikos’ blog to find out more about this storm and these products.

You might be asking how we know what the central pressure was in this storm. After all, there aren’t many weather observation sites in this part of the world. The truth is that it was estimated (in the same way the remnants of Typhoon Nuri were estimated) using the methodology outlined in this paper. I’d recommend reading that paper, since it’s how places like the Ocean Prediction Center at the National Weather Service estimate mid-latitude storm intensity when there are no surface observations. I’ll be using their terminology for the rest of this discussion.

Less than 1 week after the first HF storm hit the Aleutians, a second one hit. Unfortunately, this storm underwent rapid intensification in the ~12 hour period where there were no VIIRS passes. Here’s what Storm #2 looked like in the longwave IR according to Himawari. And here’s what it looked like at full maturity according to VIIRS:

VIIRS DNB image (23:17 UTC 18 December 2015)

VIIRS DNB image (23:17 UTC 18 December 2015).

VIIRS I-5 image (23:17 UTC 18 December 2015)

VIIRS I-5 image (23:17 UTC 18 December 2015).

Notice that this storm is much more elongated than the first one. Winds with this one were only in the 60-80 mph range, making it a weak Category 1 HF low.

Storm #3 hit southwest Alaska just before New Year’s, right at the same time the Midwest was flooding. This one brought 90 mph winds, making it a strong Category 1 HF low. This one is bit difficult to identify in the Day/Night Band. I mean, how many different swirls can you see in this image?

VIIRS DNB image (13:00 UTC 30 December 2015)

VIIRS DNB image (13:00 UTC 30 December 2015).

(NOTE: This was the only storm of the 4 to happen when there was moonlight available to the DNB, which is why the clouds appear so bright. The rest of the storms were illuminated by the sun during the short days and by airglow during the long nights.) The one to focus on is the one of the three big swirls closest to the center of the image (just above and right of center). It shows up a little better in the IR:

VIIRS I-5 image (13:00 UTC 30 December 2015)

VIIRS I-5 image (13:00 UTC 30 December 2015).

The colder (brighter/colored) cloud tops are the clue that this is the strongest storm, since all three have similar brightness (reflectivity) in the Day/Night Band. If you look close, you’ll also notice that this storm was peaking in intensity (reaching mature stage) right as it was making landfall along the southwest coast of Alaska.

Storm #4 hit the Aleutians on 6-7 January 2016 (one week later), and was another symmetric/circular circulation. This storm brought winds of 94 mph (2 mph short of Category 2!) The Ocean Prediction Center made this animation of its development as seen by the Himawari RGB Airmass product. Or, if you prefer the Geocolor view, here’s Storm #4 reaching mature stage. But, this is a VIIRS blog. So, what did VIIRS see? The same storm at higher spatial resolution and lower temporal resolution:

Animation of VIIRS DNB images of the Aleutian Islands (6-7 January 2016)

Animation of VIIRS DNB images of the Aleutian Islands (6-7 January 2016).

Animation of VIIRS I-5 images of the Aleutian Islands (6-7 January 2016)

Animation of VIIRS I-5 images of the Aleutian Islands (6-7 January 2016).

This storm elongated as it filled in and then retrograded to the west over Siberia. There aren’t many hurricanes that do that after heading northeast!

So, there you have it: 4 HF lows hitting Alaska in less than 1 month, with no reports of fatalities (that I could find) and only some structural damage. Think that would happen in Florida?

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.)